Mercor Raises $350M, Hits $10B Valuation

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0:38With RSS.com, starting your own podcast is free and easy. Upload an episode and we distribute it to Apple Podcasts, Spotify, Amazon Music, and more. Track your listeners, see where they're from, and start earning from ads just like this. If you've been thinking about starting a podcast, this is your sign. Start your new podcast for free today at RSS.com. Welcome back to the podcast. Today, we're talking about a company called Mercore, who just raised$350 million, which brings their current valuation up to$10 billion. So you may have never heard of them before. We're going to talk about the big money being thrown at this company and why, specifically, it is an important piece of the data puzzle for AI.

1:21So before we get into that, Jayden, why don't you tell them about our school community? Yeah, every single week, we record a bonus piece of content. We post it on school, and you can get access to it. We have over 300 members. It's an amazing community. Everyone comments on each other's businesses. And the purpose of it is to help you grow and scale your business or career using AI tools. This week, we recorded an entire tutorial where I break down some exciting new updates, features, and strategies to use Lovable. If you've ever used it before, we've talked about it a lot on the show. And with a bunch of new features and some cool stuff I figured out, I recorded a tutorial explaining how to use Lovable to create full-on products, websites with logins, with payment processing, um, with AI tools embedded with databases.

2:02So like you can actually have users sign up. It's not just making pretty UI, which is what I've used lovable for in the past. Um, I think I'm on the$300 a month lovable tier. I would venture to say I'm a lovable expert. It is a company that I love. It is very lovable, obviously, but, but, um, yeah, check it out. We have a really cool tutorial. If you have ever wanted to build a tool app or product SaaS, um, online, but you're not a developer, this I think is probably the moment and that it's actually finally possible. I've got a tutorial on the school community. So there's a link in the description if you want to join and check that out.

2:35All right, let's get into what Merkur is doing. This is a fascinating company. There's a lot of things I'm excited about. I mean, obviously a$10 billion valuation is incredible. Raising$350 million is really amazing in their Series C. But the thing that I think is even more interesting than all of that is the fact that what Merkur is doing today is not what they set out to do originally. And they still are like, yeah, maybe some point in the future, we're going to do our original vision, but they've completely pivoted. And I just think it's fascinating when you see companies pivot and still are able to raise$350 million.

3:04Pretty cool. So basically what they're doing is they're connecting AI labs. So like OpenAI, Meta, Google, all of the big AI companies with domain experts for training their foundational models, which means, for example, Meta might go to them and say like, hey, look, we want to like, we want our model to be a lot better at doing math or like programming or, you know, some specific niche. Can you find us like people that can help train our train our data for that specific use case? The ones that I think they cover a lot are scientists, doctors and lawyers specifically, which obviously are high value areas, right?

3:40Like if you can really fine tune a model to be really good at what scientists, doctors and lawyers do, that's worth a lot of money, right? Scientists can be making pharmaceutical drugs, doctors can be helping patients, and lawyers are working on legal cases where they can be vampires and suck money out of society. Just kidding. Don't tell anyone I said that. My grandpa actually had a big law firm growing up. So well, not growing up, but when he was, that was his career. So you know, I can't hate too hard on it. One thing that I will say is this data is really tricky to get. Otherwise, It's really hard to get your hands on.

4:13Like you can imagine one use case could be like a pharmacist, right? Like maybe the way the pharmacist looks at all of the data for drugs to come in or pharmacist receipts. The AI model has no clue because there's no centralized database, especially when it's like medical stuff or legal stuff where it's like confidential. It's not publicly available on the internet usually. And so the AI model has a really hard time training on it. So an example would be Merkur could go to like a bunch of pharmacists and say, hey, we're going to give you all of these receipts, these pharmacist receipts. or recipes or I don't even, I'm not a pharmacist, so it's a hard time for me explaining in depth what they do, which is a great example, again, of why an AI model would also struggle with that.

4:50And they would say, we want you to annotate these receipts, like take this, you know, these PDFs of receipts, highlight what each of the chemicals are, where you can find them, what they mean, what all of the short form and code words on this mean. And like, write it all down, annotate it, send it back to our AI model, then the AI model is going to scan it, and it will be able to train off of it. You need these domain experts. And this is in a thousand different industries, the same thing. You've the person that, you know, might be working on, you know, a really complex part of a machine that helps minds dig.

5:21He knows like the ins and outs of that machine, but not a lot of people do. And it's pretty hard to find the documentation online for some of that stuff. And so anyways, you get these domain experts, you have them annotate the data, and you sell it back to the big AI companies who want this rare data, basically. And that's what Merkur does. And that is how they have raised$350 million, which I think is quite impressive. That's crazy. So to simplify it, basically you have a company like Scale AI, who's more broad in labeling data, like that is a yellow banana, or that is a pineapple. But this is more specific to some of those fields that are a little bit more complex, like pharmacy, law, all that stuff.

6:05So So you're having experts in that field train the AI. Is that correct? Yep, exactly. Okay. So Merkur goes a lot more specific, a lot more in depth, and they've been able to raise a lot of money. It's actually impressive. In September, they were talking to a bunch of investors about raising at an$8 billion valuation. They ended up raising a$10 billion valuation. It just shows how important this is for the industry, but also a lot of people want to get in on this because it's making a lot of money. But what's interesting to me that I mentioned at the beginning is that this is not what Merkur started out as.

6:36This wasn't like their vision. It's just kind of what they stumbled into. They actually started out as an AI-driven hiring platform. So you can imagine like Upwork or like, you know, why can't I think of any other hiring platforms, LinkedIn Jobs? Okay, there's a real generic one. But a hiring platform that just used AI to like help match people, it wasn't really getting product market fit. They hadn't built it out fully. And then somehow they realized that these people that they were onboarding onto their platform could help with AI models. And it's kind of what they stumbled into. So they actually pivoted to providing this kind of specialized domain expertise for AI training.

7:13I think this is just a really good lesson for basically any company. No matter what you set out to do, sometimes, very often, these companies will find that there might not be as big of a demand for what they want, the problem that they want to solve as they thought. and a pivot is necessary and you can make a ton of money. So, and it's almost like they wouldn't have found this job, this like data labeling industry, if they hadn't have already kind of been working in the industry that they were in and talking to the people they were talking to. So anyways, it's just, it's an interesting thing to me that you can start out, raise obviously a ton of money.

7:44I mean, they're on their series C already. Their previous$100 million series B, they raised at a$2 billion valuation. So now they went from$2 billion to$10 billion with this huge pivot. And obviously, they kind of struck a goldmine and saw big demand. But anyways, it's a cool story to me of a successful pivot that made the company a lot of money. And even if they're not doing what they originally set out to do, they're obviously crushing it. Yeah, totally. Well, I have a question for you. Do you think there's... Because I remember back when we started this podcast over two years ago, we talked about one of a potential side hustle being getting paid to train AI models with data.

8:26You know, do you think there's still any room for that at this point? I mean, it sounds like some of those specialty fields, but like, what if you're really good at underwater basket weaving, you're like the best in the world at it. Do you think there's an opportunity or will be in the future to help train AI data or not so much at this point? No, I 100 % think that there's like so many untapped data sources, right? Like basically the AI models, people are like, oh, they plateaued. They like, you know, they're not getting that much smarter than like, you know, chat GPT-5 wasn't that much smarter than GPT-4-0, right?

8:56Like you hear that kind of sentiment a lot. I think it's because we've kind of hit a max on like the mass amount of publicly available data that's very easy to access. But there's so much like hidden data that just needs to be mined. So I think about it like mining and that's what Merkur is doing. They're like mining this data. It doesn't exist because half the time it's like regulated. It's illegal for, you know, them to publish like patient documents for medical stuff, obviously. There's like HIPAA compliance and all that kind of stuff. So you have to like mine it. So sometimes that's synthetic, but also a lot of times you need like an expert in that industry to go and create that data set.

9:32I think that there's a lot of places where this still exists and that you can go find it and make it. And yeah, if you have a really unique skill set of something that you know how to do better than anyone else, especially if you try to ask Chai Chippity to help you with it and it does a bad job. Like, it's kind of interesting to me. Anytime someone's like, oh, yeah, I tried to get Chai Chippity to do X, Y, Z. And like, it wasn't very good. Like, to me, I wouldn't say like, oh, cool. I would say, oh, well, that's a great opportunity. Where could you build a data set for that? And then who could you sell it to?

10:00There's data set marketplaces, I think, which are interesting. You could have companies that are training it at scale like Merkur's doing. But I definitely think this is a big industry that we'll probably see more and more of in the future. Yeah. I'm just wondering if there's a side hustle marketplace where I could sell all my different side hustle skills to make money on the side. I don't know. It's an interesting concept. you know yeah i haven't dug into that too much but i do think it's i do think it's interesting it could be uh definitely could be something crazy the other thing that's crazy that i want to bring up is the fact that um merker right now they're on track to hit 500 million dollars in annual recurring revenue um that's faster than any sphere did it which is cursor which hit that about a year so like this is one of the fastest growing companies now the thing is i kind of like i don't want to say i hate this but like the thing that i think is there's so much money here okay So there's so much money here.

10:52But the thing that it like irks me about this is Scale AI did the exact same thing. And everyone touted Scale AI as like the greatest thing since sliced bread. But they only have a handful of customers. And so when you only have like three or four customers, like let's say five customers, Microsoft, OpenAI, Google, maybe NVIDIA, and like maybe Anthropic, right? Like five big companies that are buying all of your annotated custom data for their big LLMs. and they're spending insane amounts of money. Like that's how they're getting$500 million in annual recurring revenue or whatever. It's not like there's, basically if one of those customers pulls out, your whole company can get pretty wrecked, which is what happened to Scale.ai when Meta invested billions of dollars into Scale.ai and took their CEO.

11:37All of a sudden Open.ai and I believe Google both said like, okay, we're not going to use Scale.ai anymore because like Meta's getting involved and owning it and now they're a competitor and blah, blah, blah, blah. And then like Scale.ai like lost a lot of customers and money and stuff. So anyways, it's amazing that they have$500 million in annual recurring revenue, but they don't have an insane amount of customers. And if any of those pull out for any reason, then they are losing quite a lot of that percentage of money. So yeah, I think that's important to bring up. Yeah, no, that's a valid point.

12:06I think, yeah, it'll be interesting to see who comes out on top. I didn't really realize that about Scale AI that they had lost business because of the whole... But yeah, crazy. Hey, if you enjoyed this episode... Oh, wait, I got one more. just one more tidbit that I have to throw in there and then let's wrap this up. And that is that they are currently paying$1.5 million per day to their contractors to annotate this data. They have more than 30 ,000 experts and they're earning over$85 an hour on average. So you actually, if you are an expert in an area, maybe that's a good side hustle to bring up is you can actually go and work for Merkur and help them annotate data in your specific field for 85 bucks an hour.

12:47I feel like it's pretty good. But again, they're working with like doctors and lawyers and stuff. So those people are already used to billing out a lot of hours, but maybe if they have extra hours, there's some extra side hustle money to be made doing that. Anyways, Jamie, you can wrap us up now. Well, hey, thanks for listening to this episode. We really appreciate it. And if you would leave us a rating or review wherever you're listening, we would really appreciate that as well. It gives us good feedback in order to make this podcast better. So and be sure to check out our school community if you are interested in taking your ai skills to the next level learning how to actually make money online using ai and build your business as well thanks for listening and we'll see you next time and if you're still listening even after we wrapped up the show here's a bonus easter egg for you which is that if you leave us a rating review sometimes we read them live on the air i wanted to read one uh and give a shout out to the person that left it which is johnny reviews everything i believe he's got a youtube channel you can go check it out uh he said hey it's johnny from johnny reviews everything just wanted to say how much i appreciate what you guys are doing with AI Hustle as someone deep in the influencer and content strategy space and borderline obsessed with AI.

13:50I've listened to a lot of creator focused podcasts and this one actually delivers. So anyways, he has a whole thing. He joined the school community. He's awesome. If you want to go check out Johnny Reviews Everything, wanted to give him a shout out. If you have a cool project, leave it in a comment in a review on Apple or Spotify and we might read it live on the air. Thanks so much for tuning in. We'll catch you guys next time. John Daly here. You know what day it is? Neither do I. I live life daily. That's why I only spend on Moto Casino. Moto's got daily free bonuses, daily tournaments, new slots added daily.

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15:03Track your listeners, see where they're from, and start earning from ads like this, even with just 10 listeners a month. If you've been thinking about starting a podcast, this is your sign. Start free at rss.com.