Fez Zafar

We’ve raised our $350M Series C at a $10B valuation from @felicis, @benchmark, and @generalcatalyst. Just 2 years after starting, Mercor is paying $1.5 million per day to experts in our marketplace. We’re creating a new category of work in the AI economy, where software engineers, bankers, lawyers, and other professionals earn based on their experience while advancing the frontier of AI. While most new categories take time to build momentum, we’ve broken every growth record. For comparison, in their first 2 years: - Uber paid out just over a $1 million to drivers - Airbnb paid out $10 million to hosts We are unlocking human potential in the AI economy.

@BrendanFoody The next generation of AI models will unlock true economic value.

How a 22-year-old dyslexic dropout created the fastest revenue-growing business in history—$1M to $500M in just 17 months. @BrendanFoody discovered that AI labs were facing a critical bottleneck: they needed human experts to create "evals"—tests that teach models what correct looks like. His company @mercor_ai began connecting labs with lawyers, doctors, engineers, and other specialists to create evals and training data for models (for $95-500/hour). Today, @mercor_ai works with 6 of the Magnificent 7, all top 5 AI labs, has never had a customer churn, and has a net revenue retention of 1,600%. In my conversation with Brendan, we discuss: 🔸 Why evals have become the primary bottleneck for AI progress 🔸 How exactly Mercor grew to $500M revenue in 17 months 🔸 Brendan’s meeting with xAI that changed his company’s trajectory 🔸 Which skills and jobs will be most valuable as AI continues to advance (hint: jobs with “elastic” demand) 🔸 Why Brendan believes AGI and superintelligence are not happening anytime soon 🔸 The three unique core values that drove Mercor’s success 🔸 How Harvard Lampoon writers are making Claude funnier Listen now 👇 • YouTube: youtu.be/ja6fWTDPQl4 • Spotify: open.spotify.com/episode/3whvAE… • Apple: podcasts.apple.com/us/podcast/why… Thank you to our wonderful sponsors for supporting the podcast: 🏆 @WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs: workos.com/lenny 🏆 @Jira Product Discovery—Atlassian's new prioritization and roadmapping tool built for product teams: atlassian.com/lenny 🏆 @enterpret_ai—Transform customer feedback into product growth: enterpret.com/lenny

After years of watching Lenny's podcast, it was incredible to do an episode together. The entire economy is becoming an RL environment machine and catalyzing Mercor's unprecedented growth.

@mercor_ai Read more here: x.com/BrendanFoody/s…

Mercor (@mercor_ai) scaled from $1-500M in revenue run rate in the last 17 months, making us the fastest growing company of all time. Our growth is accelerating. We averaged 11% week over week growth in July, 18% WoW growth in August, and 19% WoW growth in September. One trend driving this meteoric growth: the Economy is Becoming an RL Environment Machine. Reinforcement learning is becoming so effective that agents can hillclimb any benchmark, but humans need to define the rewards to automate everything. While everyone fears job loss, we’re creating a new category of knowledge work faster than any other time in history. The future of work will converge on training agents. We're paying out over $1M / day to people in our marketplace and hiring experts rapidly across nearly every domain: software engineers, doctors, lawyers, consultants, bankers, and many more.

In January 2026, @mercor_ai is expanding to NYC! Our platform creates thousands of jobs each week and we’re the fastest growing company in the world by revenue. As we continue to scale rapidly, our new office in NYC will bring us closer to some of the most talented engineers and operators in America. We’re kicking off the recruiting process for: -Strategic Project Leads (former consultants, bankers, PE analysts) -Software Engineers -Machine Learning Engineers Apply to these positions through the links in the comments below!

I often reflect on first meeting @peterfenton, flying in his helicopter around San Francisco. We talked about how leaving college to start @mercor_ai wasn’t a logical decision. It was emotional — a calling to pursue impact with the people we wanted to build with. I couldn't be more thrilled to welcome Peter to our board as we embark on this journey together.

The meteoric rise of @mercor_ai becomes clear once you meet the founders. I’m excited to announce that I’ve joined their board and will be working closely with them alongside Sundeep Jain, who brings exceptional marketplace expertise. They’ve quickly established themselves as the go-to provider for top AI companies, delivering unmatched speed and quality

Apply to join Mercor's software engineering team here: work.mercor.com/jobs/list_AAAB…

Apply to join Mercor's ML team here: work.mercor.com/jobs/list_AAAB…

Mercor (@mercor_ai) is now working with 6 out of the Magnificent 7, all of the top 5 AI labs, and most of the top application layer companies. One trend is common across every customer: we are entering The Era of Evals. RL is becoming so effective that models will be able to saturate any evaluation. This means that the primary barrier to applying agents to the entire economy is building evals for everything. This will be one of the largest buildouts we have ever seen with enterprises pouring hundreds of billions of dollars into evals for every workflow we want agents to automate. We're quickly defining a new class of work and hiring across nearly every domain: software engineers, consultants, bankers, lawyer, doctors, gamers, and many more.

THE SECRET MERCOR MASTER PLAN Imagine a world where Jeff Bezos is a hedge fund investor, Howard Shultz is a salesman, and Reed Hastings is a teacher. That was the world we lived in, not so long ago. These are the jobs they were doing before they found the best use for their talents. We founded Mercor because the labor market is the largest, most inefficient market in the world. Better matching people with the work they do everyday is the largest lever on maximizing global utility. While we gained incredible traction with our initial focus on hiring experts to train AI models, this is only the first step in our plan to solve global labor allocation. THE WEDGE Marketplaces are hard to get off the ground, but if they do take off they become huge. The successful ones have a wedge into a large and pressing unmet need. For Uber, the wedge was black cars. For Airbnb, it was conferences. We started 2024 in our apartment with no US employees, under $1M in annual revenue, and only seed companies as customers. Last year we grew 6400% and we now work with the most sophisticated technology companies in the world, making us one of the fastest growing companies in Silicon Valley history. We believe we can create hundreds of thousands of jobs with AI labs alone, but that pales in comparison to the billions of knowledge work jobs in the world. The technology that we’re been building is generally applicable. STRUCTURAL INEFFICIENCY Labor inefficiency stems from two structural challenges in the market: 1. Fragmentation – job candidates apply to a handful of jobs and companies consider a fraction of a percent of candidates in the market. This is because matching supply and demand needs to be solved manually (and previously in person). Companies manually review resumes, conduct interviews, and predict who they believe will perform well. Human time is the limiting factor. However, if you can solve this matching problem at the cost of software it allows you to interview everyone, making way for a global, unified labor market that every candidate applies to and every company hires from. 2. Imperfect Information – When you order a ride on Uber, you know what you’re getting. When you book an Airbnb, the pictures usually do a pretty good job. When you’re hiring someone, it’s extremely difficult to accurately predict how well they will perform on the job. Imperfect human judgement is embedded within every transaction. While LLMs are not perfect at talent assessment, models are quickly surpassing human capabilities. This trend will continue to make transactions more efficient. Correspondingly, our main objectives are to attract high caliber applicants to come to Mercor and accurately predict candidate’s job performance. Achieving these objectives will solve global labor efficiency more broadly. Hiring expert contractors to train AI models is the perfect forcing function on these objectives. First, we collect performance data from AI labs within days, compared to the 3 month lag from a traditional enterprise. This allows us to immediately calibrate on the effectiveness of our models and continuously experiment to find the features predictive of success. Second, we need to hire a broad pool of candidates across all knowledge work jobs (law, consulting, medicine, engineering, etc.). This builds the strength of our talent pool across every professional and academic domain. Third, we will service “unreasonable asks” from AI labs like needing to hire 300 people in two days. These high volume requests for quality people on extremely short timelines can’t be fulfilled with a services operation. They force us to build the automations at each layer of the hiring process to deliver. HIRING FOR ALL WORK We have the largest comparative advantage from automating talent assessment when the ratio of time spent assessing someone relative to the time spent working with them is the highest. When hiring someone for 5 years, it’s easier to interview them manually. When hiring someone for 5 weeks, efficient matching automation creates a huge comparative advantage. Because of this, we’ve started with shorter duration contract work, but will expand progressively towards longer duration, full-time jobs as our technology matures. So, in short, the master plan is: 1. Hire people to train AI models 2. Use those contracts to learn how to predict job performance 3. Expand to short-duration contract roles 4. Hire people for all jobs Don’t tell anyone.

@mercor_ai 12-12-7 🔜‼️

Mercor is solving talent allocation in the AI economy. The difference between greatness and failure is the right person being in the right place at the right time. Putting them there is the hardest unsolved problem in capitalism. We’re excited to announce our $100M Series B at a $2B valuation from @felicis, @generalcatalyst, @benchmark, DST and @MenloVentures.

We're solving the biggest problem in capitalism, and are recruiting the world's greatest operators and engineers in order to do so. Interested in helping us create a billion jobs? My DMs are open!

Eight years ago today: 28-3 happened.

@BrendanFoody @mercor_ai Come join one of the greatest teams in Silicon Valley!

I'm hiring an executive assistant to work directly with me @mercor_ai We're the fastest growing company in Silicon Valley, continuing 50% monthly growth with tens of millions in annual revenue, and backed by Benchmark and Peter Thiel. Please apply here! forms.gle/AgWV4oYL9vn6N4…

.@BrendanFoody, CEO @mercor_ai, says AI can hire better than a human can:

"I'm really excited by the new voice products out of Open AI, I think that voice could become a new frontier," says investor @bgurley, Benchmark General Partner: