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Mercor Raises $750M at $20B Valuation | CEO Brendan Foody
Brendan Foody argues that human expertise will remain essential for evaluating and training AI, even as models become more capable.
Sourcery · Molly O'Shea
- Brendan Foody is co-founder and CEO of Mercor.
The best minutes
- 43:35Enterprise AI adoption follows inference spending6 min
- 12:09Scalable interviews help find rare high-quality experts3 min
- 5:30Mercor sells training environments, not just expert access3 min
Also worth watching
- 36:55AI companies need different moats at each layer4 min
- 2:19Partner payment failures pushed Mercor to own expert payouts3 min
- 28:28DeepTune filled Mercor's gap in simulated work apps3 min
- 19:53Seed-stage customer failures shaped Foody's operating cadence4 min
- 15:28Mercor treats frontier research as a signal for wider adoption3 min
Skip the sponsor reads, the entrepreneur role-model and tax-policy discussion, and the closing daily routine and hiring chat.
Full analysis · 6 min read
Real Alpha · Nov 10
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.
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6 sections, the 3 other voices it tests Foody against, and what it means for you. Every episode we cut gets one.
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Inference spending tracks when buyers begin funding evals
Foody frames these bands as what Mercor basically finds among customers, not a universal budget prescription. Below $10 million in inference spend, he says companies generally do not care much about evaluations yet.
From $10 million to $100 million, he says companies generally want roughly 10% of inference spend going to evaluations. Above $100 million, he says companies need to be seriously thinking about post-training because the economics start to check out.
He ties those economics to amortizing fixed training costs across inference volume. But the bands span a wide range, and Foody gives no sample, sector split, or observed conversion rate.
That matters beside Amazon CEO Andy Jassy's October account on a16z Podcast. Jassy described AI demand as concentrated in labs and a few apps, with enterprise adoption still emerging. His account cautions against treating Mercor's customer pattern as an enterprise-wide baseline.
Our read
Foody offers a customer-based stage signal, not an industry benchmark. Teams should test it against their own workload, costs, and business value.
Mercor's product combines people, simulated apps, and scored tasks
The talent platform finds workers, Studio helps them create data, and an AI project manager automates project oversight. Foody says experts are one ingredient, not the delivered product.
The delivered environment has three parts, apps, worlds, and tasks. Apps imitate workplace tools, worlds supply their messages and documents, and tasks pair prompts with grading rubrics. The rubric gives a model a score it can learn from.
Take his Google Workspace example, where a simulated app holds documents, messages, and other work context. Experts create tasks and rubrics that let a model attempt work and receive a grade.
Foody gives a hypothetical example, not a measured benchmark. An expert hour might now produce three to five times more model improvement with a copilot, he says. He says demand then expanded exponentially as expert work became more productive, a Jevons paradox.
Mercor sells these environments, so Foody's view that expert work will persist aligns with its product. The episode offers no pricing, margin, or reuse data to test how much value comes from software versus human work.
Our read
The business he describes is a training environment, not simply a labor marketplace. The interview explains its components but does not establish their unit economics.
Human feedback remains valuable where models still fail
Foody ties the market's duration to a boundary, what humans can still do that models cannot. He names persuasive conversations, wet-lab experiments, and car repairs as examples.
For instance, math can often be checked with a calculator. Many other tasks, he says, need feedback from teachers, managers, or colleagues.
That contrast narrows the claim. Expert demand lasts where people can identify mistakes or define a useful reward signal. The transcript does not quantify how much such work remains by domain.
Foody says top researchers expect feedback-based learning to continue for decades. That is his outlook, not a measured estimate of Mercor's addressable market.
Our read
Human expertise remains useful where models lack reliable ways to judge their own work. The scale of that demand depends on which tasks remain beyond model capability.
Interview and payment systems underpin environment delivery
Foody says the talent platform has over five million people actively looking for work, and 70% come through referrals. He says referred people tend to be highest quality.
He says he and co-founder Adarsh Suri built an AI interviewer in April 2023, soon after ChatGPT launched. Foody describes scalable data collection as a central challenge in labor matching.
He later says scaling interviews helped Mercor find strong people in markets with lower talent density. Foody corrected himself before saying the company had done over seven million interviews.
A different operating lesson came when a company Mercor supplied talent to had public payment failures. Foody says Mercor had hired more than 1,000 people for that customer. Unpaid workers generated a flood of support tickets.
That pushed Mercor to take responsibility for paying experts directly, he says. Foody reports just about $1 billion in historical payouts without major issues of that sort.
Our read
Recruiting, screening, and payments are operational foundations for the product. These figures describe scale, not independently verified expert quality or environment performance.
Foody says custom models could distinguish inference providers
Foody divides the market into frontier labs, inference providers, and application companies. He says software and raw GPU access can be commoditized at their respective layers.
For inference providers, he sees customized models as a stronger defense, naming Fireworks as a company executing well. GPU resale alone, he says, offers no switching costs.
Foody says Mercor switches APIs daily based on model performance in its evals. He says investors may therefore overrate API revenue quality.
Modal CEO Erik Bernhardsson said on 20VC in November that selling tokens alone creates a race to the bottom. He sees adjacent products as a way to differentiate providers, a broader answer than Foody's model-customization focus.
Erik Bernhardsson · 20VC · Nov 2Inference providers need a broader platform to avoid commoditization
Our read
Custom models are a proposed source of retention, not demonstrated margins. Measure model lift and migration friction rather than treating GPU use as customer loyalty.
Foody expects enterprises to train around proprietary data
Foody expects Fortune 500 companies to build models and agents around proprietary data and customer needs. He says their processes and information encode competitive advantage.
Letting an outside lab train on that material, he says, would be like sharing it with a competitor. Mercor sells expert-built tasks, evaluations, and environments relevant to this vision, giving Foody a direct commercial interest in it.
His practical advice is narrower than the forecast. He says firms should first build tasks that produce clear scores. Once confident in the eval, they should scale the distribution of work they care about to hill-climb it.
George Kurtz, CrowdStrike's CEO, relayed a bank executive's view on No Priors in October. Open-weight models three months behind the frontier were sufficient for many needs, while frontier models remained for high-value work.
That does not disprove Foody's forecast, but it cautions against treating proprietary model building as the only enterprise path. The episode offers no enterprise cost or performance results to show when building internally wins.
“they should start with the eval”
Our read
Foody's strongest enterprise advice is to make performance measurable before scaling training. His forecast remains one path among several, not an established adoption pattern.
What it means for you
- Define one eval. Choose a valuable task, write its scoring rubric, and record a baseline before increasing training work. Foody's advice starts with a scoreable task, not a broad document dump.
- Split AI spending. Track inference, evaluations, and post-training separately this quarter. Compare your pattern with Foody's customer observations without treating his thresholds as targets.
- Audit data boundaries. List which proprietary processes and documents cannot be used to train an outside model. Tie that decision to the competitive advantage Foody says such data can encode.
- Test model portability. For a representative workload, log performance and effort when switching APIs. Foody says Mercor switches daily based on its evals, making portability measurable rather than assumed.
What to watch
- Whether enterprise teams begin reporting evaluation and post-training spend separately from inference.
- Whether task-level evaluations show improvements on work customers actually value.
- Whether custom models create measurable switching costs beyond GPU access or token volume.
Send this to the leader setting AI platform and evaluation budgets.
Watch the minute where Foody lays out the inference spending bands
Written by Real Alpha from the episode's transcript and the other moments it links, then checked against the transcript. Quotes are the speaker's exact words.
Minute by minute
- 0:30Work 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.
- 2:19Mercor 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.
- 5:30The 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.
- 8:43Human 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.
- 12:09Referrals 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.
- 15:28Culture 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.
- 17:59Foody 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.
- 23:14Foody 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.
- 28:28Deeptune 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.
- 31:19Foody 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.
- 34:29Brand 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.
- 41:28Data 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.
- 43:35Enterprises 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.
- 49:49Foody 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.
Published Nov 10, 2026.
Transcript · 38 min read
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.
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
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
“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”
Read the whole transcript
Actually, I've written some blogs about this because there's a lot of interesting technical problems in there.
“the experts are one component of the data sets, the RL environments that we deliver to the labs.”
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.
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
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“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.”
i mean i have so many questions off of that um one of the questions that i was like super curious
“what's happening at the frontier is a leading indicator of what happens at the AI native companies”
Would you say you have clean fuel or at this point, do you have a little bit of redemption fuel?
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.
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.
“80 percent of them go out of business within 12 months.”
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.
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
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.
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.
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“one of the customers that we work with actually suggested proactively that DeepTune was their top apps partner”
strategically? I think M&A is really important. Or it depends a little bit on the type of company,
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.
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
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.
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.
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.
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.
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
“how do you help a customer customize a model that has high switching costs and can't be ported over to other infrastructure.”
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.
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.
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.
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.
“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”
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.
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.
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.
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.
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.
