Why experts writing AI evals is creating the fastest-growing companies in history | Brendan Foody (CEO of Mercor)

25:45How do we lean into that future? Because there's so many things that are going to change over the next 10 years. And I think those are the kinds of businesses that are going to be successful. Let's talk about labor markets more broadly. You guys, so it's interesting though, you started not feeding people to AI labs, not training models. It was just like help people find jobs, help companies hire, and then you're like, oh wow, this whole opportunity. You have this really interesting view on the future of just labor markets and hiring. Talk about that. Yeah, it's interesting. I remember when we started the company as I mentioned, we were 19 and just had this like gut intuition that it felt so wildly inefficient that labor markets are so disaggregated.

26:26And what I mean by that is when we would hire someone internationally, they would apply to a dozen jobs. When we as a company in the Bay Area were considering candidates, we would consider a fraction of a percent of candidates that were available in the market. And the reason for that is that there is this matching problem that everyone is solving manually, where they'll manually review resumes, they'll manually conduct interviews, and manually decide who to hire. But when we're able to automate that matching problem at the cost of software, it makes way for this global unified labor market that every candidate applies to and every company high errors from facilitating a perfect flow of information in the economy.

27:08And I think that that future is undoubtedly what we're heading towards, but what we've realized over time is that the nature of work is also changing dramatically. And part of building that future over a 10 -year time horizon is creating that future of work. And all of the more tactical things we do in building these incredible data sets across us evals and RL environments for our customers. The way that what I've seen in how hiring has changed, I'm doing research on this with a partner known. It's so much easier to apply for companies that everyone's just applying out to hundreds of companies.

27:45AI is just making it easy to adjust their resumes and cover letters and make it feel like I applied to more of course, very specifically, but it was one of a hundred places. And then on the flip side, hiring managers are getting flooded with applications. And so now they need AI to filter. So even if we didn't want to get to this place, we're almost being pushed into this direction of so much volume on both sides We need something really smart at filtering and helping us hire and select this is exactly what you guys have been building for a long time Precisely. Yeah, and the fascinating thing like a lot of people ask are we do we think about ourselves as a labor marketplace Or do we think about ourselves as a data company?

28:23And I think that But the reason it's an interesting question is our realization on the, from what the labs need is that they actually need a labor marketplace. They actually need these exceptionally high caliber people and of course will, you know, layer on some project management and some software platform associated with it. But the really core thing that they want is how do they find these extraordinary professionals across all of these different domains that can measure model capabilities and work to build that future work together. This episode is brought to you by Interpret. Interpret is a customer intelligence platform used by leading CXN product orgs like Canva, Notion, Proplexity, Strava, Hinge, and Linear.

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29:50That's e -m -t -e -r -p -r -e -t dot com slash Lenny. Going back to just how this all works and what you guys do for models, I was talking to a friend who had an ankle sprayer, who was hurting, and he got an X -ray, and he fed the X -ray into Chatcha B -T, and then asked him, like, give me this specific X -ray, and he's like, okay, sure. And then he gave him, here's what you have. And he was talking to me, he's like, how did, what is out there on the internet, to train this model to know this stuff? And I was like, no, it's actually somebody sitting there helping the model understand this. Once they recognize it doesn't fully understand this, like humans are actually helping them learn these things.

30:29Exactly. Well, so the way it works, at least what most people's understanding is, there's a lot of complexity in how the models work is that pre -training gets a lot of the knowledge into the model. What are all the different things that sort of see in the world? and then post training and reinforcement learning is for all the reasoning of what are the pieces of knowledge that are accurate? What are inaccurate and what to prioritize at any given time to make a decision? And so behind that there would have been radiologists that worked on the post training data set to create some stasis point for here's the diagnosis and rewards and penalties associated with it.

31:07And it's really the quality of those people that went into the quality of the decision and recommendation that ChatGPT ultimately made. So let's actually follow that right because that's really interesting and I don't know how many people understand it, I sort of understand it. So the work that you do and these experts do is post -training. It's not feeding data into the model that it's trained on. We have this model at GPT -5. Now here's all the things it's missing. Let's add to it. Exactly. Yeah, it's really unlocking, allowing the model to focus on all the right tokens from pre -training all the right things in model context, upgrading the effective reasoning chains to enable the models to reason better in a more generalized way.

31:47What's the scale of people just working on the stuff? Is like thousands, tens of thousands, hundreds of thousands? Tens of thousands at a given time, hundreds of thousands more generally. I mean, it's huge. And the most exciting thing is that it's growing really quickly. I mean, I think that to your question also about the competitive landscape, historically, there were all these crowdsourcing companies that would get these super high volumes of low skilled people, I think like scale and surge, were the primary companies that pioneered that industry. And then in this transition to higher skilled labor, what people realized is that actually you can go a lot further with just getting higher caliber people, even in smaller amounts initially.

32:30Now, subsequently, scaling that back up once they're able to meet the quality bar. And I think that there's a bunch of companies that after our success and very rapid revenue growth that started early last year have chased after that, which makes sense, right? And seeing that the market was changing very quickly, we were taking off and trying to pursue a similar thesis on the market. It's interesting. There's always been these companies, Alphasites and GLG that sort of did this before AI or as like Pay to connect to an expert and ask them questions about stuff and essentially, okay, it turns out this is really useful for For models.

33:09We don't need the person in the middle exactly. Yeah. Well, but one core difference is that Alphasites would generally be a one -off call first is a lot of our work is really hiring people for projects, right? Of how do they work on something for a longer period of time? And so that's, I think, one of the reasons that some of the traditional expert networks have struggled to get into this. And also, how do you retain those people and think about all the incentives where it actually looks more similar in some ways to one of the traditional labor marketplaces of Enubar or Dordash just with much higher skilled talent that's treated exceptionally Well, it's such a good opportunity for me to learn so much about this.

33:53So I'm just going to ask questions. Yeah. So interesting to me. How much of the expert is focused on specific concrete knowledge versus personality and like softer skills? How much of it is like here's how you do an exam. Here's how you do an X rate. It depends on the lab. It's a lot of both. I think that previously it might have been more softer skills, but now a lot of the labs are focused on their business models. of what are the economically valuable capabilities that drive revenue and leaning a lot into these professional domains. But I think the creative side is also still really important to everyone.

34:31And so we're seeing a meaningful amount of both. Like we hired all the people from the Harvard Lampoon a couple of months ago, their comedy club to help with making models funnier. And so do all sorts of stuff like that, hiring Emmy award -winning screenwriter and everything across the board on creative capabilities that you look for. That is amazing. What a cool story. Yeah, I'm excited for this to kick in. How fast do these things turn around? Like say you hired this team, how fast are we going to see the impact potential? He's like months, is it years? Well, so it depends. Because some models or some labs will release iteratively where they'll just improve the model behind the scenes.

35:10Sort of every now and then again, you model. Exactly. As sort of every couple of weeks versus others do these big releases. And so it depends a lot. We're behind all of them. But the, I mean, we move really fast. It would be a customer gives us a request of we need these, you know, award -winning screenwriters and within 24 hours, we'll turn around the experts. And there's also this really interesting dynamic where in a set of 100 people that we hire, oftentimes the top 10 percent of people will drive majority of the model improvement. It's sort of like a company, If you have a 100 % company, oftentimes the top 10 % of the company will drive majority of the impact.

35:51What that means is that when we're able to build proprietary advantages in identifying who are those top 10 % of people, both in so far as how do we have them on our platform but also identify and match them effectively, it creates so much value for customers that it's difficult to compete against. And so it really does tie back to the founding thesis of the company, which is, how do we find these extraordinary people and identify them so that we can reliably deliver these top 10 % or top 10X experiences for our customers? So on that, so as the idea, you hire Jane, she's incredible at coding, and she now works for Enthoropic, and that's her full -time job doing this, or is this like a part -time thing, is this a project thing, mostly?

36:37It would sometimes be part time, sometimes it would be full time. I would say most often it's part time where it's like, you know, someone might work at a thing company where they're under employed, maybe one of the ones that's moving slower, where they have an extra 20 hours a week and that they're able to do this on the side or, you know, whatever the equivalent is, sort of across a bunch of different industries. but we also do a lot of 40 -hour -a -week roles as well. How much are they making? Is it meaningful enough for Faye engineers to spend time on this? Yeah, very meaningful. Our median pay rate in the marketplace is $95 an hour, but it can flex up well up into $500 an hour, based on the depth of someone's expertise.

37:25One thing that highlights this difference relative to a lot of the crowdsourcing companies is if you look at the economics of the crowdsourcing companies oftentimes they would pay like $30 an hour to town a sort of the average. And so think about the people that you can hire the undergrads for $30 an hour versus the Goldman bankers, the McKinsey analysts, the things software engineers, and ultimately it comes down to what are the capabilities that labs want their models to have? And it much more falls in the latter bucket than the former one. I know there's only so much you can talk about with this stuff, but so Anthropic Cloud has been so good at coding so much better, historically than other models.

38:10I also use it for writing, giving feedback on writing. What is it that allowed them to get so good at this and continue to be so good at this? Well, I can't go too much into detail about customer work, but I think that it's this trend of reinforcement learning and being very thoughtful about defining the right rewards that we're really seeing across the board and how we can mitigate reward hacking, set up the right rewards, and that's super impactful. He bells again. He bells is all he needs back to VVALs. My favorite quotes from customers is that models are only as good as their evals, which has always helped true.

38:52I think Greg Brockman tweeted this one, evals are all you need. Yeah, naturally. Let's talk about more core a little bit more. One of the maybe, not even maybe, I believe the data tells us it's the fastest growing company in history. Yeah. I want to understand what you did to make this happen. So let me just ask, what do you think are some of the core tenets of how you built Mark or that most contributed to being this successful? I think the most important thing is looking at the leading indicators in fast moving markets. Like I remember when I used to think, everyone in Venture talks about the why now.

39:34And I used to think about the why now of how from a product standpoint, less from a market standpoint, point of like, now we can automate the way that we review resumes or the way that we conduct interviews, etc. But ultimately, like, there is this legacy market that has all these incumbents and is relatively stagnant. What matters a ton is actually figuring out what are the new markets, the new pockets of demand that are changing very quickly where the wealthiest customers in the world are willing to pay whatever it takes to improve model capabilities. And how do we focus on the leading indicators of those markets to make sure that we have the best solution for the flagship customers in the market and optimize everything around that.

40:18And that's what I found has been most impactful in building the business. I think that's, maybe that's one thing is leading indicators in markets. If I had to choose another, it's customer obsession. Like we have had for the last, we're starting to like have a couple of product managers help out with go to market, but like for the last year and a half of the business we've had no one in sales and marketing. And so we're sort of like immature from a sales and marketing standpoint because we've focused 100 % of company resources on how do we build great products and experiences for our customers.

40:52You know, just getting word of mouth, the people that have worked with us at other businesses want to keep working with us, at leaning into creating those great experiences. And so that's where I spend all my time. And I think that some founders can get caught up in like how do they get really good at marketing before they've figured out the thing that really drives a lot of customer love and creates the six star experiences that you're used to building. I want to go back to that first point, which is like, okay, you found this pocket, maybe the biggest business opportunity in history. How did you first find, what was that moment of like, wait, this could be, this could be really big.

41:31So there's some crazy stories here. I remember we started the company, as I mentioned in January 2023, and then in August 2023, when I was still in college, one of our customers introduced us to the co -vanders of XAI over a Zoom call saying how we had these really smart in the software engineers that were great at math encoding. So we met them and we explained how the software engineers we had were really good at math encoding because they weren't distracted by all the humanities. They didn't have to study history in English and all these other things and they loved it, right? So they had us in two days later to the Tesla office and we met the entire XAI co -founding team except for Elon.

42:11Well, I was still a college student, right? And actually, I was just getting started at that point. And they were super excited about our focus on the quality of the experts. And so while they were still doing pre -training, they weren't ready for human data at the time, and we didn't start working with them at that point, we just knew from that point forward, before we even dropped out that the market was about to change radically, and we needed to be at the frontier of that. And so then, fast -forward a few months, one of the crowdsourcing players came to us and actually used our platform to hire over a thousand people, where this is very interesting experience because we started getting flooded with support tickets about how those people weren't getting paid.

42:55And we obviously felt horrible because we referred them to this opportunity. It was this like rap people company. And we realized that a lot of the incumbents were resting on their laurels with respect back to what was needed in the experiences they were creating for talent in their marketplaces to help improve models. And there was this opportunity to work directly with the labs in a way that kept the dignity of the experts in the marketplace, paid them extremely well and sort of cut out the middlemen. And so we started doing that in May of last year and then the rest of the history. Wow. Hundreds of millions of dollars in revenue since.

43:42So what I'm hearing here is you were very open to looking for poll. You saw some poll, you explored it. And then once you saw that there was something really meaningful that you just went deep on making that an incredible experience as amazing as possible. Exactly. I think like if I had to distill it into advice for founders, one thing I've realized is that I spent a lot of time trying like force product market fit. And in some ways, you should be persistent. You should have these feces that you have conviction about how the world will change. But sometimes you just need to sort of hear it from the market and know that it's there, the poll, to know the right places to focus.

44:24Because if it's extremely difficult to sell the marginal customer, you're not going to be able to grow a huge business. What you actually need to find is the customer that's surprisingly easy to sell into where you're going to be able to grow with them. You know that it's a large pain point. And so it's some combination of being stubborn with respect to your thesis around how the world will change, but also very open -minded with respect to exactly what form that takes and how the market's developing and how your company will fit into it. It's an amazing insight. In the moments you described, it felt like it was a combination of this XAI meeting feeling like, oh wow, they really, really want this thing that we sort of have.

45:03We're not doing an amazing job. And then it's a thousand people hired in the platform. It was those two moments that are like, wow. Exactly. And those happened even while we were a seed company, right? Well, so the first one was before we even raised at ESEAD Fund, we were totally bootstrapped because we bootstrapped the company to a million dollar revenue run rate. And I've always remained super capital -efficient. Like we've never burned money. We've owed, we're lifetime profitable. And then in, And we raised our seed round in September from General Catalyst, and it was the other experience after we raised our seed round where we really knew that there was an enormous amount of demand in this market, where we saw the volume, right?

45:41And we saw that the incumbents were sort of sleeping with respect to how the market was changing and the kinds of people that were needed to make that change happen. It's one thing to see this opportunity and search execute on it. it's another to actually succeed at this scale and consistently win. You guys have very specific values within the business. Talk about those. It feels like that's a big part of your success, too. It totally is. So I'll give the three and maybe a brief story associated with each of them. So the first one is having a can -do attitude, which ever it gets a little bit of a hard time for because it's sort of a funny saying.

46:18but we've always sat these ridiculously ambitious goals and then somehow the trajectory of the company forms around those goals. I remember when we were talking to benchmark before they let our series A, we were at 1 .5 million in run rate. And I said we'd be at 50 million in run rate by the end of the year. And they said we were absolutely insane, right? As anyone would. And plus there are minds two weeks we hit it, right? And then we've now well blown past, you know, the tracking to 500 million in run rate, which was initially our goal for this year. So setting these incredibly ambitious goals with respect to the revenue scale of the business, the caliber of experience, the fertile and all those dimensions are super important to first have a can -do attitude.

47:04The second thing is really high standards, which is who we hire and what we expect of them. We have an incredibly high hiring bar where we hire tons of former founders, people that have incredible experiences. We just hired a partner with Sundeepe Jane who joined us as president. He was previously the chief product officer and chief technology officer at Uber and joined our relatively small and the grand scheme of things company to help scale up all the processes where Uber is of course the largest labor marketplace in the world. So super high standards as of paramount importance. And then the third one that we really lean on significantly is intensity in that if you look at the early cultures of business of the legendary companies thinking of matter, Google, they have these incredible intense early stage cultures of people just moving heaven and earth and doing whatever it takes to push the frontier of model capabilities.

48:04And so very still very much output oriented of what do people achieve rather than and input oriented of the specific hours they work, but recognizing that it takes a lot to build a legendary business, and that's ultimately what we're optimizing for. I could see why this works. Can do attitude plus high standards, plus intensity. I could see how that leads to success. There's a lot of talk these days about this 6, 9, 9 culture working six days a week, 9 a .m. to 9 p .m. A lot of people are like, that's terrible. Why would you make people do that? But at the same time, I'm just constantly hearing this from the most successful AI companies.

48:40This is just the way it is to be successful. Things are moving so fast. This is an opportunity you'll never see again. Just talk about your thoughts on that. Yeah, well, to clarify, we've never mandated hours. It's more of a byproduct of people that care a lot, where we care a lot about the trajectory of the business. A lot of people come into the office and say, late, but you know, if they need to leave early and get the nerve with their kids or travel on the weekend. Of course, that's totally fine. For us, it's much more about finding people of a lot of ownership and are really bought in less so about the specific hours in the office, even though we found that oftentimes it's the people that are most bought at not always, but oftentimes it's the people that are most bought in that burn the midnight oil with us.

49:29When you say high standards, is there something you could share that gives us an example which in America is a lot of people are, think they have high standards and they don't. If you are very patient, there's always some trade -off between speed and quality when hiring. And I remember especially for our first 10 people, we were just so patient and disciplined about finding some of the best people in the world. Like half of them are, our second employee said, as an example, our second employee in the US said, was previously the head of growth at scale. You know, who joined us when we were a seed stage company, Daniel who joined us was previously scaled to consumer apps to over 100 ,000 users, and all sorts of just like extraordinary backgrounds of our first 10 hires.

50:19And I think that that initial talent density shaped so much of what the rest of the org looks like as you scaled it out. I know you also have this perspective that people talk about waiting to hire tilingly slowly, but it's actually not necessarily the right advice. Talk about that. It's painful because it's a double edged sword. Like on one hand, I'm thrilled that our first 10 people are like so phenomenal. And I think that that has paid dividends for the business. But on the other hand, I think that companies do get to the point where you just need to hire really fast. And there are some things where you need a lot of people to do them.

50:58And you need to recognize that there's going to be some variance associated with hiring, but moving quickly is the priority. And I think that in some ways we move too slowly with how we scaled out the team. And so the benefit is that everyone is extraordinary. We have this super high bar and we want to maintain that over time. But I think the downside is that, you know, while the company has grown incredibly quickly, we likely could have grown even faster if we had moved a little bit more quickly, with especially ramping from call like 10 to 100 people. Okay, I was gonna ask. So it sounds like the first 10, be very careful, take your time.

51:4210 to 100, maybe speed up a bit. But yes, though, I wouldn't say it's necessarily 10. It's determined by the point where you know it's really working. And I know that's still not like a bright line, but it's like once you know that there's so much more demand than you can handle, that's when you want to step on the gas and optimize for speed in a lot of ways. But I think especially until then, it's important to be patient, be disciplined, get the best people is always important, but speed becomes more important once you find the market opportunity, the market vacuum. You've started a couple companies in the past, much smaller scale.

52:24In this new role, SEO, this massive hyper growth company, what surprise you most about where you spend the time most or just what the role involves? Because a lot of people want to start companies these dream about being in your shoes, what do they maybe not understanding about where you a lot of your time goes? Yeah, it's actually not too surprising. Like the top two buckets are always working on hiring and time with customers. How do I really deeply understand what customers need and how we can support them and then how do I build the team and a lot of the processes around that? Of course, there's all the ad hoc things because I didn't expect of, you know, dealing with the people questions of how do we set up our levels and our comp bands and all of that, which you sort of learn as you scale up asness.

53:15But I think that the core places that I spend my time are in line with what I expected as well is what I love doing, which is very fortunate. So these two companies, you've started in the past, maybe share what they work, because they're fun, and then how do they help you be successful? And it's like, what's something that they taught you that helped you in your kernel? Yeah, so there's been like a dozen, but I'll choose my favorite two. Okay. When I was in eighth grade, I started donut dynasty where I saw that Safeway donuts were selling for $5 a dozen and I was amazed because I felt like it was an eighth grader.

53:52This was such an incredible deal. And so I started to bike down to Safeway by Safeway donuts for $5 a dozen and then go back to my middle school and then sell them for $2 each, running really good, margins of course. It sold out super quickly and so then I need to scale up. So I would pay my mom $20 to drive me in her mini van down to Safeway by 10 dozen donuts, go to my middle school, sell them all out. And then the school tried to shut me down and so because I was selling like food on school campus, which they didn't like, so they had me in the principles off as asking me to not do that. And then I moved my donut stand over 50 feet.

54:33So it was off school campus saying that they could no longer in police V. I remember we had competitors pop up where the competitors were charging. They bought these Chuck's donuts, which have anyone in the Bay Area knows are higher end donuts than Safeway donuts, but they have a higher cost basis. They cost a dollar per. And so I dropped my prices to $1 for two weeks to run them out of business before I knew what anti -conveterative practices were. And I'd hire all my friends, paying my friends in donuts, because they perceive the donuts as $2 each, where they could sell them throughout the school.

55:10I could have a lower cost basis on them. So I had all of these fun experiences and selling them. And then I could talk more about my high school business as well, which was more significant scale. But I think the takeaway from that was just, you can just do things. So many people have ideas, But the barrier to more companies being built, I think is just initiative and taking the steps to build the product or experience that customers want and investing the time and the ambition to scale that up. And so I think it was really getting reps of that that enabled me to realize that I should do it later on at a much larger scale.

55:51Amazing story. I love how wholesome that is versus like drugs. That's my mom was very worried. She was like, is there any pot of these donuts? I was like, no mom, I assure you, these are pure donuts. I love that you paid your mom $20 to do that. She was adamant she couldn't be a hand -out that she was taking her time to drive these. She needed to make a little bit of money off of it. We haggled over her title where eventually, she wanted to be head of global operations, which we thought very entertaining. I hope that's on our LinkedIn. No, yeah, maybe you shall have to add it. So you said that you'd start at dozen companies.

56:29Yeah. Wow. Well, a dozen projects. But I think it was that and then my AWS company were the two that I sort of scaled up. What's the story behind Mercor is? As the name. Mercor means marketplace and not an urge to buy sell trade. And we want to build the largest marketplace in the world. the marketplace for however, when it finds jobs. And that was really the draw to it. Okay. Maybe the last question. This is going back to earlier in discussion, because it's something I've been thinking about as we're talking. There's been this shift from data as kind of the fuel for it models. And now it's experts.

57:09Do you think there's a next step? Or is this just like, will take us to a GI super intelligence? I don't think it's necessarily changing from data. experts is more just the paradigm of realizing that labs need this close collaboration with experts to help understand what are the evals that they're building and how can they push the frontier. But I think it's very clear that evals are evergreen. So long as we want to improve models, we'll need experts to create evals for them and to create the post -training data for them to learn those capabilities. And of course there might be changes in the exact way that people do training with RL or otherwise, but they will always need an eVAL to measure what does success look like across every domain that they want to build.

58:01Okay, so then the building on that question that comes up a lot these days is, and I know we're talking about fun stuff, but I'm getting to serious stuff again. Scaling laws and just like progression of model intelligence. A lot of people are feeling like, I don't know, it's slowing down. We're not going to really get to super intelligence at this rate. What is your sense? I totally agree with that. Like, I don't think it's, I know there's been some executives at big labs that say we'll have super intelligence in three years, but I think the truth is that it's a longer road. And that's not to diminish from how extraordinary the models are.

58:35Like, I think we'll be able to automate the majority of knowledge work tasks in the next 10 years for sure, but that long road is paved with all of the evals that help to make those capabilities possible. And it's not going to be 10x more pre -training data that gets those capabilities. It's much more going to be all of the post -training data sets that are far more data efficient and thoughtful that help us get there. David Sacks, we did this interesting point that the situation we're in now is almost the best case scenario where AI is not in this fast takeoff to super intelligence. There's a lot of competitors kind of keeping each other in check.

59:17Models are already very valuable and only getting valuable, more valuable, but there's not just this like winner super intelligence taking over the world situation. Yeah, I think that's true. I think a lot of the super intelligence fear mongering is probably overrated, but at the same time, a lot of people's framing around that is even if there is a five to 10 % chance of this P -tume, then we should be careful, which seems logical. But I think that it's going to be an extraordinary 10 years for all of Silicon Valley and all of the world as this technology is able to create abundance and giving everyone better medical treatment, you know, the best access to legal recommendations and the ability to build great products more than we've ever seen before.

1:00:06And education feels like is transforming really, right? Like I even have felt bits of this over the last 10 years where like I remember ever my parents would give me a hard time for not going to classes in college and I'd be like well there's way better lectures on YouTube why not just listen there but I can only imagine as the models get extremely good at conveying information better than the best professor what that'll be right and in access to all sorts of information to better forward humanity and upskill everyone. So I'll use that as a segue to a final question. I'm going to take us to AI corner, which is our current segment on the podcast.

1:00:48What's some way that you personally use AI to do better work to help you in life? Well, let's say I use it a lot to write documents as you would expect. I also talked to get advice on problems. Like I find it helpful to just reason through, almost as a thought partner. Because yeah, I don't know. I find I think better sometimes when I'm talking something through, but I can't talk through everything with colleagues or people around me. And so this is like ChatGPT voice mode mostly, or something else. I like ChatGPT voice better a lot. There's definitely room for improvement, but I'm very excited about the future of voice.

1:01:25Let me show you something I built actually that I wasn't planning to talk about this but there's this guy Eric Antenau who Who's been recommended by a lot of people to get them on this podcast. He's this creative Product person that's kind of under the radar now. He's a Facebook for long time He built this project called parrot GPT, which is you put you basically put jet GPT into a stuffed animal to talk to Built a little wise owl. I don't have it on right now But basically you sow in a little speaker right here and you put a little magnet underneath and you could put on your shoulder and then just talk to it.

1:01:56Wow, I love it. I'll have to get one of those. You don't have to. Because I have like a some of the voice assistants in my apartment, but I really want a chat GPT voice assistant. And so I'm excited for. I was just thinking about that. I like, yeah, just come on. Why can't we have a chat GPT voice just sitting around listening to us all the time? And you can on your phone because it goes to sleep and it's like, hello, what? Yeah, exactly. Yeah. Yeah. So it's kind of what this is trying to be. We'll think there's a Kickstarter. He started that welding too that you can help you. Well, that's really easy.

1:02:25Brendan, is there anything else that you wanted to share or touch on or maybe leave listeners with before we get to a very exciting waiting room? Time to the point around initiative and that you can just do it. Do things. I encourage everyone, especially with AI and it being so much easier to build. Just take the initiative to go out and build products and and talk with customers and take that leap of faith because I think that that is in so many ways the largest barrier to more innovation, the economy in any way that we can support that. Yeah, there's so many people, just nothing, let's not bash the podcast, but just listen to podcasts, read posts, just keep reading and listening and don't do anything with that information.

1:03:09And there's never been an easier time to actually build stuff and try stuff. So definitely take that advice. just you can do things. You could move your donut stand 50 feet and get out of their jurisdiction. Okay, Brennan with that we've reached a very exciting lightning round. I've got five questions for you. Are you ready? All set. What are two or three books that you find yourself recommending most to other people? Let's see I would say in order high output management is a phenomenal book on running companies. Second is zero to one which of course is a classic and then third is where I just find it to be a really inspirational story.

1:03:46What is a recent movie or TV show really enjoyed? I really liked Oppenheimer. My favorite TV show of all time is Suits. So I know not recent, but if I had to choose a recent one, probably Oppenheimer. Very cool. Suits first time, if someone's mentioned that. Favorite product, you recently discovered that. You really love. I love using Codex, like the new version. I know it's sort of new in terms of version. Yeah, I think it's incredible and just a huge improvement. So yeah. Do you have a life motto that you find yourself coming back to sharing with folks finding useful and worker in life? I think it's, you can just do stuff.

1:04:27You know, what we were talking about earlier. Take the leap of faith. I thought you were going to say can do, which is in your Twitter profile. I did do as well. Yeah. Two great ones. Final question. So we're chatting before this about things that we could talk about and you share this interesting thing that you haven't shared anywhere else, which is that you're dyslexic. Why don't you share that with folks and just how do you get around that having built the fastest growing company in history? I don't hide it at all. I think a lot of my colleagues know. I think on one hand, it definitely makes it difficult to go through a thousand emails a day or read every document that I'm supposed to.

1:05:08On the other hand, I feel like it helps me to think a little bit differently, to be more creative, and perhaps see the ways that markets are changing, that not everyone sees. And so it's turned out okay so far. And so, you know, I try to, I think one thing it's helped me realize from a management standpoint is that we focus much more on how we can leverage people's strengths rather than helping to improve weaknesses. Because there's some things that I'm not great at and I'll never be the best in the world at. And there's others that I can hopefully refine and strive to be. That's such a also recurring theme on this podcast I asked if just focusing on strengths and not focusing over all your focus on weaknesses.

1:05:53Bren, and this was incredible. I learned so much. I have a billion more questions, but you got shit to do. Two final questions. What should people know about what you're doing and roles you're hiring for? And then how can listeners be useful to you? Absolutely. We're hiring a ton across the board on our team. We're hiring strategic project leads on our operations team, software engineers and our engineering team, as well as researchers. and so please go to mccorp .com and we would love to work with you and that's the largest way that you can help us. Share it with your friends as well. Over half of people in our marketplace come from referrals because we have a platform of people that love us and so any jobs that you want to apply to or send your friends to, we'd love to have you.

1:06:37Brendan, thank you so much for joining me. Thank you for having me. Bye everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also please consider giving us a rating or a leaving review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at Lenny's Podcast .com. See you in the next episode.