Partner payment failures pushed Mercor to own expert payouts

Transcript

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

1. 2:26

   down how Mercore is operationally structured. So I know you want to talk about reinforcement learning environments a bit, also known as RL environments. So if you break that down, how the products of Mercore work, how does it all fit in? Yeah. So there's broadly three main products that we have to produce a given data type. The first product is what we built initially, our talent platform, where we have over 5 million people actively looking for work, pay out over $4 million a day. 70% of the people come from referrals. So there's a lot of network effects associated with how we can mobilize software engineers, doctors, lawyers, etc. for any specific type of data that we want to create in the economy. From there, we onboard those experts to Studio, our data platform, where they have an AI copilot and all of the Agenda QC and automated

2. 3:21

   capabilities for how they would create an RL environment, populating all the context that mirrors what they would do in their job, all the tasks, all the verifiers, etc. And then the third platform is our AI project manager, where we basically automate all of the manual work associated with how we would manage all the experts on these projects. So similar to how we We would have project managers setting KPIs for people, saying who's doing a good job, who's doing a bad job, giving them feedback.

3. 3:52

   We now have agents that are autonomously running these projects, hiring people, determining the data quality and all that. ultimately being accountable to the customer outcomes. So you're doing $5 million in payouts a day? Yeah, not well over that, yeah. How does that work? It uses Stripe Connect as the back end. I love how you go right into the infrastructure answer. The team is spectacular. I mean, it's been incredible because one of the things we realized early on is we were working with another player in the space that was having a lot of payout issues very publicly where we were providing talent to them.

4. 4:35

   And we got flooded with support tickets that we'd hired over 1,000 people for them, people often in third world countries. And they were not getting paid and struggling to put food on the table for their families as literally we were receiving many support tickets about that. And so really, that was one of the large catalysts for we need to have a direct relationship with the labs. We need to be able to be accountable for the experience of the experts on our platform and making sure that they get paid on time. And we need to over invest in how do we have a world class payments team that never makes mistakes and always make sure that people are paid out.

5. 5:14

   And so to date, we've, you know, paid out just about a billion dollars historically, which is on a pretty steep ramp. And we haven't had any major issues or of that sort. And so it's been an incredible process of how we scale the payments team.