
Why experts writing AI evals is creating the fastest-growing companies in history | Brendan Foody
Brendan Foody (guest), Lenny Rachitsky (host)
In this episode of Lenny's Podcast, featuring Brendan Foody and Lenny Rachitsky, Why experts writing AI evals is creating the fastest-growing companies in history | Brendan Foody explores experts Writing AI Evals Are Powering History’s Fastest-Growing Companies Lenny Rachitsky interviews Brendan Foody, CEO and co-founder of Merkor, a labor marketplace that supplies expert talent to AI labs to write evals and post-training data, enabling rapid improvements in model capabilities.
Experts Writing AI Evals Are Powering History’s Fastest-Growing Companies
Lenny Rachitsky interviews Brendan Foody, CEO and co-founder of Merkor, a labor marketplace that supplies expert talent to AI labs to write evals and post-training data, enabling rapid improvements in model capabilities.
Merkor has gone from near-zero to roughly $400–500M in revenue run rate within 16–17 months, working with most top AI labs and major AI application companies, proving the scale of demand for high-quality human feedback and evaluation.
Foody explains that we’re entering an “era of evals,” where defining and measuring what “good” looks like for models—across professional domains like law, medicine, and software—is the key bottleneck to progress, and a massive new category of work.
They also discuss the future of labor markets, which skills will remain valuable, how founders can detect and pursue fast-moving market opportunities, and the cultural principles that enabled Merkor’s hypergrowth.
Key Takeaways
Evals are becoming as critical as the models themselves.
If the model is the product, evals are both the product requirements and the benchmark—defining what success looks like, enabling reinforcement learning, and serving as the sales collateral that proves model capabilities to the market.
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The main bottleneck for top AI labs is expert human judgment, not raw data.
Labs increasingly need highly skilled professionals—lawyers, radiologists, bankers, engineers—to design rubrics, tests, and RL environments that evaluate and reward correct behavior in complex domains models can’t yet master.
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A massive new job category is emerging around AI evaluation and post-training.
Contrary to the dominant narrative of job loss, AI is creating demand for tens of thousands (and growing) of experts who define tasks, write evals, and grade model outputs—often earning $95–$500 per hour on flexible, project-based work.
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The most resilient careers will be in elastic-demand fields and AI power users.
Roles like software engineering, product management, and other high-leverage knowledge work will likely expand as productivity rises; individuals who learn to deeply integrate AI into their daily workflows will outperform their peers.
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Founders should chase clear pull in fast-moving markets, not force fit.
Merkor’s hypergrowth came from noticing strong demand signals from top labs and incumbents’ weaknesses, then aggressively pivoting and focusing on that pocket instead of trying to push a slower, traditional hiring product.
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High standards plus intensity create outsized results when paired with customer obsession.
Merkor hired extremely selectively early on, maintained an intense, output-focused culture, and invested almost exclusively in product and customer outcomes rather than sales and marketing, driving viral word-of-mouth growth.
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AI progress will likely be steady and transformative, not an immediate superintelligence jump.
Foody is skeptical of near-term superintelligence; he expects a long road of incremental capability gains powered by better evals and post-training, during which AI will automate most knowledge work but still depend heavily on human-defined objectives.
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Notable Quotes
“If the model is the product, then the eval is the product requirement document.”
— Brendan Foody
“The market is bound by the amount of things where humans can do something that models can't.”
— Brendan Foody
“Models are only as good as their evals.”
— Brendan Foody (relaying a customer quote)
“We grew from one to $400 million in revenue run rate in 16 months. Fastest ascent in history.”
— Brendan Foody
“You can just do stuff. So many people have ideas, but the barrier is initiative.”
— Brendan Foody
Questions Answered in This Episode
How can non-technical professionals (e.g., lawyers, teachers, consultants) practically transition into AI eval and RL-related work?
Lenny Rachitsky interviews Brendan Foody, CEO and co-founder of Merkor, a labor marketplace that supplies expert talent to AI labs to write evals and post-training data, enabling rapid improvements in model capabilities.
Get the full analysis with uListen AI
What concrete steps should an enterprise take to design effective evals for its core value chain and measure AI-driven automation?
Merkor has gone from near-zero to roughly $400–500M in revenue run rate within 16–17 months, working with most top AI labs and major AI application companies, proving the scale of demand for high-quality human feedback and evaluation.
Get the full analysis with uListen AI
How might global labor markets change if a single platform truly became the default marketplace for all high-skill work?
Foody explains that we’re entering an “era of evals,” where defining and measuring what “good” looks like for models—across professional domains like law, medicine, and software—is the key bottleneck to progress, and a massive new category of work.
Get the full analysis with uListen AI
Where is the ethical line between using expert human labor to train models and potentially automating those experts’ jobs in the long run?
They also discuss the future of labor markets, which skills will remain valuable, how founders can detect and pursue fast-moving market opportunities, and the cultural principles that enabled Merkor’s hypergrowth.
Get the full analysis with uListen AI
If AI progress slows relative to current hype, which business models around evals and training remain durable, and which are most at risk?
Get the full analysis with uListen AI
Transcript Preview
(instrumental music) The wealthiest companies in the world are willing to spend whatever it takes to improve model capabilities.
We're entering the era of evals.
We started working with all of the top AI labs. What the labs need is a labor marketplace. They actually need extraordinary professionals that can measure model capabilities.
They found this pocket, maybe the biggest business opportunity in history.
We grew from one to 400 million in revenue run rate in 16 months. Fastest ascent in history.
Why is this so valuable?
The market is bound by the amount of things where humans can do something that models can't. The lab's primary bottleneck to improved models is how they can effectively have some way of measuring what success looks like for the model.
There's this tweet that you retweeted, "If you really think about it, we were put on Earth to create reinforcement learning training data for labs."
It's highly likely that the entire economy will become an RL environment machine, building out all of these worlds and contexts. And I think the narrative in AI over the last three years has almost entirely been one of job displacement, but very few companies and people have talked about this new category of jobs that's being created.
I talked to a lot of people about, "What should I be studying? Where should I be getting better?"
How can they leverage this technology to do so much more? We'll give people interviews where we say, "Use whatever tools are available to build a website, and let's see what product you're able to build in an hour."
Today my guest is Brendan Foudy, CEO and co-founder of Merkle. Merkle is the fastest-growing company in history to go from one to $500 million in revenue. They did this in 17 months, less than a year and a half. Brendan is also the youngest unicorn founder ever. They just raised $100 million at $2 billion valuation. Merkle, if you haven't heard of them, helps AI labs and AI companies hire experts to help them train their models using AI. They've never had a customer churn. Their net retention is over 1,600%, and they're on a nine-figure revenue run rate. In our conversation, we talk about the increasing value and importance of evals, the landscape of AI training companies like Merkle and why they've become so important and valuable, how Brendan discovered this opportunity, his insights on what product market fit looks like, the core tenets he's instilled within his organization that have allowed him to build the fastest-growing company in history, what people writing evals for labs are actually doing day-to-day, which skills and jobs are gonna last the longest with the rise of AI, why he doesn't think we'll see AGI or super intelligence anytime soon, and so much more. This episode is incredible. You need to hear this. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. It helps tremendously. Also, if you become an annual subscriber of my newsletter, you get 15 incredible products for free for one year, including Lovable, Replit, Bolt, Innate and Linear, Superhuman, Descript, Whisperflow, Gamma, Perplexity, Warp, Granola, Magic Patterns, Raycast, ChatBRD, and Mobbing. Check it out at lennysnewsletter.com and click Product Pass. With that, I bring you Brendan Foudy. This episode is brought to you by WorkOS. If you're building a SaaS app, at some point your customers will start asking for enterprise features like SAML authentication and SCIM provisioning. That's where WorkOS comes in, making it fast and painless to add enterprise features to your app. Their APIs are easy to understand so that you can ship quickly and get back to building other features. Today, hundreds of companies are already powered by WorkOS, including ones you probably know, like Vercel, Webflow, and Loom. WorkOS also recently acquired Warrant, the fine-grained authorization service. Warrant's product is based on a groundbreaking authorization system called Zanzibar, which was originally designed for Google to power Google Docs and YouTube. This enables fast authorization checks at enormous scale while maintaining a flexible model that can be adapted to even the most complex use cases. If you're currently looking to build role-based access control or other enterprise features like single sign-on, SCIM, or user management, you should consider WorkOS. It's a drop-in replacement for Auth0 and supports up to one million monthly active users for free. Check it out at workos.com to learn more. That's workos.com. You fell in love with building products for a reason, but sometimes the day-to-day reality is a little different than you imagined. Instead of dreaming up big ideas, talking to customers, and crafting a strategy, you're drowning in spreadsheets and roadmap updates and you're spending your days basically putting out fires. A better way is possible. Introducing Jira Product Discovery, the new prioritization and roadmapping tool built for product teams by Atlassian. With Jira Product Discovery, you can gather all your product ideas and insights in one place and prioritize confidently, finally replacing those endless spreadsheets. Create and share custom product roadmaps with any stakeholder in seconds, and it's all built on Jira, where your engineering teams are already working, so true collaboration is finally possible. Great products are built by great teams, not just engineers. Sales, support, leadership, even Greg from finance. Anyone that you want can contribute ideas, feedback, and insights in Jira Product Discovery for free. No catch. And it's only $10 a month for you. Say goodbye to your spreadsheets and the never-ending alignment efforts. The old way of doing product management is over. Rediscover what's possible with Jira Product Discovery. Try it for free at atlassian.com/lenny. That's atlassian.com/lenny. Brendan, thank you so much for being here. Welcome to the podcast.
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