Mercor CEO & Co-Founder, Brendan Foody: How They Grew from $1M to $500M in 17 Months
Brendan Foody (guest), Harry Stebbings (host)
In this episode of The Twenty Minute VC, featuring Brendan Foody and Harry Stebbings, Mercor CEO & Co-Founder, Brendan Foody: How They Grew from $1M to $500M in 17 Months explores mercor’s 22-Year-Old CEO On Building A $500M AI Data Rocket Brendan Foody, 22-year-old CEO and co-founder of Mercor (called “Marqor/Macaw” in the convo), explains how the company scaled from $1M to a $500M revenue run rate in just 17 months by supplying elite human experts to train and evaluate frontier AI models.
Mercor’s 22-Year-Old CEO On Building A $500M AI Data Rocket
Brendan Foody, 22-year-old CEO and co-founder of Mercor (called “Marqor/Macaw” in the convo), explains how the company scaled from $1M to a $500M revenue run rate in just 17 months by supplying elite human experts to train and evaluate frontier AI models.
He argues the data-labeling market has shifted from low-cost crowdsourcing to highly vetted “Goldman/McKinsey/FAANG-level” talent building complex reinforcement learning (RL) environments that mirror real professional workflows.
Foody pushes back on claims that synthetic data or plateauing scaling laws will eliminate the need for human experts, insisting that as long as humans can do things models can’t, there will be a massive market for expert-driven data and evals.
The conversation also covers Mercor’s economics, valuation philosophy, competitive dynamics with players like Scale and Surge, broader AI investment froth, founder secondaries, work culture, and how evals must evolve to reflect real-world capabilities instead of academic benchmarks.
Key Takeaways
Elite human expertise is now central to frontier AI training.
Mercor’s core thesis is that the market has moved from low-cost crowd work to sourcing and vetting top professionals (e. ...
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The most valuable data contributors follow a power-law distribution.
Foody notes that in a 100-person project, 10–20% of contributors drive most of the model improvement—similar to company value creation—so Mercor differentiates by identifying, attracting, and matching these “10x” experts to high-impact tasks.
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Current AI evaluation benchmarks are largely misaligned with real utility.
He argues that focusing on Olympiad math or ‘humanity’s last exams’ is “wholly disconnected” from what enterprises care about; future evals must measure performance on realistic workflows (e. ...
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Synthetic data will augment, not replace, expert human input for years.
While synthetic data can scale and cheapen some training, any capability where humans can still outperform models requires human-generated ground truth and verifiers; until superintelligence exists, humans remain essential to pushing the frontier of model capabilities.
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Vendor consolidation will favor those with the deepest talent networks and matching intelligence.
Labs often start with multiple data vendors but tend to concentrate spend with partners that deliver the biggest model performance gains; Mercor bets that its referral-based supply, high pay rates, and sophisticated matching algorithms will make it a winner as the market consolidates.
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Hypergrowth doesn’t require reckless burn if unit economics are strong.
Mercor grew from $1M to $500M run rate while remaining profitable, emphasizing capital efficiency and fundamentals instead of massive subsidies—Foody sees “fortress balance sheet” financings as mostly signaling tools rather than operational necessities.
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RL environments could eventually encode a huge swath of the economy.
Foody relays that multiple lab leaders believe RL environments will “subsume the entire economy” as monotonous, repetitive knowledge work is turned into codified workflows for models, with humans designing frameworks and evals instead of executing repetitive tasks.
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Notable Quotes
“We scaled the business from one to 500 million in revenue run rate in the last 17 months, which is the fastest revenue growth of all time.”
— Brendan Foody
“The total addressable market is limited by the amount of things that humans are better at than models.”
— Brendan Foody
“Eval’s are bullshit when they’re about Olympiad gold medals and humanity’s last exam. They’re wholly disconnected from the outcomes that consumers and enterprises actually care about.”
— Brendan Foody (paraphrasing and agreeing with Harry Stebbings)
“Having phenomenal people that you treat incredibly well is the most important thing in this market.”
— Brendan Foody
“If we think about the model as the product, then the eval is the PRD.”
— Brendan Foody
Questions Answered in This Episode
If RL environments truly ‘subsume the entire economy’, what types of jobs and tasks will realistically remain human-only for the longest period?
Brendan Foody, 22-year-old CEO and co-founder of Mercor (called “Marqor/Macaw” in the convo), explains how the company scaled from $1M to a $500M revenue run rate in just 17 months by supplying elite human experts to train and evaluate frontier AI models.
Get the full analysis with uListen AI
How can outsiders independently verify that expert-driven data actually yields superior model performance versus cheaper, synthetic-heavy approaches?
He argues the data-labeling market has shifted from low-cost crowdsourcing to highly vetted “Goldman/McKinsey/FAANG-level” talent building complex reinforcement learning (RL) environments that mirror real professional workflows.
Get the full analysis with uListen AI
What would a gold-standard, real-world eval suite for a large enterprise actually look like, end-to-end, and who should own maintaining it?
Foody pushes back on claims that synthetic data or plateauing scaling laws will eliminate the need for human experts, insisting that as long as humans can do things models can’t, there will be a massive market for expert-driven data and evals.
Get the full analysis with uListen AI
At what point does concentration risk with a few major lab customers outweigh the benefits of deep partnerships for a company like Mercor?
The conversation also covers Mercor’s economics, valuation philosophy, competitive dynamics with players like Scale and Surge, broader AI investment froth, founder secondaries, work culture, and how evals must evolve to reflect real-world capabilities instead of academic benchmarks.
Get the full analysis with uListen AI
How might the economics and ethics of paying $95/hour to elite contributors versus $30/hour crowd workers reshape the labor market around AI?
Get the full analysis with uListen AI
Transcript Preview
I made hundreds of thousands of dollars when I was in high school.
(instrumental music) Today we have the fastest growing company in history, and thrilled to welcome Brandon Foody, co-founder and CEO at Macaw to the hot seat.
We were already at a nine-figure revenue run rate, and the company quadrupled since the Scale acquisition. We scaled the business from one to 500 million in the last 17 months, which is the fastest revenue growth of all time, one month faster than Coursera's time. Our average marketplace pay rate is $95 an hour. To put that in frame of reference, Scale and Surge generally pay about $30 an hour. We have the demand to like double overnight. If we can meet capacity, RL environments will subsume the entire economy, because it doesn't make sense that humans would be doing monotonous, redundant work.
What's one widely held belief about AI that you're like, "God, that's so wrong, just please stop?"
Uh, we'll have...
Ready to go? (instrumental music) Brandon, dude, I've been so looking forward to this. I just had the best chat to Victor who gave me the best intel. So you should be really quite nervous at this point. But thank you for joining me.
(laughs) Thank you for having me on. I'm, I'm not sure what to expect with that. But I'm excited to jump in.
I think mothers are the most important things in the world. And Victor told me that I had to start with your ability to sell early, and why your mother was nervous about it. Can we just start there?
(laughs) Absolutely. So I had a dozen different side hustles when I was growing up, selling things in, in one form or another. But one of my favorites is that in eighth grade, I loved selling donuts. Where I saw that Safeway was selling donuts for $5 a dozen, and so I would buy Safeway donuts, I would bike to my middle school and sell them for $2 each. And I saw it was working so I wanted to scale it up. So, I asked my mom to drive me to Safeway. She said that she didn't want any giveaways, so she would charge me $20 to drive me in her minivan to Safeway, buy 10 dozen donuts, go to my middle school, sell them for $2 each. Uh, I had all sorts of things happened where competition popped up selling Chuck's Donuts, which if people aren't familiar, has like a $1 cost basis, and so ... But they were higher quality donuts. And so I dropped my prices to $1 for two weeks to run them out of business because I, I knew that middle schoolers would care more about, uh, price as the comparative advantage. I had my, uh, principal called me into their office to try to shut down my donut stand, saying that, uh, you know, I wasn't allowed to sell food on school campus, and so I moved my donut stand 20 feet over off of school campus so that they couldn't police me so to speak. And tying back to your question, Harry, after my mom saw all of this when I was in eighth grade, she was very nervous that I would start selling drugs, right? 'Cause it's like, you know, small, a small jump from donuts to drugs. And so, she insisted that while I'm not Catholic, I should go to Catholic high school, uh, to make sure that, that I, uh, I stayed in touch with my values and, and met my co-founders there. So I guess she, she was right all along.
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