Mercor sells training environments, not just expert access

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 5:37

   Wow. I mean, for some people who don't understand the business model, going out to the broader umbrella of Mercore, you're not focused on GMV. It's not a marketplace. So how does the business model fit? Yeah, so I think the largest misconception about the business is that people think about us as a marketplace like Uber, where we would just match experts with a job and then take a fee on that, when actually the experts are one component of the data sets, the RL environments that we deliver to the labs.

2. 6:11

   And so an RL environment includes three parts. The first part is all of the apps, which are like clones of Google Workspace and other popular applications that people would want to teach agents how to use within the economy. The second is the worlds, which is all the content within those apps. So think the messages, the slides, the docs, the sheets, etc. And then the third part is the tasks, which are prompts and rubrics, similar to how a professor would create a rubric to grade an essay, where the model can attempt the problem and then get a grade on how well it did that it's able to learn from.

3. 6:49

   And the barrier to automating the entire economy is how do we cover the full distribution of all of the RL environments that mirror everything in the distribution of the economy? So cover all of the different apps people use, all of the different worlds, and all of the different tasks. And so ultimately, these like RL environments is the end data that we deliver to our customers. And the experts that work to populate the worlds and the tasks are one ingredient, one moat that has been a foundation for how we do that effectively and scalably.

4. 7:22

   Obviously, a lot of this is predicated on models, but how has the advancement of agents coming into the entire AI system changed and maybe increased what you're doing? Yeah, I think that one of the large benefits is that agents have made it much more productive for someone to spend time creating data and training an agent. And so as an example, like, say, a hour of someone's time would previously get x amount of product model improvement. Now, an hour of their time might get three to five X that because they're able to use an AI co-pilot to make them more productive and how they build out all of these data sets. But ultimately what's happened as we've made the experts more productive is that it's been this case of Javon's paradox where the demand just expanded exponentially because ultimately like 99% of knowledge still

5. 8:24

   lives in people's heads. And if we just make it more effective and productive to have humans teaching models, that just increases the amount that people teach models throughout the economy, because it ultimately ties back to the principle I mentioned at the beginning, which is that it doesn't make sense for people to do repetitive work. They should just train the model how to do it. And that's a far more scalable and productive way to contribute to the economy.