The Exchanges

Q When you look at that dispersion, what do you think is inaccurate? You said you don't really believe the ninety-ten. What, what do you believe a more accurate representation is?

A Well, in our, uh, apex, uh, benchmarks, we're getting closer to around, uh, 50%, um, of long horizon workflows. Um, top models are scoring around, around, around that much, but I think that, um, the percentage for, there's a, there's a class of workflows that are just sufficiency based where you do it and it's done and you're, you're good. This is something like updating a CRM. Um, 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, you know, binary, like, can the models do it or not? Um, and these can be things like legal arguments or, Uh, to an extent, medical advice where you could always get better. Um, and in those cases, I think that the percentage framing is, is just totally off, and we need to be thinking more about continuous uncapped rewards.

AI assessment note: “we're getting closer to around, uh, 50%, um, of long horizon workflows.”

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

A The data type that's growing the fastest for us is environments. You know, you might have seen a lot about these RL environments on, on Twitter. It's kind of like a, you know, hype term. Every company kind of, like, has a different definition for it, but we are certainly the leader, um, in the category, and view it as basically these, like, simulations of apps that you might want your agent to use, and also 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, 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 eviled and trained on, looks a lot closer to what they see in deployment. Right, 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 instruction, uh, when InstructGPT first came…

AI assessment note: “The data type that's growing the fastest for us is environments.”

Q 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% and frontier moves further and further away?

A I'm not convinced that 90% of enterprise enterprise workflows can be handled by Open models are frontier models right now. We think that these calculations might be based off of existing demand or 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, you know, 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, the leaders moving to.

AI assessment note: “I'm not convinced that 90% of enterprise enterprise workflows can be handled by Open models”

Q Yeah. Are you kidding me? Um, yes, absolutely. Um, I totally get that. Can you please paint the bull case for how McCore is a two hundred billion dollar company?

A It looks like, um, we, we, we're, we sell, uh, services. We have, we're basically like a tech enabled services company. Our services are incredibly valuable in driving, um, you know, revenue gains for our customers, um, 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 kind of like exactly what you want, but also the optimization objective for better performance. As long as more and more, uh, 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.

AI assessment note: “we can serve a growing demand for human data for eval and training”

Q 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% It's 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?

A I'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 or 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, you know, 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.

AI assessment note: “I'm not convinced that 90% of enterprise workflows can be handled by open models”

Q When you look at that dispersion, what do you think is inaccurate? You said you don't really believe the ninety-ten. What, what do you believe a more accurate representation is?

A Well, in our, uh, apex, uh, benchmarks, we're getting closer to around, uh, 50%, um, of long horizon workflows. Um, top models are scoring around, around, around that much, but I think that, um, the percentage for, there's a, there's a class of workflows that are just sufficiency based where you do it and it's done and you're, you're good. This is something like updating a CRM. Um, 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, you know, binary, like, can the models do it or not? Um, and these can be things like legal arguments or, Uh, to an extent, medical advice where you could always get better. Um, and in those cases, I think that the percentage framing is, is just totally off, and we need to be thinking more about continuous uncapped rewards.

AI assessment note: “we're getting closer to around, uh, 50%, um, of long horizon workflows.”

Q Dude, I have to ask. You said there, 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 hadn't done? And what did you learn?

A So, one, uh, interesting thing that happened this year was, um, our annotation platform serves a lot of different workflows. And the demand for human data is so large and it's so heterogeneous that And our delivery team is so good at delivering projects and selling projects that We, um, 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, uh, shape of data has changed a lot since it started, um, with InstructGPT 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. Um, 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. Um, and what we needed to do sooner was to put guardrails on the type of services that we support, uh, and work 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 …

AI assessment note: “narrowing down the services that we support was something we should have done a lot sooner”

Q Do 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?

A 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, uh, 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. Um, it is tough, though. It makes the job of management a lot harder because it, 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.

AI assessment note: “difficult, but I like it, and I'd rather be in an environment like this”

Q What's so hard about it? Making it really simple? Explaining it? What, what is the challenge with not dumbing down, but democratizing?

A Running a human data project is just hard. Um, There is, ah, 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 people will try to write a guideline that says, like, here's how you, 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 whatever guidelines the customers have, and the projects are running on time, all the bottlenecks are removed. Um, it's just an operationally intense process because it necessarily deals with, um, edge cases and things that haven't seen before and are outside of model capabilities. The data types also change very frequently. So we're, we've moved from supervised fine tuning to preference ranking to, uh, rubric based, um, annotation to now RL environments across a whole bunch of different modalities. There's a lot of complexity within each project and then…

AI assessment note: “I would boil it down to those two things of like the need for paranoia”

Q Um, when they, when they, when they go on a project with you, uh, are they price sensitive? Like, are they haggling going, oh, well, you know, Edwin at Surge gave me a 10% discount. Can I have that? Or are they like, just give me the fucking data?

A Well, there's always the, you know, aspect of negotiation and the procurement team trying to get a better deal, um, but we're, uh, we've chosen a great business where our work directly affects the business outcomes of our customers, right? So, we, 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, right, you would make more revenue. If we're, 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, um, they're happy to crank the lever. Like, people want to crank it harder and harder because spend on record directly translates to have more revenue for our customers.

AI assessment note: “there's always the, you know, aspect of negotiation and the procurement team”

Q you think we'll have an unbundled data provider world? You know, I, I'm a venture investor, and I, I see so many people that are like, oh, we're like, we're like McCaw, but for like, you know, uh, 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?

A To an extent, we're already in this world. Um, I wouldn't, Uh, 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, um, as the skill bar for annotation, uh, gets higher and higher as models get better, you have startups where the founders are actually just making the data, right? And labs love this because it's just like Totally, uh, mispriced. You know, they get, like, someone raises a bunch of money, um, they have loads of cash to blow, and they go to these labs, and they're like, I need to, like, I need to get your business, like, please let me work for you. And then they're smart people. They're founders. They're formerly, like, great technical employees. Um, but they're running the projects themselves. They're doing the annotation themselves. And this is just, like, 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. Um, 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, and vendors, our customers know thi…

AI assessment note: “To an extent, we're already in this world.”

Q 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% and frontier moves further and further away?

A I'm not convinced that 90% of enterprise enterprise workflows can be handled by Open models are frontier models right now. We think that these calculations might be based off of existing demand or 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, you know, 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, the leaders moving to.

AI assessment note: “I'm not convinced that 90% of enterprise enterprise workflows can be handled”

Q Yeah. Are you kidding me? Um, yes, absolutely. Um, I totally get that. Can you please paint the bull case for how McCore is a two hundred billion dollar company?

A It looks like, um, we, we, we're, we sell, uh, services. We have, we're basically like a tech enabled services company. Our services are incredibly valuable in driving, um, you know, revenue gains for our customers, um, 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 kind of like exactly what you want, but also the optimization objective for better performance. As long as more and more, uh, 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.

AI assessment note: “Evals and training data are the primary bottleneck to model performance right now.”

Q 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% It's 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?

A I'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 or 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, you know, 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.

AI assessment note: “I'm not convinced that 90% of enterprise workflows can be handled by open models”

Q Yes, absolutely. Can you please paint the bull case for how McCore is a two hundred billion dollar company?

A Yeah, 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 evals 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 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, um, arm as well.

AI assessment note: “Evals and training data are the primary bottleneck to model performance right now.”

Q Do you want to hear something funny? Brandon said on the show that it would hit a hundred percent. And he said that you already spend more today than you do on salaries.

A Yeah, yeah, yeah, we, we do. Um, and, uh, a hundred percent sounds reasonable. We're, um, so as I said, like we're, uh, I've only worked at hyper growth companies and that's what Mercore is and, uh, continues to be, um, more so, you know, more so every day as the growth, uh, just accelerates. And for us, it makes sense because the demand that we have is so high. Um, we're just, um, like the, the company is more than 10 X in headcount since I joined. The revenue is also commensurately increased. We're, we're just in a race nonstop to service our insatiable customer demand. So, um, for us, it, it makes sense because we're, we can't spend money fast enough to service all of the demand that we have.

AI assessment note: “Yeah, yeah, yeah, we, we do. Um, and, uh, a hundred percent sounds reasonable.”

Q Help me understand. Do we have smaller engine product teams? Do we just build much more than we ever used to? How do you think about that?

A I think this Paradigm makes the job of product management a lot harder because we're trying not to build 10 X more product surface area. It makes things incredibly chaotic. We have moments in time where product surface area, uh, 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. Um, but we have, uh, 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, uh, as a, as a product team constantly fighting to reduce surface area and simplify things, um, and we also see a, uh, higher ratio of PMs to ENG, uh, because engineering is less bottlenecked. So there's much, much more work to be done in, uh, like understanding the, uh, Uh, workflows of users, the needs of users, and what products actually drive revenue the most becomes the bottleneck now to, uh, servicing more demand for us.

AI assessment note: “we also see a, uh, higher ratio of PMs to ENG”

Q Dude, I have to ask. You said there, 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 hadn't done? And what did you learn?

A So, one, uh, interesting thing that happened this year was, um, our annotation platform serves a lot of different workflows. And the demand for human data is so large and it's so heterogeneous that And our delivery team is so good at delivering projects and selling projects that We, um, 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, uh, shape of data has changed a lot since it started, um, with InstructGPT 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. Um, 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. Um, and what we needed to do sooner was to put guardrails on the type of services that we support, uh, and work 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 …

AI assessment note: “putting guardrails, narrowing down the services that we support was something we should have done”

Q Question. Do good engineers really want to be FDs though?

A I think good, 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 person, um, who's incredibly valuable, and that's what a lot of people are looking for when they're looking for FDs, um, and it's also like a profile that we look for generally, which is why we have so many, uh, alumni go off and start, uh, companies.

AI assessment note: “those engineers are great fits for FDEs”

Q Do 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?

A 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, uh, 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. Um, it is tough, though. It makes the job of management a lot harder because it, 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.

AI assessment note: “it's difficult, but I like it, and I'd rather be in an environment like this”

Q Are people as short-sighted as just being, wanting to be paid the most? I've heard that McCaw pays the most. Is that just a secret?

A It's, it's not, I wouldn't say it's like, you know, short-sightedness because we want to retain the top experts as well, right? So if you get paid a lot on like one project and it's like, you know, there's like some kind of like, I, I know there are a lot of other, um, uh, competitors in the space who will do some crazy like bonus payouts and stuff for short-term sprints. Um, that's not, that doesn't get you to come back as much as a great Experience with, ah, a lot of work. Um, visibility into, like, what future work is coming up. The feeling of, like, I'm growing my skill set. I have, um, the ability to pick between a few different jobs. Um, I have, 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 instruction document. It's a very foreign kind of job. That's part of the experience as well. And get, 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, um, interested.

AI assessment note: “I wouldn't say it's like, you know, short-sightedness because we want to retain”

Q What's so hard about it? Making it really simple? Explaining it? What, what is the challenge with not dumbing down, but democratizing?

A Running a human data project is just hard. Um, There is, ah, 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 people will try to write a guideline that says, like, here's how you, 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 whatever guidelines the customers have, and the projects are running on time, all the bottlenecks are removed. Um, it's just an operationally intense process because it necessarily deals with, um, edge cases and things that haven't seen before and are outside of model capabilities. The data types also change very frequently. So we're, we've moved from supervised fine tuning to preference ranking to, uh, rubric based, um, annotation to now RL environments across a whole bunch of different modalities. There's a lot of complexity within each project and then…

AI assessment note: “the need for paranoia and the need for very crisp communication that make it challenging”

Q Um, when they, when they, when they go on a project with you, uh, are they price sensitive? Like, are they haggling going, oh, well, you know, Edwin at Surge gave me a 10% discount. Can I have that? Or are they like, just give me the fucking data?

A Well, there's always the, you know, aspect of negotiation and the procurement team trying to get a better deal, um, but we're, uh, we've chosen a great business where our work directly affects the business outcomes of our customers, right? So, we, 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, right, you would make more revenue. If we're, 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, um, they're happy to crank the lever. Like, people want to crank it harder and harder because spend on record directly translates to have more revenue for our customers.

AI assessment note: “there's always the, you know, aspect of negotiation and the procurement team trying to get a better deal”

Q you think we'll have an unbundled data provider world? You know, I, I'm a venture investor, and I, I see so many people that are like, oh, we're like, we're like McCaw, but for like, you know, uh, 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?

A To an extent, we're already in this world. Um, I wouldn't, Uh, 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, um, as the skill bar for annotation, uh, gets higher and higher as models get better, you have startups where the founders are actually just making the data, right? And labs love this because it's just like Totally, uh, mispriced. You know, they get, like, someone raises a bunch of money, um, they have loads of cash to blow, and they go to these labs, and they're like, I need to, like, I need to get your business, like, please let me work for you. And then they're smart people. They're founders. They're formerly, like, great technical employees. Um, but they're running the projects themselves. They're doing the annotation themselves. And this is just, like, 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. Um, 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, and vendors, our customers know thi…

AI assessment note: “To an extent, we're already in this world.”

Q I 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?

A If you're super talented, yeah. Um, We're, ah, yeah, I mean, we're, the, the company culture here is of, ah, high agency, high performance, high ownership. Personalities can change, you can learn how to work with people, ah, better, and, um, but, but we, we care about, like, growth, and we care about, like, we, we wanna hire people who give a shit. That's a lot harder to coach into someone than, you know, Uh, smoothing it out with, with your colleagues, getting some, you know, making sure that we have happy hours, people all get along. Like, that, that's easy to work out. You know, 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.

AI assessment note: “If you're super talented, yeah.”

Q Oh, no, I'm, No wonder you left Europe. Um, 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?

A 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 grokking how we operate, how we, 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, more junior things are becoming less relevant? So the hiring for us is biased towards more, more senior candidates.

AI assessment note: “we've biased towards more senior hires who are better at understanding what drives the business”

Q What has been the secret to scaling supply on the marketplace side so efficiently? Like, That's fucking hard. How, how, how have you guys done that so well?

A I'd probably put it down to, um, three things. Um, the first one is a great expert experience. So, uh, experts get paid on time. They get paid well, uh, transparently. Um, the, everybody involved in what the expert experiences cares deeply about, um, Whether or not, like, whether or not they're having any challenges, and whether or not the work is, um, dignified, and well paid, and fairly paid, and that is a requirement for a great referrals program, because nobody's gonna refer their friends to, uh, you know, their colleagues to some kind of job that's like, that sucks, right? So, everybody caring about expert experience, Drives a great referral program, and additionally, a great sourcing team that's able to find people in every corner of the world with very specific skills, um, helps us fill the gaps when, uh, you know, we have spiky demand for a specific skill set.

AI assessment note: “I'd probably put it down to, um, three things. Um, the first one is”

Q 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.

A It totally depends on the use case. So I've, I've mostly worked at hyper growth companies where growth matters at all costs, right? Your willingness to spend for growth as long as the unit economics are fine. When you're looking at coding agent 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 customers being served is, like, way lower than the 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, um, you know, their token spend, if it's on, if it's for growth, if it's for improving the efficiency of your head count, that's just, uh, what you need to do to, to service large amounts of demand when you're starting up.

AI assessment note: “It totally depends on the use case. So I've, I've mostly worked at hyper growth”

Q If we think about the kind of pre-AI era, how has what it takes to be a great PM changed for this new world?

A 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, 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. Uh, 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 Mercore 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, ah, skill issues have almost gone away. So now it's all about, ah, judgment, and am I doing what is going to drive the most business value?

AI assessment note: “There's two major changes. One is that You don't really need to learn”

Q Do you like that? I spoke to Brandon about this, but is it a good thing to have the McCaw Mafia? Because you also want to retain talent.

A 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, uh, 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, oh, I don't want to work.

AI assessment note: “It's a lot better to lose someone to starting a company”