20VC: Mercor CPO on Revenue Concentration from Frontier Labs | Why Large Enterprise is Scared to Partner with Frontier Labs | Why Small Specialised Models is the Future with Osvald Nitski

20:30Osvald Nitski:I think that it's a knowledge dissemination problem. So I think that that's one way to look at it. The other way is why not hire someone to just do this agent deployment at your own company? And I just don't think the skill is out there yet. I don't think there's enough. I don't think the talent is available for every enterprise to have their own expertise in it at this point in time. But that'll change over the long run. This is, I think, like, you know, maybe a decade long change. Do good engineers really want to be FDs, though? Good and there are a lot of different types of good engineers. There's a lot of things, a lot of ways to be a good engineer.

21:05Osvald Nitski:And one way to be a good engineer is being a great communicator and cutting through to the source of a problem and simplifying. And I think that those engineers are great fits for FDs. And I think that those engineers are also great fits to eventually become founders. And I think that that is a different profile of person who's incredibly valuable. And that's what a lot of people are looking for when they're looking for FDs. And it's also like a profile that we look for generally, which is why we have so many alumni go off and start companies. Do you like that? I spoke to Brandon about this, but is it a good thing to have the McCall Mafia?

21:43Because you also want to retain talent.

21:45Osvald Nitski:I'm proud that of the people I work closest with on my teams, I've only had attrition to founding. And we've had quite a bit of it. It's a lot better to lose someone to starting a company than to taking another job. It's interesting from a personal level, because I like these people. I wish the best for them. I really enjoy seeing it. It is tough, though. It makes the job of management a lot harder because we just have so many high agency people who are very ambitious, and it's difficult, but I like it. And I'd rather be in an environment like this than one where everyone's like, oh, you know, soft and, you know, oh, I don't want to work.

22:24Oh, no, I'm just... No wonder you left Europe. How has hiring changed in a post-AI new world When you look at the people that you add to your team today, especially in product, what do you ask today or look for today that you didn't before?

22:41Osvald Nitski:I think touching on the earlier point of everybody needing to up-level and think closer to business impact, we've biased towards more senior hires who are better at understanding what drives the business forward, finding kind of like really crocking how we operate, how we make more revenue, how we deliver better services to our customers, how we keep our customers happy. I found that more senior candidates just get that a lot faster. And like I said, all of these like kind of like tool, like can you use the tool? Can you do all these other kind of like more junior things are becoming less relevant?

23:18Osvald Nitski:So the hiring for us is biased towards more senior candidates. Do you know what? Do you worry that you're just falling for the kind of classic, I'm so sorry to be, like the fast growth founder mode, which is like your VCs come in and say, oh, you need to hire this person from Facebook. and you get the seasoned operator who fits exactly that rubric and it never works. It never works. We're not quite doing, seasoned here is a spectrum. I'm not saying we're hiring people who are in formerly an executive positions. We are treating everything as an executive search where we want to find someone who's at the sweet spot.

23:56Osvald Nitski:They're still hungry. They've done the job that we want them to do for a few years. And they're right in kind of like really hitting their prime. So that's... When do you think people hit their prime? I think 25 to 35. Oh, I just turned 30. I'm bang in the middle. Perfect. Good timing for you. Perfect timing for me. Okay. In terms of like the questions, what we look for in the take-home assignments, has that changed? We've moved away from take-home assignments. We do one take-home assignment, which is like, can you just like use an agent to go, you're on your own for a bit of time, go use an agent, you know, produce this artifact for me and we'll look at it.

24:37Osvald Nitski:Do that once, you know that the person's AI fluent. And then we move towards a lot of whiteboarding because we want to avoid, like we'll do one round where we know, where we find out if the person is familiar with AI tools. Okay, so cool. We do that. I'm familiar with AI tools. And now you're like, come into my room. We've got a whiteboard. What do you want? What are we going to do? What do you want to see? What would impress you? We care a lot about being able to set up good experiments and understanding statistics, having good judgment, and then systems design as well. The reason is these are just skills that are so easy to...

25:13Osvald Nitski:I'm so sorry to interrupt you. Good experiments and systems design, it feels quite wordy. What does that actually mean? We ask people... Well, I don't want to give away too much about our interview process, but there are... We need to run a lot of experiments. as a product team. We need to make sure that our team knows how to run a good experiment that actually reveals information and isn't just totally like fudged. And with AI tools, it's very easy to offload a lot of thinking and a lot of judgment. We want to make sure that people still have the ability to have good judgment and know what they're doing and not just like regurgitate what comes out of Cloud.

25:51That's so interesting. I completely agree with you. I have it with my team, which is like we do scripts for content, for reels, for... I do all questions myself. I would never use AI and I'm very concerned about it because you lose the muscle to me. Can I ask you, how do you retain thinking, thought, creativity when so many people are so freaking hooked already?

26:10Osvald Nitski:I think it's kind of, I tell my team, it's kind of like phones, you know, they kind of fry your brain and they turn it into goop. But I love, I do a lot of stuff on my phone. Like I use my phone all the time. You just have to learn personally where that boundary is of like, what's, when is a good time to scroll through reels and when's a bad time, you know? in a meeting, try not to scroll through it. For work, that boundary, I think, is between kind of like the judgment and decision-making and the execution, right? So I want very careful never to delegate judgment or decision-making to models because it makes you think that it's doing the right thing, but you have to be paranoid with them still, right?

26:48Osvald Nitski:You still have to like double-check everything. And that's what I tell my team is that like don't delegate your decision making like your actual job to a model because you're going to lose that ability and then you're going to get psychosis. When you look at the experiments that you've run, does the data correlate to the outcome? I often think in investing, sometimes I do no work and no diligence and I make loads of money. And sometimes I do lots and I make terrible investments that lose all the money. Do the inputs correlate to the outputs? It varies. It varies because we run a lot of experiments.

27:24Osvald Nitski:Sometimes they do, sometimes they don't. We want to get more that actually show good results and move the business forward. And that's really the job of the team is to find the right experiments to run and make the narrative around, you know, these changes to our product have impacted the business in a positive way. That's a lot of the core job right now. So, you know, it's week to week, month to month, we get different results, but we try to trend in the right direction over time and people start to learn the dynamics of the product, learn the dynamics of the users better and better to improve over time.

27:57When you think about product and engine running experiments that you mentioned there, running good experiments, how do you structure the teams today and what does that meeting look like?

28:08Osvald Nitski:We have a few different groups that do experimentation. So we have two major product areas where this is most relevant. our marketplace, which matches experts to jobs, and our annotation and eval platform, which is where experts log in to do annotation for eval or training data sets, where our operations team also logs in to run those projects, and our customers will log in to see their data and run evals. So, annotation, platform, we call it studio, marketplace, call the marketplace. These two groups, they're kind of like self-contained in trying to do, to make their individual product offering better.

28:47Osvald Nitski:And we have two modes, main modes of engagement within human data. Talent only, which is when we just send experts to our customers and they'll run the project. So this is like a lab needs a doctor or a lawyer or whatever and they're like, we're just going to use them and like, thanks for finding the best person for the job. You'll need to pay them, performance manage them, but like we'll run the project. And then a managed service project where we give our customers data. So for the talent only model, we just use the marketplace. For the managed service, we use the marketplace to send people to our annotation platform, and then we'll run the project and give them the whole data set.

29:21Totally. Are they two separate product teams? They're two separate product teams.

29:24Osvald Nitski:How big are the product teams? Around two to three per product area with also data scientists and a design team. Data scientists dedicated to each and a design team that flexes between them of just a few. So you have like pods of like four or five? That's fair. Yeah. Will those ratios change over time, do you think, between PMs and design, or will that stay the same? I think that the ratio of PM to ENG will change over time to have fewer engineers per PM as engineering velocity increases with better coding agents. And we will be bottlenecked by understanding business needs, user needs. That's more of a PM job.

30:05Osvald Nitski:We need to be very careful as a hyper growth company to grow the teams in lockstep because as the headcounts increased, you know, like more than 10x in the last year, we just want to be careful not to grow one faster than the other. The trend will be higher PM to end ratio though. So when we talk about the good experiments and making sure that we're running a really tight process, what does that look like in terms of the meetings? You have a weekly product team meeting. What is the right way to approach cadence of product team meetings and how to run them today? We break it down into, so within these product areas, we'll have like a whole PA like weekly in this product and ENG and a lot of other stakeholders as well.

30:47Osvald Nitski:And this one is just like everyone, you know, kind of like, it's broken down into pods. So as I mentioned, within that product area, there might be like, let's say three product managers. They'll all have like a pod of these parts of the product that we can naturally kind of like segment work into. Our marketplace, for example, has a expert facing side and a hiring manager facing side. These are naturally two distinct pods. There are some other pods within here as well, like managing the expert experience, making sure that everyone has great customer support. There's never any issues with working from a core.

31:22Osvald Nitski:Each of these pods will do their own sprint planning. They'll come together in the weekly kind of like product area meeting. And we try to keep it efficient, but maintain like a lot of visibility between the pods because they all need to have their roadmaps well aligned. Yeah, but we need to have weekly meetings to maintain accountability. Do them on Friday a bit later in the day, make sure no one's leaving early on the weekend. What do you not do in your product meetings that you should do to make them better? That's a good question. It varies by product area. So the challenges in like the marketplace versus like the annotation platform are a bit different.

31:54Osvald Nitski:The main challenge as we grow quickly is having the right amount of communication and feedback from other teams. So like our marketplace and our studio team need to get information from each other, right? There's cases where like something's wrong in one and it's popping up in the other. Something's wrong with one product and it's affecting like the expert experience when they're like on the other one somehow. that communication just like the because the headcount and the teams grown so quickly like the communication channels like just explode very quickly so we need to do more kind of like cross product area collaboration keeping it efficient it's just really hard as the team grows because we're you know the nodes just keep moving around and there's more of them what has been the secret to scaling supply on the marketplace side so efficiently how have you guys done that so well I'd probably put it down to three things.

32:51Osvald Nitski:The first one is a great expert experience. Experts get paid on time. They get paid well, transparently. Everybody involved in what the expert experiences cares deeply about whether or not they're having any challenges and whether or not the work is dignified and well-paid and fairly paid. And that is a requirement for a great referrals program because nobody's going to refer their friends to, you know, their colleagues to some kind of job that's like, that sucks. So everybody caring about expert experience drives a great referral program. Additionally, a great sourcing team that's able to find people in every corner of the world with very specific skills helps us fill the gaps when, you know, we have spiky demand for a specific skill set.

33:39Are people as short-sighted as just being wanting to be paid the most? I've heard that McCall pays the most?

33:45Osvald Nitski:I wouldn't say it's like, you know, short sightedness because we want to retain the top experts as well. If you get paid a lot on like one project and it's like, I know there are a lot of other competitors in the space who will do some crazy like bonus payouts and stuff for short term sprints. That doesn't get you to come back as much as a great experience with a lot of work, visibility into like what future work is coming up, the feeling of like, I'm growing my skill set. I have the ability to pick between a few different jobs. I'm doing interesting work. I have great communications from the people running the project.

34:21Osvald Nitski:It's really hard to sign up for online work and then you just get hit with this 100-page instruction document. It's a very foreign kind of job. That's part of the experience as well. Knowing that you're going to get paid highly for a long time for something that you can do for a long time is what keeps people interested. Have you seen your margin improve over time or is it one where actually margin's relatively fixed given the complexity? So margins are an interesting thing in this business. We try to think about as a product team, how do we deliver the best value for our customers? That is independent of how do we price the project.

34:58Osvald Nitski:So there are cases where you could have automatic quality control and synthetic data improvements to make the delivery better. situations where you could think about the staffing on the project to change the cost of the service. All of that, as a product team, we want to make sure that we could deliver the best value to our customers and we can have the best experience for our experts. Margins are decided after the fact based on consideration of costs. And now for a lot of these projects, the costs are driven from paying experts and LLM spend on things like synthetic data and automatic quality control.

35:32Does the it's not revenue, it's not revenue shouting from the crowds throwing peanuts, does that annoy you? And is there anything there that hasn't been said that you think people are just like not getting?

35:43Osvald Nitski:It doesn't annoy me, no, because, you know, we end every week with millions more in the bank, right? So it's funny how you can have, I've been at other companies where I've seen, you know, interesting financial engineering and accounting and people People can have all these different metrics, but we end every week with so much more money in the bank. The business is very healthy and we can't spend money fast enough. So what people want to call it is up to them, but the cash flow is insane. Does it matter that you have such high revenue concentration? The Frontier Model providers, your biggest customers by far, some would say, that's a lot of concentration.

36:23How do you think about that?

36:25Osvald Nitski:I can answer this from kind of like a how it affects the product team. Yeah. We would love to move, like our biggest challenge is moving down market so that every single enterprise can efficiently run human data projects for eval and training. And that'll diversify our revenue for sure because there's many more enterprises than there are labs. And that's a harder product to build. And that's the direction that we are taking our products, taking the company is to be able to self-serve, run these projects very efficiently, have like AI project managers so that it's a lot easier to do this work for smaller customers because running a human data project for a lab is incredibly hard.

37:03Osvald Nitski:It's a white glove service that requires a lot of people on the operations team. As we make that more efficient with better products, better processes, we can do smaller projects that are more heterogeneous for more customers. It's the direction we have been heading, which has reduced concentration, and it's the direction that we'll continue to head as every enterprise begins to have human data work for their proprietary use cases. What's so hard about it? Making it really simple? Explaining it? What is the challenge with democratizing? Running a human data project is just hard. There is so much information that needs to be transmitted from the customers, the end users of our customers, to experts.

37:47Osvald Nitski:And all All the edge cases matter, right? So people will try to write a guideline that says, like, here's how you make a data point. But the experts will have some edge case that gets bubbled up, and what you do on that edge case matters a lot. So the process of making a human data project is basically continually surfacing these edge cases, which requires insanely fast alignment between customers, maybe their customers, maybe other experts in the field, and the experts who are doing the annotation. And it also requires a huge amount of paranoia from the operations team to make sure that Every data point is perfect.

38:18Osvald Nitski:It fits whatever guidelines the customers have, and the project's running on time. All the bottlenecks are removed. It's just an operationally intense process because it necessarily deals with edge cases and things that haven't seen before and are outside of model capabilities. The data types also change very frequently. So we've moved from supervised fine-tuning to preference ranking to rubric-based annotation to now RL environments across a whole of different modalities. There's a lot of complexity within each project and then between projects. So I would boil it down to those two things of like the need for paranoia and the need for very crisp communication that make it challenging.

38:57What data type is not hugely in demand today that you think will be hugely in demand next year?

39:04Osvald Nitski:The data type that's growing the fastest for us is environments. You might have seen a lot about these RL environments on On Twitter, it's kind of like a hype term. Every company kind of has a different definition for it. But we are certainly the leader in the category and view it as basically these simulations of apps that you might want your agent to use. And also a rich start state, which we call the world, that is basically representative of all the data you might have on your machine, like your laptop. And then we have tasks that train agents, how to use those tools to accomplish something that's useful.

39:40Osvald Nitski:It's a bit of a complicated annotation process because the agent has to interact with this simulated world. We have to make that start state, which can be hundreds of files, thousands of files. And the shift here is that the data that the models are now, the agents are being evaled and trained on, looks a lot closer to what they see in deployment. So if you want to learn how to use something like Salesforce, you need a pretty high fidelity mock that acts exactly like Salesforce in your eval and training. and it's complicated to get this set up. Just like years ago, preference ranking was really hard to get set up.

40:14Osvald Nitski:SFT was really hard to get set up when InstructGPT first came out. So this is the frontier right now. Labs are figuring out, NeoLabs are figuring it out. Eventually it'll get so smooth that enterprises can do it too. Are labs price sensitive on data acquisition? By data acquisition. When they go on a project with you, are they price sensitive? Are they haggling going, oh, well, Edwin at Surge gave me a 10 % discount. Can I have that? Or are they like, just give me the fucking data? Well, there's always the aspect of negotiation and the procurement team trying to get a better deal. But we've chosen a great business where our work directly affects the business outcomes of our customers, right?

40:58Osvald Nitski:So we have a great setup where if you're making an eval set, like in your lab, you're evaluating something that your customers want to do. If you could just do it better, you would make more revenue. If they're buying a training set, they're now hill climbing that eval set that they've said, you know, that represents what their customers want to do. So as long as like the amount of money they're spending on data is less than the revenue that they're going to get, they're happy to crank the lever. Like people want to crank it harder and harder because spend on Mercore directly translates to more revenue for our customers.

41:28Do you think we'll have an unbundled data provider world? You know, I'm a venture investor. I see so many people that are like, oh, we're like, we're like McCaw, but for like domestic robotics. And you're like, okay, cool. Good. Okay, I get it. But do you think we will see this kind of specialized data provider world where niches have thousands of players?

41:52Osvald Nitski:To an extent, we're already in this world. It's not that successful, though, for the small players always. So how I would describe it is we're facing what looks like a cottage industry of founders doing annotation themselves, right? So you have all of these small startups where as the skill bar for annotation gets higher and higher as models get better, you have startups where the founders are actually just making the data. And labs love this because it's just like totally mispriced, you know? They get like someone raises a bunch of money, they have loads of cash to blow, and they They go to these labs and they're like, I need to get your business.

42:27Osvald Nitski:Please let me work for you. And then they're smart people. They're founders. They're formerly great technical employees. But they're running the projects themselves. They're doing the annotation themselves. This is just VC subsidized work that labs love. The problem is scaling it beyond a few data points or what one founder or full-time employees can do. And this is the position that we're in is we're having to compete against basically founder-led annotation, where some of them are even running it as cash flow businesses and they're just like taking the profits home themselves. It doesn't scale though.

42:58Osvald Nitski:And vendors, our customers know this, that it won't scale when you want to 10x the throughput, 10x the amount of projects. But it is indicative of the direction the field's heading in, in that we need higher skilled experts. We need the best people in the world to be doing the sanitation. Don't laugh. I have a bit of an ego. And so I like to feel like a special snowflake. And what I mean by that is I would be like, oh, when Meta or OpenAI or you name your large company is buying data from multiple people, it feels like you're being promiscuous and cheating on me. Do you mind and do you monitor budget and percent of budget that gets spent with you versus another provider?

43:38Osvald Nitski:Of course, we do a lot of competitive intelligence. Our customers like us. So, you know, they'll often share information with us, but everybody just wants models to get better, right? So we're happy to have this kind of competitive pressure that tells us like where to go. If someone else is able to do something better than us, we'd love to hear about it and then do it better than them, right? It's healthy to have vendor bake-offs. It pushes us to make our services better. We do stay on top of it because we want to deliver better services to our customers. We want to know who's doing better than us and then we want to surpass them.

44:11Osvald Nitski:So it's a totally healthy thing to happen as long as Mercor is winning. We said frontier earlier. Yeah. I'm an investor in Lagora and everyone's like, no, your real competition is actually Anthropic. And I'm like, if Anthropic go after legal and winning Cooley and Goodwin, something's gone very wrong with the world because they should be solving cancer and climate change. To what extent am I right? And how do I balance between Anthropic coming for Lagora and Figma and Anthropik's also working on the frontier problems that humanity faces today? That's a good question. I would look to precedents from other big tech companies who've had a lot of different efforts like Google, Microsoft, coincidentally also try to solve climate change and cancer, but it's not their main business.

45:02Osvald Nitski:And they have their hands in a lot of different areas, but competitors still emerge. So you've seen like, you remember Google +, right? That didn't go anywhere, right? It probably, maybe it freaked some people out when it happened. You probably remember Threads. I don't know the current state of Threads. Apparently 400 million users, according to their marketing team. That's very interesting.

45:28I won't comment too much on that because I... How fascinating.

45:30Osvald Nitski:I'd love to see the engagement. I genuinely don't know anything about this. But yeah, so it's in, I think if you look to precedents here, Large companies often try to, you know, make new bets, diversify, but they lose to companies that have intense focus on their market. So we'll see how it plays out. But I would wonder if there's anything to learn from, you know, history with Google and Microsoft having many business units, many efforts, but a core business that has driven all of their revenue. You know, I love Brendan. I remember texting him when there was the hack. It's tough when there's a hack because you're like, I don't know what to say, but like, I'm here for you.

46:09you know, thumbs up. And I felt like such a VC because you're like, I'm here for you. Good luck. Fuck all help that is. My question to you, how did that change your mindset and approach to product? It's a really hard thing to go through. I remember you were under intense pressure and stress. And I seriously am sorry for that because it's horrible to go through. How did it change your product mindset?

46:30Osvald Nitski:You know, I'm not an expert in security, but we hired a lot of experts in security and I listen to them. And that's the main change is just larger investment and learning from the experts that we've brought in-house. Are we entering a golden age for cyber? And what I mean by that is we're seeing a huge amount of AI generated code, which in a lot of cases has holes, but we're seeing a lovable and a replet and a you name it produce a huge amount of output. Are the threats going to increase much more significantly than we're anticipating? Most likely, yes. Where we see it the most is it's an interesting data type because it's competitive and you can have these AlphaGo type situations for cyber offense and defense where you can have uncapped rewards and performance and the field's constantly moving.

47:18Osvald Nitski:So we love this kind of stuff because it's like a game from a data perspective. And we see very rapidly increasing demand for cyber defensive capabilities via data and very interesting data types. And this is an example of something where sufficient - Wait, can you help me understand? What data types do people want around security that they maybe didn't want before there was this explosion in demand? I have to be careful not to reveal too much about customer work. The category is growing very quickly. And the nature of a lot of security work is that it's adversarial, right? So it's not this sufficiency style work like update a CRM and then you're good.

48:00Osvald Nitski:There's a constant cat and mouse game between the offensive capabilities and the defensive capabilities, which to our point earlier about the 90 % of enterprise workflows that can already be completed, there's never going to be that 90 % for security because the goalposts are always going to move. So most cyber as a category is growing, and the nature of the data types is much more kind of like uncapped, evolving, adversarial in terms of where the goalposts are. Can you help me out here? You're Estonian by kind of heritage. I say to European founders, SF is the worst place to start a company. It is impossible to acquire talent.

48:37It is impossible to afford it. And then it's impossible to retain it. Is the talent war in SF as brutal as it seems?

48:46Osvald Nitski:Yeah, it's pretty brutal. It is very difficult to hire. It is difficult to retain. It's difficult. I think it's harder than before, but it's easy when you're on a rocket ship, right? It's always easy when you're on a rocket ship. To get someone, it's hard to make the right decisions about who you want to hire. When you've made a bad hire, what did you not see that you wish you'd seen? It's really hard to assess agency and ownership in the interview process. I am super freaking talented. I'm super talented. I'm a bit of an asshole. I'm not like a total asshole, but I'm a bit of a douche. Are you okay with that?

49:22Osvald Nitski:If you're super talented, yeah. The company culture here is of high agency, high performance, high ownership. Personalities can change. You can learn how to work with people better. We care about like growth and we care about, we want to hire people who give a shit. that's a lot harder to coach into someone than smoothing it out with with your colleagues getting some you know making sure that we have happy hours people get along like that that's easy to work out yeah like you got you can have a couple assholes they get drinks together a few times and then you smooth it out it's really hard to make someone give a shit yeah also if you hire multiple assholes they can just hang out together it's fine and it's a group of that we don't we don't hire a lot of ass.

50:03Osvald Nitski:I was like, what? No, no. Also, like happy hours? Like, really? We had a great offsite just recently, actually, with our annotation team. We went to Tofino in Canada. It's on the west coast of Canada. It's the only place you can surf. And everyone did surfing lessons. We went to a floating sauna. And it was a great time. I thought it was actually great for the team. And it was a great use of money. And everybody loved it. And I think that doing these outdoor activities where people are being active is good. Are you ready for a quickfire round, dude? Sure. Yeah. What have you changed your mind on most in the last 12 months?

50:34Osvald Nitski:Honestly, I think it's probably the environment, RL environment market, because when we were starting it off last year, it was so complicated to do these deliveries. It was so hard to get it to work that I just thought it wasn't going to work out. I thought it wasn't going to scale, but then it did. So I was pretty surprised. What changed? The demand was very high and we got it to work, right? We just had to try a lot of different things to get environments to actually improve model performance. So we just kept going at it and it ended up working. I'm your little brother and I'm studying computer science at universities today.

51:07You sit me down and say, little brother, you should know this. What should I know?

51:12Osvald Nitski:Get a real internship as soon as possible because whatever you learn in school is probably going to be outdated quickly. Where should I get a real internship? I know that sounds stupid, but should I start my own company? Should I join a fast growing company? Should I join a super established company where there's adults in the room, so to speak? Maybe I'm biased, but join a fast growing company in San Francisco. Doesn't need to have adults in the room, but somewhere on the frontier, that's indicative of where the field is going. A bit larger than 10 people, not super early, just to kind of like filter out the companies that might not go anywhere.

51:50Would you say that you're too late for me?

51:52Osvald Nitski:Mercore? No. No, we still act like a startup. How many people do you have? Maybe 500. Culturally, we're a startup. We're paranoid. We're in office all the time. We're fast moving. We want to hold on to that as long as possible. I love it. That's amazing. Totally. Absolutely. Yes. Which competitor do you most respect and why them? I don't think about competitors too much. They're all kind of even in that they're all behind Mercore. We really try not to think about them as much as we try to think about our customers. So I respect our customers a lot. I love the work that they're doing. We stay on top of what competitors are doing, but every time I look at one of their websites, they're just doing something we did like a week or a month ago.

52:36Osvald Nitski:We write a blog, someone else writes a blog like a week later, that's the exact same thing. We make an update to our website, someone else makes an update to their website, that's the exact same thing. So I spent a lot more time... Would you say that about Saj? It's happened before. Yeah, it's happened before. They're a bit out there. We honestly, like I don't spend that much time thinking about them because I spend more time thinking about customers. We've seen it. They're a bit out there in that they don't copy us as much. And they do seem a bit different from others in the field. Hard to say why.

53:06Osvald Nitski:They're very secretive. Yes, absolutely. Can you please paint the bull case for how McCore is a$200 billion company? Yeah, for sure. So it looks like we sell services. We're basically like a tech-enabled services company, our services are incredibly valuable in driving revenue gains for our customers, primarily through better model capabilities. Evals and training data are the primary bottleneck to model performance right now. If every enterprise needs to have specialized proprietary models, even if the capabilities start to saturate, the evals serve as the PRD for exactly what you want, but also the optimization objective for better performance.

53:48Osvald Nitski:As long as more and more, as long as better models are valuable to the economy, there will be demand for eval sets and training sets. If we can make that process faster and faster, we can serve a growing demand for human data for eval and training. And then we also have a growing agent deployment enterprise arm as well. What line of revenue do you not have today that you think will be very significant in three years time? I think that real world, like physical data is going to grow significantly over the next three years. Robotics is an interesting area for us. The data market for robotics is nascent relative to Gen.ai, relative to autonomous vehicles as well.

54:33Osvald Nitski:And we think that's going to grow a lot. Do you scale supply ahead of demand? We at times retain exceptional talent to do work that might be valuable in the future. And we can do like off-the-shelf data creation to basically like, you know, make use of supply when demand is low and then resell that data later. In that case, we do. Otherwise, we don't. What's the best piece of advice you've ever been given? I got a lot of advice to join small companies, join startups, move to San Francisco. I grew up in Canada. I went to school in Toronto. I followed that advice. I think it was great. I've loved living out here.

55:11Osvald Nitski:and I like small companies. I like fast-growing companies. It's been super fun and great for my career. Final one for you. What are you most excited about that you don't think enough people are talking about? Probably the same answer as before in that like the three years out opportunity of robotics. I think there's a lot of discussion around robotics on Twitter and in certain - I'm sorry, can you just help me out here? And this is where I get in trouble. It's Friday afternoon, it's past six. Fuck it, I can say what I want. I don't get it, okay? whenever you watch a robotics demo, they're like, you know, you see this kind of terribly moving robot around a home.

55:48And then after watching it, like take one water out of a fridge in 15 minutes, it goes, and Brendan was in the other room all along. And you're like, are you fucking kidding me? I had this absolute spacko in my kitchen for 15 minutes getting a water and Brendan was in my backroom doing it. That's where we're at. What am I not seeing? Help me help get excited.

56:11Osvald Nitski:Yeah, I think if you go back like a decade or so, like self-driving cars, they had the people in them all the time. You would see crews driving around San Francisco and there was like a person in it for years, for years, right? But now I take Waymo more than I take Uber. I'm thrilled for you. Welcome to London. We still have these people in cars. I love it, but it's in one city. It can't deal with like very ambiguous data. It's still like pretty irrelevant. It's made leaps and bounds in the past, at least in San Francisco and Austin, Phoenix. It's tough because, yeah, I guess the distribution is unequal, but it's an incredible service here in San Francisco and people here use it a lot.

56:54Osvald Nitski:So like technically it works and it might be, you know, there might be like regulatory challenges or other challenges with scaling. But you think we'll hit a chat GPT moment with robotics, which will cause an inflection in usage and adoption? Yeah, I think so. But I think it might play out similar to driverless cars where it's really hard to scale physical things as opposed to software. So it might be more of a more of like a Waymo robo taxi cruise type moment than a chat GPT moment. But I think the progress will be there. You have been fantastic. Thank you so much for putting up with this like incredibly wayward, poorly structured conversation, which was brilliant.

57:34And I so appreciate you putting up with it. Thanks for having me.

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