Oswald acknowledges that Mercor's revenue is concentrated among frontier labs, and the company's strategic priority is building self-serve products to serve the broader enterprise market. ✦ AI generated
Oswald Nitski · 20VC · 2026-07-25 · original ↗
starts at this moment · 38:05
“Does it matter that you have such high revenue concentration? You know, the frontier model providers are your biggest customers by far. Some would say, 'Woof, that's a lot of concentration.' How do you think about that?”
So, I can answer this from a how it affects the product team. We would love to move like our biggest challenge is moving down market so that every single enterprise can efficiently run human data projects for eval and training. And that'll diversify our revenue for sure because there's many more enterprises than there are labs. And that's a harder product to build. And that's the direction that we are taking our products, taking the company — to be able to self-serve projects very efficiently, have like AI project managers so that it's a lot easier to do this work for smaller customers because running a human data project for a lab is incredibly hard. It's a white glove service that requires a lot of people on the operations team. As we make that more efficient with better products, better processes, we can do smaller projects that are more heterogeneous for more customers. It's the direction we have been heading which has reduced concentration and it's the direction that we'll continue to head as every enterprise begins to have human data work for their proprietary use cases.
verbatim transcript · starts at 38:05
38:05>> Does it matter that you have such high revenue concentration? You know, the frontier model providers are your biggest customers by far. Some would say, "Woof, that's a lot of concentration." How do you think about that? >> So, I can answer this from a kind of like a how it affects the product team. Yeah. We would love to move like our biggest challenge is moving down market so that
38:30every single enterprise can efficiently run human data projects for eval and training. And that'll diversify our revenue for sure because there's many more enterprises than there are labs. And that's a harder product to build. And that's the direction that we are taking taking our products, taking the company is to be able to self-serve projects very efficiently, have like AI project managers so that it's a lot easier to do this work for smaller
38:57customers cuz running a human data project for a lab is incredibly hard. It's a white glove service that requires a lot of people on the operations team. As we make that more efficient with better products, better processes, we can do smaller projects that are more heterogeneous for more customers. It's the direction we have been heading which has reduced concentration and it's the direction that we'll continue to head as every enterprise
39:22begins to have human data work for their proprietary use cases. >> What's so hard about it? >> Making it really simple, explaining it? What is the challenge with not dumbing down but democratizing? >> Running a human data project is just hard. Um there's so much information that needs to be transmitted from the customer as the end users of our customers to experts. And all the edge cases matter, right? So people will try