Why I Joined Mercor – Anish Bathwal

Anish Bathwal spent years as a consultant at McKinsey before joining Mercor to be closer to the answer every company was asking: "how do we use AI?"

What does a consultant actually do?

The honest answer is we get dropped into rooms where the problem isn’t even defined yet and told to figure it out. A software company wants to embed AI into a product that’s been built the same way for fifteen years. A university is watching enrollment decline and can’t tell if it’s the cost, the experience, or the competition. Nobody agrees on what’s wrong, let alone what to do. The job is to synthesize ambiguity into something actionable—fast.

I spent my time in consulting doing this across software and education. The industries were different, but by the time I left, every client was asking the same question: how do we use AI? The urgency was striking—organizations that had nothing in common were all hitting the same wall. But the bottleneck was never buy-in. It was never implementation. It was performance. The models weren’t good enough on the problems that actually mattered—the domain-specific work where expertise couldn’t be faked.

No amount of consulting could fix that. What could fix it was better data. The kind produced by real professionals with years of work experience or advanced degrees. I started paying attention to how models actually improve: through high-quality, knowledge-intensive training data generated by people who deeply understand their field. The question that stuck with me was deceptively simple. What would it look like to solve the performance problem at the source?

Then I heard from some friends in the Bay Area about a startup called Mercor. They were hiring thousands of lawyers, doctors, financial analysts and even consultants and deploying them on projects for the world’s top AI labs. The thesis resonated immediately: to build next-generation models, you need highly specialized data, and to create that data, you need real experts generating complex tasks that require deep reasoning. You could have domain experts create evals that teach a model to replicate their reasoning at scale—not expertise delivered to one client at a time, but expertise encoded permanently. Two days later I was on a flight from NYC to SF.

Now I work on web browsing—training models to persist through complex, multi-step queries rather than stopping at the first result. Think about what that unlocks: a model that can dig through dozens of sources to answer a genuinely hard question, the way a researcher or analyst would. Today’s models quit early. The data we create is designed to teach them not to. PhD-level annotators write research-grade queries at volume, paired with quality frameworks rigorous enough to ensure every task actually moves the needle against benchmarks. Researchers at the lab fold what we produce directly into their models. You can watch the benchmarks move with each model release.

The best AI models a year from now will be built on data that doesn’t exist yet. Someone has to create it, and that requires systems that can channel real expertise into training signal at scale. That’s what we’re building.

For anyone at a similar crossroads—where you’ve gotten good at navigating ambiguity but want your work to compound beyond a single engagement—I’d encourage you to check out our open roles.