Speaker 1We can't spend money fast enough to service all of the demand that we have. We end every week with so much more money in the bank. I don't think there's an ROI problem right now. Even Figma, we're moving away from it in favor of cloud design more and more. Get a real internship as soon as possible, because whatever you learn in school is probably going to be outdated quickly.
Speaker 2This is 20 Product with me, Harry Stebbings, and today we have Oswald Nitzke, CPO at McCore, in the hot seat. Today, it's a really open conversation in a way that I don't think has been had with someone from McCore before about what happens if frontier models actually do what the data providers are going to do? How does synthetic data cannibalize their business? Does open source help or hurt data providers? Because their biggest customer, oh yeah, it's the closed frontier models. This and so much more in our conversation with Oswald today. But before we dive into the show, today, most companies have tried AI. Most aren't seeing results. Not because AI doesn't work, it's because AI hasn't reached the workflows yet. That's the gap Asana is built to close. Asana is the operating system for human agent teams, your easy button for AI productivity across every team. Ready-to-go AI teammates, pre-built for marketing, ops, and IT. No prompt engineering, no setup. They show up where the work is happening, already onboarded in your workflows, ready to deliver. With Asana, your whole company can work on the same plan, towards the same goal, whether you're a team of 10 or a team of 10,000. Asana, where humans and agents work flow together. Try it at asana.com. That's A-S-A-N-A dot com. While Asana keeps the work moving, ZeroHash helps move money on chain. Every great software company eventually runs into money. Uber had to move it. Shopify had to hold it. Airbnb had to settle it. Money movement. Stopped being a fintech problem, it became a software problem. And increasingly, god, it's an AI problem too. ZeroHash is the infrastructure that makes global instant money movement seamless. One API integration for stablecoins, digital assets, and modern payment rails, so builders can stay builders. ZeroHash powers some of the world's largest enterprises and financial institutions, including Calchi, Stripe, Morgan Stanley, Gusto, and Interactive Brokers. If you're thinking about stablecoins, digital assets, and the future of money, it's time to talk to the team at ZeroHash. Visit ZeroHash.com/20VC to learn more. While ZeroHash powers on-chain payments, Framer powers your website. When a new landing page turns into a pile of tickets and handoffs, Framer helps your team move faster. Here's what I love. Framer is the pro AI website builder for creators, teams, and businesses that care enough to get every detail right. The agents close the gap between AI generated ideas, and production-ready website work. Because it all happens where the site actually lives. It lands on the canvas, stays editable, and can be published when the team is ready. So you can build custom code components, create and manage CMS content, optimize SEO settings, and ship everything all in one place. The agents bring speed and scale. You bring taste, judgment, and control. It's an enterprise-level solution too. Premium hosting, enterprise-grade security, 99.99% uptime SLAs, which is why the world's leading brands like Perplexity and Miro build in Framer. Learn how you can get more out of your site from a Framer specialist, or get started by building for free today at framer.com/20vc for 30% off a Framer Pro annual plan. That's framer.com/20vc for 30% off. framer.com/20vc. Rules and restrictions may apply. You have now arrived at your destination. Oswald, it is so good to have you on the show. Dude, I've heard so many good things from Brandon. So thank you so much for making this happen, man. Thanks for having me. Super excited. I am seeing open, open, open. Everyone claiming that we will see the mass migration from Frontier closed to open. Kimmy very recently came out with their new model. And I didn't really know. Does open cannibalize McCore's core business?
Speaker 1I wouldn't say that open source model improvements cannibalize our core business because data's most valuable on the Frontier of model performance. So each of our customers has their own unique goals and is purchasing Eval and training data sets to fill gaps in current model capabilities. Open models just raise the floor of what people are interested in. As long as customers still have new capabilities that they want to get better at, our business still continues to grow. Open source models just mean that nobody is buying anything that Kimmy K3 can already
Speaker 2do. So if like 90% of enterprise workflows--. Can be done with open models, which more and more people say they can be. And that 10% is really where you serve your customers and provide data. I'm naive. Does that not make it harder and harder to make huge amounts of revenue if that 10% on Frontier moves further and further away?
Speaker 1I'm not convinced that 90% of enterprise workflows can be handled by open models or Frontier models right now. We think that these calculations might be based off of existing demand. There are things that come top of mind when current model users are thinking of what models can do. But there's a whole category of latent demand that people aren't even--. These are things that people aren't even trying to do with models yet. Most commonly, we think these are like long horizon tasks, like setting up a procurement agent to fully automate your procurement team for months on end. You only check on it maybe once a week. We think that's just not even captured in these calculations when someone says enterprise workflows are being handled because nobody's trying to do these things yet. The market for data to support those use cases is growing, and that's where we see a lot of the leaders moving to.
Speaker 2We see a lot of leaders moving there and seeing new capabilities that they never thought existed. But then we have Alex Karp, I thought a rather sedate performance. Normally, he jumps up and down much more, but it was still rather energetic. The way he said about the incredible skepticism we see from large enterprises towards data and sharing data with the Frontier model providers, to what extent do you see skepticism and fear from large enterprises in working with Frontier model companies?
Speaker 1We see it depend on the specific workflow and how core it is to the business. Things that are general things that every company needs to do, like HR, procurement, it can be less sensitive, and enterprises are more open to putting these workflows on proprietary models. It's the core work that the company is doing that's vital to its business, that differentiates it from competitors. We see more sensitivity, so you can imagine this being the actual legal services that a law firm provides, like what are the actual memos that it's writing, what is the advice that it's giving to its clients.
Speaker 2Am I the only one who sees the irony in we put the sensitive data on open source, most likely Chinese models, and we put the HR and procurement data on the closed model? Am I a moron?
Speaker 1Well, it depends on where you run the open models, right, whether or not that's a bad idea. The beauty about open weights models is that the inference can happen in multiple places, so you could make mistakes using them, but you have more control.
Speaker 2When you look at that dispersion, what do you think is inaccurate? You said you don't really believe the 90/10. What do you believe a more accurate representation is?
Speaker 1In our APEX benchmarks, we're getting closer to around 50% of long horizon workflows. Top models are scoring around that much. Workflows that are just sufficiency-based where you do it and it's done and you're good. This is something like updating a CRM. You couldn't really get much better at it. And then there's a class of workflows that we shouldn't even be thinking about in terms of binary, like can the models do it or not? And these can be things like legal arguments or, to an extent, medical advice where you could always get better. And in those cases, I think that the percentage framing is just totally off and we need to be thinking more about continuous.
Speaker 2When we think about it could be better, I had Lynne Quao, the founder of Fireworks on the show the other day, and she was like, exactly that is why we'll have specialized models for every single company, because it could be better, depends entirely on the company. One company wants to focus on growth. One company wants to focus on margin. Another wants to focus on, I don't know, if we're in Europe, work-life balance. And so you need individual specialized models for every company. Do you buy that we will have specialized models for every company, or is that a little bit self-serving towards Fireworks?
Speaker 1I buy it. I think it's also self-serving towards Mercore in that we think that every specialized model will need enterprise-specific eval and training data to show the model how to perform in its setting. And I think it depends on, I think the diversity and the market for this depends on the value that customers can get from these specialized models. I think there are cases where the ROI is really justified, and I think those cases will increase over time. But we certainly believe in this future, yeah.
Speaker 2Switching back, Alex Kopp's second point in that show was ROI questionability. You mentioned the word ROI, though, which is what made me think of it. It's very present, and enterprises maybe have questionability around the ROI that they're getting. Do you think we have an enterprise ROI problem with AI today?
Speaker 1I don't think there's an ROI problem right now. I think we're in a period of... of exploration and experimentation where there's more tolerance more patience to get that ROI calculation right now there's a lot of different projections around where token prices will go where performance will go right now we're we're starting to see some amount of tightening of the screws on spend here and there but I think the the paradigm we're in is still let's see what happens because things are moving so quickly that the ROI calculation might shift too dramatically still two ways I want to go in this I'll take the
Speaker 2first way we saw Aaron from ClickHouse say that he's 6x to spend and that's what they need to do because we need to be at the frontier and then you see Uber and Microsoft and some forms of I think it was Grok or X or one of Elon's companies put budgets on per user head what do you think is the right way to be navigating this cycle if I'm a founder listening what would your advice be on how I should think about optimizing the balance between performance and budget
Speaker 1it totally depends on the use case so I've mostly worked at hyper growth companies where growth matters at all costs right you willingness to spend for growth as long as the unit economics are fine when you're looking at coding agents spend for your software engineers that that's not cogs for your work you know that doesn't like if that's really high that could still be giving you compounding gains if you're looking at like a customer service agent that has massive token spend and you know the revenue you're getting from the customer is being served as like way lower than token spend then you're then you're definitely in a bad position but in my experience has been just these growth stage companies and I think for a lot of founders considering you know their token spend if it's on if it's for growth if it's for improving the efficiency of your headcount that's just what you need to do to service large amounts of demand when you're starting up
Speaker 2Mr Benioff from Salesforce said that he spends 300 million a year on anthropic which works out to be about 3.8 percent of developer salaries if you average the salaries do you think that is the going rate moving forward do you think that will be 20 percent or do you think it'll be a hundred percent or will
Speaker 1it be way less I hope that we can move towards a feature of better accounting of the outcomes being driven by token spend because even here I think in a company like Salesforce we have a company of that size certainly you're getting you should have different spend profiles depending on what the team is doing again here you have teams that might be more like solutions engineering or forward deployed where you have to think in terms about unit of unit economics and teams doing R&D where you can be more you can have more tolerance for spend so I think at the large companies you have to consider like which parts of your organization are doing what and how much tolerance should you have in different areas I think macro the percentage will increase over time to more than three percent Jordan has
Speaker 2something funny Brandon said on the show it would hit a hundred percent and he said that you already spend more today than you do on salaries yeah yeah yeah
Speaker 1we do hundred percent sounds reasonable so as I said like we're I've only worked at hyper growth companies and that's what Mercore is and continues to be more so and you know more so every day as the growth just accelerates for us it makes sense because the demand that we have is so high like the company is more than 10x and headcount since I joined the revenue is also commensurately increased we're just in a race non-stop to service our insatiable customer demand so for us it makes sense because we can't spend money fast enough to service all of the
Speaker 2demand that we have do we just build 10x more products quicker help me understand do we have smaller engine product teams do we just build much more than we ever
Speaker 1used to how do you think about that I think this paradigm makes the job of product management a lot harder because we're trying not to build 10x more product surface area it makes things incredibly chaotic we have moments in time where product surface area rapidly expands because people think oh I can make all these features really quickly this is like I could you know like let me just like push these multi thousand line PRS but we have we're constantly in this battle to try to simplify our product surface area and find the interactions and the workflows that are most scalable so the trend that we see is we're as a product team constantly fighting to reduce surface area and simplify things and we also see a higher ratio of PMs to Eng because engineering is less bottlenecked so there's much much more work to be done in like understanding the workflows of users the needs of users and what products actually drive revenue the most becomes the bottleneck now to servicing more demand
Speaker 2for us if we think about the kind of pre-ai era how has what it takes to be a great PM change for
Speaker 1this new world there's two major changes one is that you don't really need to learn as many like tools anymore you know you just have to be able to use uh coding agents like a couple tools will do everything you need you know even even figma is uh we're moving away from it in favor of cloud design more and more you know less less tool diversity for us and then the other is everyone needs to up level a lot and think about business impact much more I think that all work is starting to look like higher level so the the kind of like the minutia and the details get sorted out way faster and all the PMs at Mercora have to think way more about is what I'm focusing my time on the right thing I can do things very quickly now you know it's like there's a skill issues have almost gone away so now it's all about judgment and am I doing what is going to drive the most business value dude
Speaker 2I have to ask you said that like a core job is retaining simplicity and deciding what to do versus what not to do what did you do in product that with the benefit of hindsight you wish you
Speaker 1hadn't done and what did you learn one interesting thing that happened this year was our annotation platform serves a lot of different workflows the demand for human data is so large and it's so complex that and our delivery team is so good at delivering projects and selling projects that we supported I think too many workflows for human data projects and we built a tool that was extremely flexible in supporting all sorts of different research experiments that customers might want to do so the shape of data has changed a lot since it started with instruct GPT for gen AI from supervised fine tuning to preference ranking to all these environment type projects there's a lot of multimodal projects that have totally different formats and your annotation tool needs to support these and different workflows and customers will ask for all sorts of stuff we tried to serve every ask we made a tool that's maximally flexible has all sorts of we we had like hundreds of different projects running on it that's just chaos to manage and what we needed to do sooner was to put guardrails on the type of services that we support and we're closer with our operations team to say like hey here's the best practices you know customers are going to ask for everything like we can do it but should we do it if there's no enduring demand for certain workflows maybe it's not worth the investment so putting guardrails narrowing down the services that we support was something we should have done a lot sooner though we did it recently how do you determine enduring demand so this is uh what makes Mercor a hyper growth company is that we're incredibly tapped into the market and the ecosystem it's really judgment from leadership I think it's it's very hard to say kind of like what will data look like in a year or two and the best way to figure it out is to stay in constant touch with leaders from a diverse set of labs and constantly be validating hypotheses we I think Brandon does it very well I think our operations team does it very well but ultimately it's kind of like uh it's kind of a guess which
Speaker 2lab has the most advanced and sophisticated data team I can't speak too much to uh customer
Speaker 1details but um well super good everyone is everyone's sophisticated everyone blows me
Speaker 2away in in different ways that's such an unfair question okay I totally agree the the other question to ask is which has the worst team no I'm joking my question to you was you mentioned another element though which actually didn't shock me but I thought it was interesting was the movement away from figma can you talk to me about that because I hear more and more companies doing it what do you think about that and what was the thinking there to be honest I let the team do
Speaker 1whatever is best for them and this is a trend I've just observed amongst almost everybody is that cloud design has done a great job people really like using it it's easy to use and we've just had a natural movement towards it it's also a bit easier to not have too many tools not manage too many licenses and because cloud is like making all these other great great features uh people just gravitate towards it and then it's it's a bit less friction to have the procurement team issue licenses for figma for every single person
Speaker 2we were talking about the ROI earlier for enterprises and we're seeing Microsoft set up a services Department we're obviously seeing Palantir Skyrocket and services becoming an increasing part of everyone's business is that the future of AI Enterprise deployment and how do you think about the incredible rise of services in deployment I have a bit of a hot take here I think
Speaker 1it's the future for the short term as the knowledge of how to use AI gets disseminated throughout industry we have basically a concentration of a bunch of people in San Francisco who really know how to deploy agents eval agents be AI first in engineering and in other areas, that knowledge just isn't out there yet. And eventually it will be. And maybe you won't need at that point teams to go and set things up, set up AI agents for every enterprise, and it'll become more of like a job function, similar to software engineering.
Speaker 2And so in the short term, it enables deployment. In the long term, products become more and more sophisticated that they're able to do it themselves. Because Matan from Factory said to me, you know what, services, they're just an excuse for crap product.
Speaker 1I 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 FDEs though? Good engineers, 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. 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 FDEs. And I think that those engineers are also great fits to eventually become founders. And I think that that is a different profile of people. And I think that that is a different profile of a person who's incredibly valuable. And that's what a lot of people are looking for when they're looking for FDEs. 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.
Speaker 2Do you like that? I spoke to Brandon about this, but is it a good thing to have the McCall Mafia? Because you also want to retain talent.
Speaker 1I'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, someone to starting a company than to, you know, 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, I don't
Speaker 2want to work. Oh, 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?
Speaker 1I 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. And I think that's something kind of like really grokking 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, can you use the tool? Can you do all these other kind of like more junior things are becoming less relevant? So the hiring for us is biased towards more senior candidates.
Speaker 2Do you know, 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 know, you get the seasoned operator who fits exactly that rubric. And it never works. It never works.
Speaker 1We're not quite doing, you know, seasoned here is a spectrum, right? We're not, I'm not saying we're, we're hiring people who have, you know, are in like formerly in executive positions. We are treating everything as an executive search where we want to find someone who's at the sweet spot. 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...
Speaker 2When do you think people hit their prime? I think 25 to 35. Oh, I just turned 30. I'm back 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?
Speaker 1We've moved away from the take-home assignments. We've moved away from the 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 and, you know, produce this artifact for me and we'll look at it. 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 will 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
Speaker 2AI 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?
Speaker 1We 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
Speaker 2are so easy to, um, I'm so sorry. I'm so sorry to interrupt you. Good experiments and systems design. It feels quite wordy. What does that actually mean?
Speaker 1We 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, uh, 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, of cloud. That's so interesting.
Speaker 2I completely agree with you. I have it with my team, which is like, we do scripts for, 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 fricking hooked already?
Speaker 1I 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. Uh, you just have to learn personally where that boundary is of like, what's, when is a bad time, you know, um, 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's, it make you think that it's doing the right thing, but you have to be paranoid with them still, right? 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. So that's, that's, that's what I tell my team is that like, don't delegate your decision-making
Speaker 2to a model because you're going to, 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?
Speaker 1It varies, you know, that's, uh, it varies because we run a lot of experiments. Um, sometimes they don't, we want to get more that actually, uh, 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, this, these changes to our product have, have impacted the business in a positive way. That's a lot of the core job right now. So, you know, it's, it's week to week, month to month, we get different, different results, but we try to trend in the right direction over time. And, and people start to learn, learn the dynamics of the product, learn the dynamics of like the user is better and better to improve over time.
Speaker 2When you think about like product and engine running experiments, like you mentioned there and running good experiments, how do you structure the teams today? And what does that like meeting look like?
Speaker 1We 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. And then we also have, we have our 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 like product offering better. 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, 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 do the, 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 give them, we'll run the project and give them the whole data set.
Speaker 2Totally. Are they two separate product teams? They're two separate product teams.
Speaker 1How 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. Yeah.
Speaker 2So 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?
Speaker 1I think that the ratio of PM to end will change over time to have fewer engineers per PM as engineering velocity increases with better coding agents and we will be bottling necked by understanding business needs, user needs. That's more of a PM job. 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
Speaker 2about 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,
Speaker 1so within these product areas, we'll have like a whole PA like weekly in this product and engine, a lot of other stakeholders as well. 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. We'll have like a pod of these parts of the product that are, 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 any, you know, with working from record. 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
Speaker 2leaving early on the weekend. What do you not do in your product meetings that you should do to
Speaker 1make 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. 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 team's grown so quickly, like the communication is not as good as it used to be. So I think it's a good idea to keep that in mind. And then the communication channels like just explode very quickly. So we need to do more kind of like cross product area collaboration. Keeping it efficient is just really hard as the team grows because we're, you know, the nodes just keep moving around and there's more
Speaker 2of them. What has been the secret to scaling supply on the marketplace side so efficiently?
Speaker 1How have you guys done that so well? I'd probably put it down to three things. The first one is a great expert experience. Experts get paid on time. Transparently, everybody involved in what the expert experiences cares deeply about whether or not, like 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
Speaker 2set. Are people as short sighted as just being wanting to be paid the most? I've heard that
Speaker 1McCaw pays the most. 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, right, from the people running the project. It's really hard to sign up for online work and then you just like have, get hit with this like hundred page construction 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,
Speaker 2or is it one where actually margins relatively fixed given the complexity?
Speaker 1So, 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. So, there are cases where you could have automatic quality control and synthetic data improvements to make the delivery better. Situations where you could, you know, think about the staffing on the project to change the cost of the service. All of that, like we, 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, you know, consideration of, of costs. And, you know, now for a lot of these projects, the costs are driven from paying experts and LLM spend on things
Speaker 2like synthetic data and automatic quality control. Does 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?
Speaker 1It doesn't annoy me, no, because, you know, we end every week with millions more in the bank, right? So, it's, 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 can have all these different metrics, but we end every week with so much more money in the bank, like the business is very healthy and we can't, we can't spend money fast enough. So, what people want to call it is, you know, up to them, but like the, the cash flow is insane. Does it matter that you have
Speaker 2such high revenue concentration? You know, the frontier model providers, your biggest customers by far, some would say that's a lot of concentration. How do you think about that?
Speaker 1I can answer this from kind of like a, how it affects the product team. 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. 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,
Speaker 2making it really simple, explaining it? What is the challenge with democratizing?
Speaker 1Running 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 and all the edge cases matter, right? So we'll try to write a guideline that says like, here's how you make a data point, but the experts will have like some edge case that gets bubbled up and like what you do on that edge case matters a lot. So the process of making a human data project is basically like 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. It fits into the guidelines the customers have and the projects are 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 bunch 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
Speaker 2very crisp communication that make it challenging. What data type is not hugely in demand today that you think will be hugely in demand next year? The data type that's growing the fastest for us is
Speaker 1environments. You might have seen a lot about these RL environments on Twitter. It's kind of like a hype term. Every company kind of like has a different definition for it. But we are certainly going to look at RL in the category and view it as basically these like simulations of apps that you might want your agent to use. And also as a rich start state, which we call like 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. It's a bit of a complicated annotation process because the agent has to like 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, SFT was really hard to get set up when InstructGPT first came out. So this is the frontier right now. Labs are figuring it out, Neo labs are figuring it out. Eventually it'll get so smooth that enterprises can do it too.
Speaker 2Are 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."
Speaker 1Well, 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- The business outcomes of our customers. So we have a great setup where if you're making an eval set and you're a 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 represents what their customers want to do. So as long as 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. People want to crank it harder and harder because spend on record directly translates, have more revenue for our customers.
Speaker 2Do you think we'll have an unbundled data provider world? I'm a venture investor. I see so many people who are like, "Oh, we're like McCore, but for domestic robotics." And you're like, "Okay, cool. Good. Okay, I get it." But do you think we will see this specialized data provider world where niches have thousands of players?
Speaker 1To an extent, we're already in this world. It's not that successful. Not at all, 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. 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 totally mispriced. Someone raises a bunch of money. They have loads of cash to blow, and they go to these labs and they're like, "I need to get your business. 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 taking the profits home themselves. It doesn't scale, though, and 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 this annotation.
Speaker 2Don'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 me. 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?
Speaker 1Of course, we do a lot of competitive intelligence. Our customers like us, so 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 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. So it's a totally healthy thing to happen as long as Mercor is winning.
Speaker 2We said frontier earlier. I'm an investor in Legora, and everyone's like, "No, your real competition is actually Anthropic." If you 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 Legora and Figma, and Anthropic's also working on the frontier problems that humanity faces today?
Speaker 1That'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. And coincidentally, also try to solve climate change and cancer. But it's not their main business. And they have their hands in a lot of different areas. But competitors still emerge. So you remember Google+, right? That didn't go anywhere, right? Maybe it freaked some people out when it happened. You probably remember Threads. I don't know the current state of Threads.
Speaker 2Apparently 400 million users, according to their marketing team. That's very interesting.
Speaker 1I won't comment too much on that because I... How fascinating. 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 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 history with Google and Microsoft having many businesses. Business units, many efforts, but a core business that has driven all of their revenue.
Speaker 2You 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 I'm here for you." 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. I'm sorry for that, because it's horrible to go through. How did it change your product mindset?
Speaker 1You know, I'm not an expert in security, but we hired a lot of experts in security and I listened to them. And that's the main changes, just larger investment and learning from the experts that we've brought
Speaker 2in-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 producer huge amount of output. And the threat is going to increase much more significantly than we're anticipating.
Speaker 1Most 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. So we love this kind of stuff because it's like a game from a data perspective and we see very rapidly. There's obviously increasing demand for cyber defensive capabilities via data and very interesting data types. And this is an example of something where sufficiency...
Speaker 2Wait, 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?
Speaker 1I 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. 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.
Speaker 2Can 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, it is impossible to afford it and then it's impossible to retain it. Is the talent war in SF as brutal as it seems?
Speaker 1Yeah, 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. 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.
Speaker 2I am super fricking 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?
Speaker 1If 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 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 your colleagues, making sure that we have happy hours, people all get along. That's easy to work out. 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.
Speaker 2Yeah, also if you hire multiple arseholes, they can just hang out together. Fine, let's groupify.
Speaker 1We don't hire a lot of assholes. No, I can be 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 like outdoor activities where people are being active is good. Are you ready for a quickfire round, dude? Sure, yeah.
Speaker 2What have you changed your mind on most in the last 12 months?
Speaker 1Honestly, I think it's probably the environment, our 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 like, I was pretty surprised. What changed? The demand was very high and we just, we got it to work, right? We just had to try like 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.
Speaker 2I'm your little brother and I'm studying computer science at university today. You sit me down and say, little brother, you should know this. What should I know?
Speaker 1Get a real internship as soon as possible because whatever you learn in school is probably going to be updated quickly.
Speaker 2Where 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, you know, adults in the room, so to speak?
Speaker 1Maybe 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.
Speaker 2Would you say that you're too late for me?
Speaker 1Recore? No, no, we still act like a startup.
Speaker 2How many people do you have?
Speaker 1Maybe 500. Culturally? 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.
Speaker 2I love it. That's amazing. Totally. Absolutely. Yes. Which competitor do you most respect and why them?
Speaker 1I don't think about competitors too much. They're all kind of even in that they're all behind Recore. 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. 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. You know, they write, 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 we, I spent a lot more time. Would you say that about SUDGE? It's happened before. Yeah, it's happened before. They're, 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, they're a bit out there in, in that they, they don't copy us as much. And they do seem a bit different from others in the field. Hard to say why they're very secretive.
Speaker 2Yes, absolutely. Can you please paint the ball case for how McCore is a $200 billion company?
Speaker 1Yeah, for sure. So it looks like we sell services. We have, we're basically like a tech enabled services company. Our services are incredibly valuable in driving, you know, 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 eval serve as the PRD for kind of like exactly what you want, but also the optimization objective for better performance, as long as more and more, as long as better models are valuable to the economy, there'll 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.
Speaker 2What line of revenue do you not have today that you think will be very significant in three years time?
Speaker 1I think that real world like physical data is going to grow significantly over the next three years. Robotics is, robotics is an interesting area for us, the data market for robotics. Robotics is nascent relative to gen AI, relative to, you know, like autonomous vehicles as well. 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 and then resell that data later. In that case, we do. Otherwise, we don't.
Speaker 2What's the best piece of advice you've ever been given?
Speaker 1I 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 and I like small companies. I like fast growing companies. It's been super fun and great for my career.
Speaker 2Final one for you. What are you most excited about that you don't think enough people are talking about?
Speaker 1Probably the same answer as before in that like the three years out. I think there's a lot of opportunity of robotics. I think there's a lot of discussion around robotics on Twitter and in certain.
Speaker 2I'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 like demo, they're like, you know, you see this kind of terribly moving robot around a home and then after watching it, like, take one water out of a fridge in 15 minutes. It goes. And Brandon was in the other room. And you're like, are you fucking kidding me? I had this absolute spacko in my kitchen for 15 minutes getting a water and Brandon was in my back room doing it. That's where we're at. What am I not seeing? Help me help get excited.
Speaker 1Yeah, I think I think if you go back like, you know, a decade or so, like self-driving cars had, you know, 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.
Speaker 2I'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.
Speaker 1It's made leaps and bounds in the past decade, at least in, you know, 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. So like technically it works. And it might be, you know, there might be like regulatory challenges or other challenges with scale scaling.
Speaker 2But you think we'll hit a chat GPT moment with robotics, which will cause an inflection in usage and adoption?
Speaker 1Yeah, 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.
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