# Mercor Raises $750M at $20B Valuation | CEO Brendan Foody

Markdown copy of https://realalpha.app/v/xzA6lYBzmzQ/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

Brendan Foody argues that human expertise will remain essential for evaluating and training AI, even as models become more capable.

- Show: Sourcery
- Hosts: [Molly O'Shea](https://realalpha.app/people/molly-oshea/)
- Guest: [Brendan Foody](https://realalpha.app/builders/brendan-foody/) is co-founder and CEO of Mercor.
- Published: Nov 10, 2026
- Length: 54 min

## The best minutes

- [43:35](https://realalpha.app/v/xzA6lYBzmzQ/?t=2615) [Enterprise AI adoption follows inference spending](https://realalpha.app/s/enterprise-ai-adoption-follows-inference-spending/) (6 min)
- [12:09](https://realalpha.app/v/xzA6lYBzmzQ/?t=729) [Scalable interviews help find rare high-quality experts](https://realalpha.app/s/scalable-interviews-help-find-rare-high-quality-experts/) (3 min)
- [5:30](https://realalpha.app/v/xzA6lYBzmzQ/?t=330) [Mercor sells training environments, not just expert access](https://realalpha.app/s/mercor-sells-training-environments-not-just-expert-access/) (3 min)

## Also worth watching

- [36:55](https://realalpha.app/v/xzA6lYBzmzQ/?t=2215) [AI companies need different moats at each layer](https://realalpha.app/s/ai-companies-need-different-moats-at-each-layer/) (4 min)
- [2:19](https://realalpha.app/v/xzA6lYBzmzQ/?t=139) [Partner payment failures pushed Mercor to own expert payouts](https://realalpha.app/s/partner-payment-failures-pushed-mercor-to-own-expert-payouts/) (3 min)
- [28:28](https://realalpha.app/v/xzA6lYBzmzQ/?t=1708) [DeepTune filled Mercor's gap in simulated work apps](https://realalpha.app/s/deeptune-filled-mercors-gap-in-simulated-work-apps/) (3 min)
- [19:53](https://realalpha.app/v/xzA6lYBzmzQ/?t=1193) [Seed-stage customer failures shaped Foody's operating cadence](https://realalpha.app/s/seed-stage-customer-failures-shaped-foodys-operating-cadence/) (4 min)
- [15:28](https://realalpha.app/v/xzA6lYBzmzQ/?t=928) [Mercor treats frontier research as a signal for wider adoption](https://realalpha.app/s/mercor-treats-frontier-research-as-a-signal-for-wider/) (3 min)

Skip the sponsor reads, the entrepreneur role-model and tax-policy discussion, and the closing daily routine and hiring chat.

## Full analysis: Brendan Foody says $10 million inference spend signals evals

- Foody says Mercor generally finds companies in the $10 million to $100 million inference band want roughly 10% for evaluations.
- That is a customer pattern, not a published benchmark, and the wide band conceals important variation.
- Operators should build task-level evals and compare spending stages with their own workloads, not copy a quota.

Its 6 sections, what it means for you and what to watch are on [the page](https://realalpha.app/v/xzA6lYBzmzQ/#analysis), free with an account:

- [46:34](https://realalpha.app/v/xzA6lYBzmzQ/?t=2794) Inference spending tracks when buyers begin funding evals
- [5:50](https://realalpha.app/v/xzA6lYBzmzQ/?t=350) Mercor's product combines people, simulated apps, and scored tasks
- [9:11](https://realalpha.app/v/xzA6lYBzmzQ/?t=551) Human feedback remains valuable where models still fail
- [12:27](https://realalpha.app/v/xzA6lYBzmzQ/?t=747) Interview and payment systems underpin environment delivery
- [39:38](https://realalpha.app/v/xzA6lYBzmzQ/?t=2378) Foody says custom models could distinguish inference providers
- [43:44](https://realalpha.app/v/xzA6lYBzmzQ/?t=2624) Foody expects enterprises to train around proprietary data

## Summary

Brendan Foody is Mercor’s co-founder and CEO. Founded in 2023 by college dropouts, Mercor has expanded from AI-powered recruiting into building data, evaluations and reinforcement-learning environments for AI training.

Foody argues that work will shift from repetitive tasks to people training and evaluating agents, and that human expertise remains necessary wherever models still need feedback. He describes Mercor as a platform that combines an expert network, data tools and AI project managers to deliver reinforcement-learning environments, not simply match workers with jobs. He expects Fortune 500 companies to build their own models and agents around proprietary data, with expert-created evaluations and tasks guiding the process. For AI businesses, he sees brand and data as durable application-layer advantages, and customized models as a stronger moat for inference providers than reselling compute.

### Minute by minute

- [0:30](https://realalpha.app/v/xzA6lYBzmzQ/?t=30) **Work shifts toward training agents.** Foody says Mercor has grown more than fivefold on a mid-nine-figure base since he and O’Shea last spoke. He argues that as agents take over repetitive work, people will increasingly train them, create evaluations and update skills files; building the technology for that shift is Mercor’s long-term ambition.
- [2:19](https://realalpha.app/v/xzA6lYBzmzQ/?t=139) **Mercor combines three products.** Foody describes a talent platform with more than 5 million people, Studio for building data and reinforcement-learning environments, and an AI project manager that can hire experts and assess data quality. He says Mercor pays out more than $5 million a day, uses Stripe Connect and invested in payments after workers supplied through another provider reported not being paid.
- [5:30](https://realalpha.app/v/xzA6lYBzmzQ/?t=330) **The product is the environment.** Foody says Mercor is not a marketplace that simply matches experts to jobs. Its customers receive reinforcement-learning environments made up of app replicas, the content inside them and tasks with prompts and grading rubrics; experts help populate the worlds and tasks.
- [8:43](https://realalpha.app/v/xzA6lYBzmzQ/?t=523) **Human expertise remains necessary.** Foody argues that demand for human-created training data grows as AI makes experts more productive. He says models need a reliable way to identify mistakes and receive feedback, especially in domains where there is no simple answer key, and that humans will remain useful as long as they can do things models cannot.
- [12:09](https://realalpha.app/v/xzA6lYBzmzQ/?t=729) **Referrals and interviews find experts.** Foody says 70% of experts come through referrals and that Mercor has completed more than 7 million interviews. The company built an AI interviewer in 2023 to assess candidates against rubrics and gather information such as their projects. He contrasts paying out about $1 million a year two years earlier with paying more than $5 million a day now.
- [15:28](https://realalpha.app/v/xzA6lYBzmzQ/?t=928) **Culture anchors category leadership.** Foody credits Mercor’s high hiring standards, long-term thinking and customer focus for its position in the market. He says the team works backward from what the world may look like in five years and treats frontier AI research as a signal of what will later reach companies and the wider economy.
- [17:59](https://realalpha.app/v/xzA6lYBzmzQ/?t=1079) **Foody favors first-principles thinking.** Foody says Mercor works with urgency but warns that copying competitors can make a company second place. He describes losing early customers, a security incident and other setbacks, and says leaders should surface problems when things are going well and rally the team in difficult periods. He says the company focuses on customer and expert outcomes rather than weekly revenue.
- [23:14](https://realalpha.app/v/xzA6lYBzmzQ/?t=1394) **Foody draws lessons from founders.** Foody says Elon Musk’s mission focus and Jeff Bezos’s long-term thinking inspire him; he also admires Sam Altman and Dario Amodei. He describes a college paper arguing that removing taxes for the bottom half of Americans, whom he said earn under $70,000 a year and provide 3% of federal tax revenue, could encourage job creation.
- [28:28](https://realalpha.app/v/xzA6lYBzmzQ/?t=1708) **Deeptune fills an apps gap.** Foody says Mercor acquired Deeptune to expand its ability to build app replicas for reinforcement-learning environments. A customer had recommended Deeptune as an apps partner, and Foody says the teams’ complementary capabilities and aligned cultures made the acquisition a strong fit. He calls it Mercor’s second acquisition and says the company is exploring other categories.
- [31:19](https://realalpha.app/v/xzA6lYBzmzQ/?t=1879) **Foody says mission outweighs offers.** Foody says he and co-founder Adarsh Suri have had chances to walk away with substantial cash, but prefer to keep working on a problem they consider important. Discussing Scale, he says its core team moved to Meta while much of the company remained at Scale, which then focused most of its business on Meta; he says Mercor would not take that path.
- [34:29](https://realalpha.app/v/xzA6lYBzmzQ/?t=2069) **Brand and data can protect apps.** Foody says Mercor helps Harvey source experts and shape data for legal benchmarks and post-training. He argues that application companies need frontier capabilities in their domains, and that brand and data can provide durable advantages as software layers become easier to build. For inference providers, he says custom models create more switching costs than reselling GPUs.
- [41:28](https://realalpha.app/v/xzA6lYBzmzQ/?t=2488) **Data remains underpriced.** Foody says investors have underestimated demand for data because they expected superintelligence to arrive within a few years. He argues that making experts more productive has expanded demand for model improvement, and that human input remains necessary while people can do things AI cannot. He expects data’s importance to become more widely accepted as enterprises evaluate agents.
- [43:35](https://realalpha.app/v/xzA6lYBzmzQ/?t=2615) **Enterprises will build their own intelligence.** Foody expects every Fortune 500 company to act like a Neolab, using its own data and expertise to train models that preserve its competitive advantage. He says companies spending $10 million to $100 million on inference should devote roughly 10% of that budget to evaluations; above $100 million, post-training can make economic sense. He advises building clear evaluation tasks before scaling training.
- [49:49](https://realalpha.app/v/xzA6lYBzmzQ/?t=2989) **Foody expects enterprise growth, not an IPO.** Foody says Mercor will not go public in the next 12 months, though an IPO is on its radar, and says enterprise adoption is what he most wants to pursue. He describes dividing his time roughly equally among hiring, customers, and research and product, while relying on the leadership team and agents to help the organization scale.

## Transcript

Sourcery, hosted by Molly O'Shea, with Brendan Foody.

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[0:00](https://realalpha.app/v/xzA6lYBzmzQ/?t=0) Darsh and I have had opportunities where we could walk away with a lot of cash, but that's not what motivates us. Like we'd rather work 100 hours a week to solve a really important problem in the world. So now if we're paying out, call $5 million a day, two years ago, 24 months ago, we were paying out a million dollars a year. Do you have any chips on your shoulder? I would say I always have like varying chips on my shoulder. We have no interest in being second place in a market. brendan welcome sorcery excited to be here thanks for having me molly i mean we were just talking about this but we first went on a walk probably over a year ago and how much have you grown since we've grown i think this was in september of last year so we've grown by more than 5x on yeah on a mid-nine figure base that's crazy it's been pretty wild yeah so how are you going and become a trillion dollar company? Great question. Well, I think it starts with working backwards from how will humans contribute to the economy? Because right now, most work is relatively repetitive, where people will repetitively respond to customer support tickets or repetitively redline a legal contract or repetitively edit a podcast and all the different

[1:24](https://realalpha.app/v/xzA6lYBzmzQ/?t=84) formats that they're used to, when in the future, it's going to much more look like training an agent one time, how to automate these repetitive tasks. And so what we see emerging within enterprises is the shift away from all these repetitive tasks and how work has been done in the past and moving towards people contributing to the economy through creating evals, updating skills files, training agents how to automate all this repetitive work. And so if we subscribe to the fact that the whole way that humans contribute to the economy is transforming radically, clearly leading that industry, defining that industry, and building all of the technology around how that work happens creates the opportunity to build a legendary category-defining company. And that's what we've always been focused on from day one is just solving this problem of how do humans fit into the economy as that is evolving ever so quickly. So let's break that down. Let's break down how Mercore is operationally structured. So I know you want to talk about reinforcement learning environments a bit, also known as RL environments. So if you break that down, how the products of Mercore work, how does it all fit in? Yeah. So there's broadly three main

[2:45](https://realalpha.app/v/xzA6lYBzmzQ/?t=165) products that we have to produce a given data type. The first product is what we built initially, our talent platform, where we have over 5 million people actively looking for work, pay out over $4 million a day. 70% of the people come from referrals. So there's a lot of network effects associated with how we can mobilize software engineers, doctors, lawyers, etc. for any specific type of data that we want to create in the economy. From there, we onboard those experts to Studio, our data platform, where they have an AI copilot and all of the Agenda QC and automated capabilities for how they would create an RL environment, populating all the context that mirrors what they would do in their job, all the tasks, all the verifiers, etc. And then the third platform is our AI project manager, where we basically automate all of the manual work associated with how we would manage all the experts on these projects. So similar to how we We would have project managers setting KPIs for people, saying who's doing a good job, who's doing a bad job, giving them feedback.

[3:52](https://realalpha.app/v/xzA6lYBzmzQ/?t=232) We now have agents that are autonomously running these projects, hiring people, determining the data quality and all that. ultimately being accountable to the customer outcomes. So you're doing $5 million in payouts a day? Yeah, not well over that, yeah. How does that work? It uses Stripe Connect as the back end. I love how you go right into the infrastructure answer. The team is spectacular. I mean, it's been incredible because one of the things we realized early on is we were working with another player in the space that was having a lot of payout issues very publicly where we were providing talent to them.

[4:35](https://realalpha.app/v/xzA6lYBzmzQ/?t=275) And we got flooded with support tickets that we'd hired over 1,000 people for them, people often in third world countries. And they were not getting paid and struggling to put food on the table for their families as literally we were receiving many support tickets about that.

[4:52](https://realalpha.app/v/xzA6lYBzmzQ/?t=292) **Brendan Foody:** And so really, that was one of the large catalysts for we need to have a direct relationship with the labs. We need to be able to be accountable for the experience of the experts on our platform and making sure that they get paid on time. And we need to over invest in how do we have a world class payments team that never makes mistakes and always make sure that people are paid out.

[5:14](https://realalpha.app/v/xzA6lYBzmzQ/?t=314) And so to date, we've, you know, paid out just about a billion dollars historically, which is on a pretty steep ramp. And we haven't had any major issues or of that sort. And so it's been an incredible process of how we scale the payments team. Actually, I've written some blogs about this because there's a lot of interesting technical problems in there. Wow. I mean, for some people who don't understand the business model, going out to the broader umbrella of Mercore, you're not focused on GMV. It's not a marketplace. So how does the business model fit?

[5:50](https://realalpha.app/v/xzA6lYBzmzQ/?t=350) **Brendan Foody:** Yeah, so I think the largest misconception about the business is that people think about us as a marketplace like Uber, where we would just match experts with a job and then take a fee on that, when actually the experts are one component of the data sets, the RL environments that we deliver to the labs.

[6:11](https://realalpha.app/v/xzA6lYBzmzQ/?t=371) And so an RL environment includes three parts. The first part is all of the apps, which are like clones of Google Workspace and other popular applications that people would want to teach agents how to use within the economy. The second is the worlds, which is all the content within those apps. So think the messages, the slides, the docs, the sheets, etc. And then the third part is the tasks, which are prompts and rubrics, similar to how a professor would create a rubric to grade an essay, where the model can attempt the problem and then get a grade on how well it did that it's able to learn from.

[6:49](https://realalpha.app/v/xzA6lYBzmzQ/?t=409) And the barrier to automating the entire economy is how do we cover the full distribution of all of the RL environments that mirror everything in the distribution of the economy? So cover all of the different apps people use, all of the different worlds, and all of the different tasks. And so ultimately, these like RL environments is the end data that we deliver to our customers. And the experts that work to populate the worlds and the tasks are one ingredient, one moat that has been a foundation for how we do that effectively and scalably. Obviously, a lot of this is predicated on models, but how has the advancement of agents coming into the entire AI system changed and maybe increased what you're doing?

[7:35](https://realalpha.app/v/xzA6lYBzmzQ/?t=455) Yeah, I think that one of the large benefits is that agents have made it much more productive for someone to spend time creating data and training an agent. And so as an example, like, say, a hour of someone's time would previously get x amount of product model improvement. Now, an hour of their time might get three to five X that because they're able to use an AI co-pilot to make them more productive and how they build out all of these data sets. But ultimately what's happened as we've made the experts more productive is that it's been this case of Javon's paradox where the demand just expanded exponentially because ultimately like 99% of knowledge still lives in people's heads. And if we just make it more effective and productive to have humans teaching models, that just increases the amount that people teach models throughout the economy, because it ultimately ties back to the principle I mentioned at the beginning, which is that it doesn't make sense for people to do repetitive work. They should just train the model how to do it. And that's a far more scalable and productive way to contribute to the economy.

[8:49](https://realalpha.app/v/xzA6lYBzmzQ/?t=529) So one kind of comment on this category in general is that it's a non-durable category, that training models using all this knowledge work, all this kind of stuff that goes into it is only going to last for so long. Do you see that? Obviously, you don't see that happening. But what are your arguments against that? I think it ultimately comes down to your superintelligence timelines, because the market for humans teaching models how to do something is bound by the amount of things that humans can do that models can't do across either the digital world or the physical world. And so long as there are things in the economy that a human can do, and it might be something that is joining a Google Meet and having a persuasive conversation or running a scientific experiment in a wet lab or repairing a car, whatever it is, we need humans that measure that frontier.

[9:47](https://realalpha.app/v/xzA6lYBzmzQ/?t=587) The primary way that models improve is that they need some stasis point for what is the place where they're making a mistake, how do they measure that, and then how do they use that as a reward that they can learn from. it's sort of analogous to the way humans learn of imagine if you are in an english class learning how to write better essays and the teacher just says that you need to like grade all of your own essays and like no one else can give you feedback uh you just got to grade your own essays right it's like pretty difficult to measure what is beyond the frontier of your own capabilities and that's why we have feedback there might be some domains where you can learn based on a clear signal. Like math is a good example because there's calculators where you can sort of check your work. But in the vast majority of domains in the economy, the way that we learn and the way that we improve is getting the feedback from our teacher, our professor, our managers, our colleagues, etc. And that's very similar to how models learn and how most of the top researchers of these labs expect that they'll continue to learn for decades to come. This episode is brought to you by brex my favorite you become what you spend on and i refuse to spend my time on work

[11:01](https://realalpha.app/v/xzA6lYBzmzQ/?t=661) that shouldn't exist expense reports receipt chasing and manual closes the companies building what's next from versell OpenAI anthropic granola and deepgram all made the same call they all run on brex brex is the intelligent finance platform that combines cards expenses and banking into a single stack with agentic finance built in ai agents that handle expenses automatically enforce policy before spend happens and close your books in minutes that's why sorcery runs on brex so i can spend time on building and not busy work it's time to get brex af learn more at brex.com slash sorcery that's b-r-e-x.com slash s-o-u-r-c-e-r-y bye from handshake to signature faster harvey the ai platform built for legal work is trusted by 80 percent of the amlaw 100 and for 20% of the Fortune 500.

[12:02](https://realalpha.app/v/xzA6lYBzmzQ/?t=722) Learn more today at harvey.ai. That's harvey.ai. How good is AI at real professional work? Merkur's AI Productivity Index, or APEX, tracks it with every major Frontier model release. Built with partners like Harvey and Cognition, see where the Frontier actually stands at merkur.com slash APEX. I have to ask, how do you find these experts and how do you evaluate them? So 70% of them come in through referrals. There's also other channels that we use, but that's the one we're most focused on because we find that people that are referred tend to be the highest quality. And then evaluating them is a really cool process.

[12:42](https://realalpha.app/v/xzA6lYBzmzQ/?t=762) And this was actually one of our first large breakthroughs because we built the first AI interviewer when Adarsh Suri and I were in college in April of 2023, right after ChatGPT came out. The first one had like seven or eight second latency. It would make an API call to GPT-4 and do some preprocessing based on the resume, make an API call, say the question, ask people, and then evaluate the interviews according to a rubric or a set of criteria to see how well people do.

[13:20](https://realalpha.app/v/xzA6lYBzmzQ/?t=800) **Brendan Foody:** And the realization we had as we scaled that up is that so much of the challenges in labor matching just come down to scalable data collection.

[13:33](https://realalpha.app/v/xzA6lYBzmzQ/?t=813) And an analog for that is that if you're hiring Stanford AI PhDs, you can probably meet with five people and like one of them is going to be pretty good. versus if you're hiring software engineers from a remote place in India, you need to meet with sometimes hundreds or a thousand people to find like the really world-class engineers. But they are there and many of them are on our team. And so as we were able to scale up the amount of interviews that we did to solve these differences in talent density, that was really one of the large drivers of how do we get spectacular people? and also analyzing all the different filters people might not be considering of what are who referred them of what is their what are their personal projects that they do on github on figma or whatever it is all of these things getting pulled into context then analyze so we now do over five or we do we've done over seven million interviews and it's been pretty crazy to see things scale up as we um have now created the fastest growing job category in history it's crazy yeah literally so fast it's very surreal yeah i mean i for as a frame of reference so now if we're paying out call five million dollars a day as of two years ago 24 months ago

[14:58](https://realalpha.app/v/xzA6lYBzmzQ/?t=898) we were paying out a million dollars a year so it's like um i i think that many people at the company don't fully realize just that like that that's never happened before um and that it's this very special opportunity where we found this giant market before anyone else and we've become the category leaders and how we um create this really important new category yeah that's amazing i mean i have so many questions off of that um one of the questions that i was like super curious about just to deviate for a second is like you look at your team you're all early 20s not all of us but a lot of our early people yeah early early 20s right your dropouts of college you've been able to grasp and really hold on to one of the fastest, if not the fastest growing new category in AI and in technology in years. And meanwhile, like you have very large competitors get bought out by like meta you have other ones that are popping up like every other month and somehow reaching like a billion in revenue and yet like and you guys like you know you overcome challenges like the data leak and that kind of thing but still you're like number one in the market so what is it about your culture and your founding team that

[16:21](https://realalpha.app/v/xzA6lYBzmzQ/?t=981) you guys are able to continue to persevere and win I would say there's probably three values that we have that are explicit values in the company, but they're really operationalized that have been super impactful. The first one is high standards, just having like a really high hiring bar and making sure that we don't compromise that has been a huge differentiator relative to everyone else in the company and the quality of the people on the team is unparalleled. The second thing is a value we call time travelers, which is about being super like long-term orientative, just like living in the future of what is the world going to look like in five years and how do we work backwards from there? And that was how we sort of found the market before all of these other talent networks. And we just have always understood the frontier

[17:16](https://realalpha.app/v/xzA6lYBzmzQ/?t=1036) **Brendan Foody:** of the research super well and how what's happening at the frontier is a leading indicator of what happens at the AI native companies and application layer companies is a leading indicator

[17:28](https://realalpha.app/v/xzA6lYBzmzQ/?t=1048) of what happens in the global economy. And just being very thoughtful about how things systematically flow through these adoption curves has been really important. And then the third thing is customer obsession. We are an incredibly hardworking, customer-obsessed company where we move mountains for our customers because they build the most important technology on earth. And we are extremely committed to the mission of how we help them to do that. And I would say those three things have been the most formative as we've developed market leadership. Would you say you have clean fuel or at this point, do you have a little bit of redemption fuel?

[18:12](https://realalpha.app/v/xzA6lYBzmzQ/?t=1092) What was the first one? Clean fuel? Clean fuel. How do you define clean fuel? like okay dylan field explained this to me before their acquisition by i forget was it adobe got scrapped like he was just running on pure clean fuel like nothing put a chip on his shoulder then that happened got a major chip on his shoulder and he was like no i'm gonna fucking win and like do it with you know gumption do you run off of clean fuel or what do you have any chips on your shoulder i would say i always have like varying chips on my shoulder i like we always have had a maniacal sense of urgency where um the company is super intense like if um people aren't executing um we keep a really high bar and we move super quickly um and so we always want to be thoughtful about what are competitors doing? How can we move faster? But the thing I would caveat is that I think many companies fall too much into the trap of looking at what their competitors do, which becomes mimetic. Like it's a really good way to be second place in a market, right? And we have no interest in being second place in a market, right? Like we have an interest in defining important categories.

[19:32](https://realalpha.app/v/xzA6lYBzmzQ/?t=1172) And I think that just requires more of this like first principles thinking, more motivation from being a time traveler of working backwards from what will the world look like in five to 10 years, less so focus on competitors. So maybe that's related to your question of what motivates me, but I think all of my co-founders and our early team are really just excited. about how do we build a legendary company that solves this problem of how do humans fit into the economy over the next decade? What were the biggest challenges that you had to overcome? There's been so many. I mean, I think that a lot of people who haven't actually founded companies don't know the feeling of just getting punched in the face over and over again. And, you know, We've had so many cases where we've lost our seed stage customers early on because the initial business for the first roughly year and a half was all seed stage companies.

[20:39](https://realalpha.app/v/xzA6lYBzmzQ/?t=1239) **Brendan Foody:** And so, you know, 80 percent of them go out of business within 12 months.

[20:43](https://realalpha.app/v/xzA6lYBzmzQ/?t=1243) And it was sort of fighting that constant tension. Some of them became large companies like Cognition. um but that was definitely always a little bit painful um there have been uh cases like the security incident there's been cases where uh we lose a early member of the team like there's so many things where you just get punched in the face and i think founders gain an appreciation that like chaos is the norm and you just have to get used to uh chewing glass and staring to the best as elon says because ultimately it's the impact on the other side of that five to ten year journey that is motivating and makes it all worth it how do you manage that for your team it's really interesting like we i think we so i saw you at the upfront summit not too long ago and i was interviewing Alfred Lin on stage and something that he's talked about a couple of times, even through our interviews, is they like to be shock absorbers for their founders and their companies. So do you like to be a shock absorber for your employees? How do you help direct them and let them know what's going on? I agree with this. I think what Alfred was saying is super accurate that when everyone feels like they're on top of the world, that's when you need to be

[22:07](https://realalpha.app/v/xzA6lYBzmzQ/?t=1327) cracking down on what are all the things going wrong. And when things are not going well and it's one of the rough patches, that's when you want to be the cheerleader that's telling everyone how good a job they're doing and helping to rally the company. I think that that is certainly super important. The other thing that relates to this is just driving the long-term orientation. like even on our executive team someone like uh put up like a weekly revenue graph and i was like no we i don't want the company like looking at this right it's like the company should be looking at how happy are our customers how happy are our experts um how well utilized our experts all these different things that like actually drive creating value in the world um and how that compounds Because I think that that is one of, as I've learned and been fortunate to get insights from people who've been at a lot of the legendary companies early on, this long-term orientation is one of the most important characteristics of the people and the companies that accomplish extraordinary things.

[23:20](https://realalpha.app/v/xzA6lYBzmzQ/?t=1400) Who are some people that you look up to? Well, I do think Elon is the greatest entrepreneur by results. I wish he didn't get into politics so much, but I think luckily he's out of it. Luckily, he's out of it. Yeah. I heard from someone that he said that the government is the one institution that wasn't fixable. So whatever that tells us. But I think that one thing I admire about him, not only the repeated track record, which is obvious, but just the mission orientation of really enduring so much pain because he cares about building these companies that shape the trajectory of a really important market and define that category.

[24:04](https://realalpha.app/v/xzA6lYBzmzQ/?t=1444) So I think he's been exceptional. Jeff Bezos is one person we talked about where he really embodies this long-term thinking more so than almost anyone. And being so principled about what are the things that matter at the very beginning of Amazon and how those map on to the product strategy decades later is a lot more challenging than it looks. And I've been amazed by that, the ability of him to create an entrepreneurial culture. There's also a lot of founders now that are clearly extraordinary. Like Sam and Dario are both spectacular. I often feel bad that both of them will always get so much hate on. Right. It's like there's always this constant cycle of like you're on top of the world and like you're the America's worst enemy. And then all of a sudden you're back. And so I feel bad about that. But those are definitely some of the entrepreneurs that inspire me the most. I want to talk about Jeff Bezos. So you wrote a paper that what came first? So I wrote I wasn't a published paper, but the joke I made on Twitter was that I remember I was in this class my freshman year of college. And I had to write about how can the government pass policies that better line incentives in the economy. And the thesis of my research paper

[25:38](https://realalpha.app/v/xzA6lYBzmzQ/?t=1538) was that the largest positive externality in the economy is creating jobs. And the largest way that the government collects tax revenue is by taxing jobs. And for people that like haven't studied economics, if you tax like the employee, that is functionally the same as just taxing the company, right? It's like a direct disincentive to job creation. And maybe this sort of makes sense for people that are making a billion dollars a year. Like I don't care or I don't make that, but I don't care that much if I don't have if I have a bigger tax bill. But the fact that we do this for the bottom half of Americans that are making under 70K a year is like so deeply irrational.

[26:21](https://realalpha.app/v/xzA6lYBzmzQ/?t=1581) The bottom half of Americans constitute 3% of federal government tax revenue. And yet it's a giant burden on them that they both economic as well as all of the time associated with doing this. And so the thesis was just eliminate taxes for the bottom half of Americans because that will incentivize more job creation, especially at the low end. And then I saw my hero, Jeff bezos posting about this on x and so i retweeted him and then he reposted me uh which was i was on top of the world about and so because you know it's like this weird feeling when i grew up like listening to all his podcasts and admiring him so much um and to finally like at least get some engagement i've never i've never actually met him in person so you haven't i haven't working on it Okay, well, we'll manifest that one.

[27:11](https://realalpha.app/v/xzA6lYBzmzQ/?t=1631) Yeah, we'll have to make it happen. That's exciting. AI needs more than chips. It needs power, land, and infrastructure. Zone develops next-generation data center campuses, partnering with AI companies, site developers, and technology leaders to bring compute online faster and at scale. Zone is building the foundation of the AI frontier. Visit zonefrontier.com to learn more. That's zonefrontier.com to learn more. ai agents create your own custom agent with a simple prompt agents can monitor markets execute trading strategies and manage your cash across stocks bonds options crypto and more want to buy at market close and sell the next day's open just tell your agent automate your investing at public.com founders ship faster on deal set up payroll for any country in minutes hire anyone anywhere get Visa's handled fast and get back to building. Visit deel.com slash sorcery. That's deel.com slash S-O-U-R-C-E-R-Y. So let's talk about the state of the business today. So you recently had some big news. You acquired DeepTune. Yeah. Why did you do that? So it really ties back to what are the critical ingredients of the RL environments we need to automate everything in the economy, where the three parts I mentioned are, first of all, the apps, second of all, the worlds,

[28:59](https://realalpha.app/v/xzA6lYBzmzQ/?t=1739) and third of all, the tasks. And the thing that we have the deepest competency in is all of the worlds and all of the tasks, because that's what a lot of the experts do. And we have agents that help to augment and make that efficient. And our largest bottleneck was, how do we get the apps that clones of Salesforce and Adobe and Google Workspace that we can have all of the experts populate with the relevant content because naturally that is not what we started doing.

[29:28](https://realalpha.app/v/xzA6lYBzmzQ/?t=1768) **Brendan Foody:** It wasn't as much of our comparative advantage. And so one of the customers that we work with actually suggested proactively that DeepTune was their top apps partner and they should just give

[29:40](https://realalpha.app/v/xzA6lYBzmzQ/?t=1780) us apps that we could populate as we were building out these environments. And so it's sort of ironic when like a customer initiates a partnership it's a good signal that there's something there and so we started working with um i got to know tim the founder better um and he was spectacular and that's obviously half of any of these things right just like who are the people that you want to work with for the next decade and it was clearly just a one plus one equals three kind of thing where we're setting up our new york office they were in new york they were very mission aligned culture aligned team and we could both solve one another's bottlenecks in scaling to solve important problems for the world. And so we took the leap of faith and made it happen. Was this your first acquisition? This was our second, but it was much larger than our first. We also acquired a company called Seeple, founded by Robbie and Kat, who are also spectacular. But Tim had made a giant amount of progress with DeepTune and had just raised his Series A from A16Z, so it was a little bit further along. Do you have an M&A plan going forward? Or how do you think about this strategically? I think M&A is really important. Or it depends a little bit on the type of company,

[31:02](https://realalpha.app/v/xzA6lYBzmzQ/?t=1862) But it's at least something that is super high leverage, right, where you can make mistakes at large scale, but you could also achieve like high positive impact at large scale. And so it's definitely something we think about. And we're exploring a few different categories that we think could be interesting. Have you gotten poached? Have I gotten poached? Yeah. Are people trying to acquire you? I'm sure you've gotten a handful. There have been cases of that. But it really ties to what motivates me and my co-founders. Like Adarsh and I have had opportunities where we could walk away with a lot of cash and never work again and get whatever we want in life. But that's not what motivates us.

[31:50](https://realalpha.app/v/xzA6lYBzmzQ/?t=1910) like we'd rather work 100 hours a week to solve a really important problem in the world. And we have the privilege of of doing that, an incredible market with a spectacular team, and it's hard to know if we'll have that privilege again. And so I think that's really the thing that frames it and how we think about our priorities and what we want to accomplish. How would that work? Who would you get acquired by? What happened with the scale acquisition? Because they still have outside customers? Yeah, the scale acquisition is a little bit funky, of course, because of the M&A stuff, or that M&A restrictions but it's Alex and the core team went over and then a lot of the rest of the team stayed at scale and scale pivoted most of their business to just focus on meta um I don't think we would ever do something like that but does meta still use other people meta still uses other uh vendor or I yeah I shouldn't speak too much to what they do but yeah well alex is very talented he's incredibly talented um and i i've only met alex once but i've been fortunate to work with a lot of the people that spent a lot of time with him and i think he was one of the most generational entrepreneurs of the last decade and so

[33:15](https://realalpha.app/v/xzA6lYBzmzQ/?t=1995) it's been great to see what he's been able to accomplish at meadow as well it's funny because We just had Scott Wu on. We just put out this episode about being independent. I watched that. Scott is so fun to listen to. He's great. I mean, we cut out a lot because there was a lot of laughing going on. But he's awesome. Their team is cracked. I don't think people will use that term anymore, but cognition is cracked. And, you know, we talked about because most of the companies in coding agents have gotten acquired or they're doing something like that. And so I asked him, like, why are you going to be independent? And he wants to build a massive company.

[33:54](https://realalpha.app/v/xzA6lYBzmzQ/?t=2034) Yeah, I think it makes sense. Right. It's like for all of the founders that have accomplished something extraordinary, the motivation is never economic. It wouldn't it wouldn't make sense to do it for money. It's always an emotional pull to doing something really important in the world. And so even when I'm indexing on what are the companies we want to acquire, who are the founders we want to acquire, who are the engineers and executives that we want to work with, it's people that have that emotional pull of doing something extraordinary that tend to be the most effective and accomplish the most impressive things.

[34:35](https://realalpha.app/v/xzA6lYBzmzQ/?t=2075) Another company we were just at was Harvey. And so we talked with... I love those guys too. they're great they're off their office is gorgeous i know i was there the other day and uh yeah it's it's uh very beautiful i don't think i've seen a prettier office on that well you'll have to check out our office yeah ours is nice it might not be the same caliber but yeah this is like is like really really nice anyway so we had winston on we had gabe on and we had nico on and we talked about Lab and the recent benchmarks that they put out. And you were mentioned as one of the partners that helped with that. So can you walk through how you helped and what your, I guess, your role in Lab was? Well, so it's similar to the process I described where the work that we do with them corresponds to starting with great experts across all the granular domains that they care about, and then ultimately helping to curate what are the management processes and specific data shapes that those experts build to translate to a really high-quality data set.

[35:43](https://realalpha.app/v/xzA6lYBzmzQ/?t=2143) And we've put out a lot of public legal benchmarks, such as APEX and APEX Agents, and so they found some of that work really interesting and, of course, have a unique lens in looking at this problem because of understanding all the taxonomy of what law firms care about and how to cover the distribution that the labs aren't effective at. And so I'm really excited about the work that they're doing in post-training models. I think that that is like my mental model for how the AI market is going to develop is that the frontier labs are a leading indicator on what the AI native companies like app layer companies are going to do, which is a leading indicator on the enterprises.

[36:31](https://realalpha.app/v/xzA6lYBzmzQ/?t=2191) And the app layer companies that are going to survive need to be building the frontier model in their specific domain, like Harvey building the frontier model in law, because ultimately the model is the product and you need to be able to have frontier capabilities to stay competitive and build a moat relative to the labs. And so we're super excited to partner with them on that. and there's a lot more coming soon. It's really interesting. I think you also said that AI app layer companies are going to die. Not exactly. Well, what I said is that there's sort of three main categories of companies that have meaningful revenue scale outside of data companies.

[37:17](https://realalpha.app/v/xzA6lYBzmzQ/?t=2237) The first one is Frontier Labs. The second one is the inference providers. So first one being, you know, Anthropic OpenAI, second one being Fireworks Space 10, and the third one is the app layer companies like Harvey. And my thesis is that there are large portions of each of those businesses that get commoditized, right? Like if everyone's buying GPUs in the inference layer, then the GPUs got commoditized. If the app layer companies are building software layers, those will get commoditized as it becomes cheaper and cheaper to build software. But the two things where companies are able to build moats that I've seen that have been durable is first of all their brand of which Harvey has a world-class brand certainly top in the law category and maybe more generally across application layer startups and then second of all their data moats which we can help with meaningfully of how do they understand this flywheel from the not training on customer data but learning the distribution of what do their customers care about and using that to inspire what are the benchmarks that they want to build, what are the training data sets that they need to curate to be best in the world at those.

[38:32](https://realalpha.app/v/xzA6lYBzmzQ/?t=2312) And I think those are really going to be the two primary modes to look for in the application layer companies that are able to build durable and potentially legendary companies. What are the modes for the top two layers? Well, with the Frontier Labs, there's certainly a lot of economy of scale associated with just spending more on compute and more on data, where if you have the most distribution and revenue that you can generate from your models, you can invest more in having more compute and more RL environments, which the labs are scaling up their investments in exponentially. I think there's also, so I'd say it's probably mainly economies of scale. And then there's some flywheels associated with a given model helping them to improve their subsequent model more, i.e. recursive self-improvement, which won't be a vertical takeoff, but it will contribute and make things slightly faster over time.

[39:34](https://realalpha.app/v/xzA6lYBzmzQ/?t=2374) And then brand modes, I would say, are the main ones for foundation models. For the inference companies, some of them are, I would say, the primary one that I think the inference companies need to get right and Fireworks is executing really well on is building custom models, right? It's just the other side of the coin of what Harvey's thinking about because the switching costs of reselling GPUs are zero. As much as people try to tell this nice. story of how there's great margins, like of course, when you buy a bunch of GPUs and those appreciate like 50%, you'll have like good margins for a period of time. The real switching costs

[40:10](https://realalpha.app/v/xzA6lYBzmzQ/?t=2410) **Brendan Foody:** and durable margins come from how do you help a customer customize a model that has high switching costs and can't be ported over to other infrastructure. And so that's how I think

[40:20](https://realalpha.app/v/xzA6lYBzmzQ/?t=2420) about sort of the modes that are developing at the frontier model labs, and then also the modes that are developing in the inference layer and how those companies will build durable businesses. Which is the most vulnerable? I think that it feels like the switching costs for APIs are so low that I think investors overrate some of the revenue quality of api businesses um because ultimately like so much of these just boil down to switching costs if you make it that simple um yeah and like we switch between apis on a daily cadence uh based on whatever models performing the best on our evals for how we run the business i think every company will do that as they mature and have skilled in French bills. And so some of these companies may be really big. But I think the revenue quality is sometimes overrated. Okay. Well, since we're talking macro, there was another tweet you put out. By the way, that was a tweet. So we'll tag that tweet.

[41:35](https://realalpha.app/v/xzA6lYBzmzQ/?t=2495) But there was another tweet that you put out that AI progress requires three things, compute, algorithms, and data. Why do you think data is mispriced between that stack? I think data is still contrarian is the top thing where, for some reason, there has been this perception by investors that we will get super intelligence in three years and AI will be better than humans at absolutely everything. And so we won't need humans to then create evals and train models. And the investors have been saying this for the last three years. Like I remember even in our seed round, like, you know, most of the top firms in Silicon Valley, we've always been very fortunate to work with investors we love, but a lot of the top firms in Silicon Valley just thought that the market would dissipate within a couple of years.

[42:28](https://realalpha.app/v/xzA6lYBzmzQ/?t=2548) And I think the thing that everyone has always missed is the case of Javon's paradox or functionally the lump of labor policy, which is just like as we make the experts more efficient, as the experts are able to improve models three, five, ten times more, that has dramatically increased the amount of demand for people to improve the models. And ultimately, as long as we don't have superintelligence, as long as there are things that humans can do that AI can't do in the economy, we need humans to measure that frontier. And so I think that has been the largest disconnect. And I honestly feel sort of grateful for it in a lot of ways because we've always had lots of access to capital, but it being contrarian reduces the amount of competition in the market.

[43:21](https://realalpha.app/v/xzA6lYBzmzQ/?t=2601) So I'm glad it's I'm glad our market is not nearly as competitive as the foundation model business. But I think that it is on the verge of becoming consensus, especially as people see all of the enterprises that are starting to evaluate agents that they're deploying in post-trade models. So how does this play out over the next 10 years? I think that the key development is that every Fortune 500 is going to look like a Neolab, where they have their core data sets, their core distribution that they care about. to serve their customers, and they need to mobilize some combination of their full-time employees and contractors to curate really high-quality tasks for evaluation and training of the models that they're building. Because ultimately, so many of these processes that are currently human are going to become agentic, and this is the most efficient and scalable way to distill all of that human knowledge throughout their company into the models. And so that will be a giant transformation that we're just starting the first inning of.

[44:36](https://realalpha.app/v/xzA6lYBzmzQ/?t=2676) Damn, I was going to ask you that. So I actually, I asked Winston for some questions ahead of time, and he wanted me to ask about AI data sovereignty. It's going to be so critical. I mean, there's a reason that Jensen and Alex Karp and Satya are all talking about this. Like, I think that Anthropic and OpenAI will have extraordinary businesses. I think both of them could very well be 10 trillion plus dollar companies. But ultimately, we don't want them to subsume the global economy and be the only place that you can go to access frontier intelligence. And for value to accrue to every other company in the Fortune 500 and Americans more generally, we need to make sure that all of these companies are able to train their own models and build their own intelligence that codifies their competitive advantage. I think there's sometimes this narrative from certain labs when they're fundraising that all of a sudden they're just going to turn on training on the usage data flywheel of all of the enterprise data. The Fortune 500s are going to be fine with this and this is the way that they're going to like collect all the environments throughout the economy when clearly that's not the case right clearly the reason that a company like Goldman Sachs exists is that they

[45:58](https://realalpha.app/v/xzA6lYBzmzQ/?t=2758) have a set of competitive advantages associated with the people the processes the documents etc in their business and turning on allowing a frontier lab to train on that data would be analogous to just sharing all of it with a competitor and so they're not going to do that And I believe that instead they're going to be building their own models, deploying their own agents that codify all of this knowledge. And really the heuristic to look at for what is the catalyst of this transition and happening within the enterprise is how much they're spending on inference. Because what we basically find is that when a company is spending under $10 million on inference, they don't generally care that much about evals or how to optimize it or whatever.

[46:47](https://realalpha.app/v/xzA6lYBzmzQ/?t=2807) They're still figuring things out. Once they go from the $10 to $100 million on inference range, then they generally want to spend... That is such a wide gap. It's a wide gap, but we're spending almost a million dollars a day on inference. So like some companies scale fast. And once they're in the 10 to 100, then it's roughly 10% of their inference budget should be going to eval of how do they build out the data sets that tell them like what are all the models they should route this to.

[47:17](https://realalpha.app/v/xzA6lYBzmzQ/?t=2837) **Brendan Foody:** Once they're past $100 million in inference, like a Harvey or a Cursor or all of these companies, then they need to be really seriously thinking about how do they post-train models because the economics start to check out.

[47:32](https://realalpha.app/v/xzA6lYBzmzQ/?t=2852) If you can spend tens of millions of dollars a year, sometimes hundreds or billions in the case of Cursor, in training your own models because you can amortize that fixed cost across all of the inference that you have. And so I expect the inference spend of enterprises will ramp materially, but they'll also go through these curves of adoption where they roll out evals in that 10 to 100 range and then start post-training in the 100 plus range. As they deploy frontier intelligence throughout their businesses. Because you have such a unique and advantageous perspective, I mean, you see literally every company and you see how their data is being used and all this kind of stuff.

[48:13](https://realalpha.app/v/xzA6lYBzmzQ/?t=2893) What advice do you have, taking into account everything that you just mentioned on inference, what advice do you have to these enterprises as they continue to build up their sovereign AI stack? I would say that the largest thing that enterprises misinterpret is how frontier models actually learn, where the vast majority of where the compute gets spent on in trading is no longer just like read all the documents of the company or learn from inputs and outputs and SFT. Like most of the compute is now going towards RL, where the model will attempt the problem many times and then get a score on one of these rubrics.

[49:00](https://realalpha.app/v/xzA6lYBzmzQ/?t=2940) And RL in the vast majority of cases requires subject matter experts that are able to create these rubrics and tasks to evaluate and train models. And so for any company that is training their own model, they need to build tasks that produce clear eval scores. And then once they feel confident in the eval, then they should scale up the distribution of what they care about to hill climb that eval. So maybe those are the two pieces of advice is, first of all, build tasks rather than just like pretending the model can read everything and learn. And second of all, that they should start with the eval because that is the foundation for how every AI lab and successful application layer understands how well they're doing and has a baseline in improving their models of their training.

[49:55](https://realalpha.app/v/xzA6lYBzmzQ/?t=2995) Good advice. Thank you. Yeah. Okay. So two more questions. What are you most looking forward to in the next 12 months? What can we expect from Merck? Or are you going to go public? I don't think we'll go public in the next 12 months. but we are. It's funny. I like think in 2024, I tweeted IPO by end of year. And I thought it was like clearly a joke because we dropped out of college like 12 months prior. And people are like, whoa, like no way. And so we're not going to go public in the next 12 months, but it's certainly on our radar. The thing I'm most looking forward to is really the stuff on enterprise where I think that it's starting to happen so quickly as the inference bills mature that the really classic companies, the Fortune 50 non-technology companies, are starting to understand how important this is.

[50:54](https://realalpha.app/v/xzA6lYBzmzQ/?t=3054) Some of those companies are facing existential threat from the frontier models as those companies creep into their sectors. And that's just going to be a huge catalyst for transitioning the way that intelligence gets used and deployed in the economy. And so I think it'll be incredible to see. Cool. Well, I know this is a very strange closing question, but you asked me this before we started recording and you were like, how do you spend your days? So I want to ask you, how do you spend your days are you flipping donuts no longer flipping donuts uh i let's see so i generally will wake up around 7 45 and then if i'm i work out most days around 7 50 until 8 30 and then i'm in the office by nine and then i would usually have four hours of meetings a day sometimes it's closer to 8 to 10, and I would knock out lots of messages, emails, and Docs elsewhere. And my time is probably roughly a third on hiring, a third time with customers, and a third time on research and product. And then I'm in the office until 10 p.m. And I rinse and repeat six days a week. And then Sundays I work from home.

[52:22](https://realalpha.app/v/xzA6lYBzmzQ/?t=3142) nice it was interesting when i talked to winston i got so so many people reached out to me listening to that episode because they're like oh my god like it's really great to hear someone who's going through that hyper growth of how they're managing their day and what they spend their time on and there was one part where he was talking about like fixing people's calendars and like deleting meetings are you trying to like constantly try to just like remove distractions How do you manage focus? Totally. It's challenging because I think that it's easy for everyone to want to escalate things to me.

[52:59](https://realalpha.app/v/xzA6lYBzmzQ/?t=3179) And the only way the org scales is not only me, but also the people on the ground having high trust in the leaders and the executives in the organization. And so making sure that we have a really strong leadership team is probably the most important thing for that. And I feel like I could spend all day and more just responding to my messages and emails. And so at some point, you also have to just stop responding to everything. I still inbox zero every day because I want to make sure I get through everything. But being very thoughtful about that. And we're to leverage agents to make things more efficient.

[53:41](https://realalpha.app/v/xzA6lYBzmzQ/?t=3221) Cool. Are you guys hiring? We are hiring a ton. And so it's ironic because everyone says, oh, like, we're going to displace all the jobs, but all of the AI companies, us included, are hiring as quickly as possible. And I would, yeah, we'd love to work with you. Check out Mercor.com. And not only are we hiring for the experts, but lots of full-time employees to join our engineering team, our research team, as well as our operations team. Amazing. Great place to end it. Well, thank you so much, Brendan. Thanks for having me.