Why Datumo Could Redefine the Future of AI Training

0:00Today on the podcast, I want to talk about a really interesting startup called Dato. that just raised$15.5 million to take on Scale AI. The reason why I think they're interesting is basically what they're doing. I think the industry and all the drama behind Scale AI's recent meta aqua higher acquisition investment thing makes it a really interesting company, but also how they were able to get their investment. They actually got it from Salesforce Ventures in, I think, maybe like a very unique way. So I wanna dive into everything this company is doing and where the market is going in this area, because I think it is an absolutely massive market, as we've seen with Scale and Meta doing their$14 billion deal.

0:39There's a lot going on here. So let's dive into it. Before we do, I wanted to mention, if you want to try any of the AI models I talk about on the show, I'd love for you to check out my startup, which is called AI Box. On AI Box, I have the top 40 AI models. You're able to try all of them in one playground and essentially try them side by side to see which model is the best. We have audio, text, image. It's$20 a month. So instead of paying that for an individual platform, you get access to over 40 different models and we're adding new ones all the time. So if you want to try that out, it is AIbox.ai.

1:11I'd love to have you try it out. Let's get into the show today. So this company is based out of Seoul, South Korea. It's called Daidumo and they're taking on scale AI. What's interesting is I think most companies are basically from surveys that have been done say that they're not prepared to use AI in a safe and responsible way. I think this is kind of a funny statistic. It's also this is done by like a report out of McKinsey, basically, that kind of had these findings. I will say there's got to be some some biases in there on who is pulled inside of this. I think there's plenty of companies that say that they know how to use AI safely, but probably also a lot of executives at companies that just don't feel like they don't know what they don't know.

1:52And so maybe this is kind of the result. In any case, one of the big concerns a lot of people, regardless of that data, that data, I think one concern a lot of people have is basically understanding how an AI will make a decision. 40 % of the people that took that McKinsey survey said that they view it as a significant risk. About 17 % said that they are actively addressing it and trying to like basically figure out this problem. So this is where we are today. And this is basically where the company Dedumo comes in. They started as a data labeling company. And now they want to also help companies basically do like a safety benchmark for AI models.

2:33So that you're able to like monitor and improve your models, you're able to test what kind of responses come out. And that's kind of a new thing that they've been doing. They've been a data labeling similar to scale AI that's kind of been their core their core product up until this point. So on Monday, they just announced another 15.5 million dollars. They've raised about 28 million dollars total. And so this round, their investors were Salesforce, they had KB Investments, ACVC Partners, and SBI Investments. There's a bunch of other people. But their CEO, his name's David Kim, and he is a former AI researcher at Korea's agency for defense development.

3:10Basically, he was really frustrated. The reason he started the company is it just takes a really long time and it's very time consuming to label data for any sort of AI training. So he came up with an idea, which is basically he built this reward based app. I think this is super, super funny. But basically, it's a word based app, anyone can get on there. And you basically if you have free time, you can sit there and label data in your spare time and you get paid for it. So I thought this was kind of funny. But it's obviously a huge business. It needs a lot of humans to do this. And so they basically crowdsource this data labeling on this app.

3:45So this is kind of cool. They did like a startup competition that they won, yada, yada. But I think overall, great idea. Before the app was fully built, they actually had tens of thousands of dollars in pre-contract sales during their customer discovery phase. So obviously, they're going to talk to customers like, hey, would you use this? A ton of people are like, yes. And they are, you know, signing pre-contracts. So basically, they're like, all right, this is a great idea. In their first year, they actually passed a million dollars in revenue. So a ton of people wanted this well-labeled data.

4:19Today, they have a whole bunch of really big companies that use them. Mostly it's Korean companies. So they have Samsung, LG Electronics, LG CNC, Hyundai, Navr. There's the sole-based telecom giant, which is SK Telecom. So a lot of big companies in Korea are basically their main clientele. And I imagine they'll try to push and expand a little bit more to America in the coming years. Over the last couple of years, their clients also started asking them for other things other than just data labeling. I talked about this in the intro, but basically they're going to start doing, they're going to start helping companies to benchmark models.

4:55Right now, they're seven years old. They have over 300 clients and they generated about$6 million in revenue last year. So growing really well. Here's a quote from one of the co-founders, Michael Huang. He said, and basically this is talking about the new service that they're going to be offering, which is kind of this benchmark thing. He said, they wanted us to score their AI model outputs to compare them to other models. That's when we realized we were already doing model evaluation without even knowing it. We started in data annotation and then expanded into pre-training data sets and evaluations as the LLM ecosystem matured.

5:28So they're growing, they're adding these new features. I think basically the drama in the industry is that meta just spent 14.3 billion dollars they did this kind of like acquisition investment into um scale ai i think they bought like they they got like 50 of the company i mean honestly everyone like says like oh my gosh meta bought scale ai but it's kind of exact same thing that microsoft did with open ai for 10 billion dollars to get 50 of the company so or 50 of one of the shell companies that owns shares in the blah blah blah i know it's all convoluted for open ai but in any case, I don't see it as being super far, super far off.

6:02But when this happened, a lot of the big customers of Scale AI pulled out, OpenAI being one of them. OpenAI actually stopped using Scale AI after the whole meta deal because they're like, well, meta's our competitor and how can we trust it? All this stuff, right? So there's a whole bunch of similarities between these companies, between Dedumo and Scale AI. I think basically they have this pre-trained data that they're labeling. One thing I do think that's interesting that they're doing, it differentiates themselves, but I think this is a great business model for a lot of these types of companies today, is that they actually have some licensed data sets.

6:36So beyond just, you know, labeling data sets for companies, they have their own licensed data sets that they'll also license to people. One of those that's really interesting is it is a whole bunch of data crawled from published books. And the company actually says that it's a really good, they say a rich structured human reasoning. So they said it's notoriously difficult to clean this kind of data, but it's actually, it helps with the reasoning. So all these reasoning models, apparently reading books is a good way for them to like reason through, learn how to reason through problems, which I thought was absolutely fascinating.

7:13Unlike other companies, they have a full stack evaluation platform. So that's their Dedumo Eval, which they basically have recently launched. One of their main products is kind of a no-code evaluation tool. It's for non-developers. So people that are on policy, trust, safety, compliance teams will use this. You don't have to be a developer and really know how any of that works. I thought the story of how they actually got their most recent investor, which was Salesforce Ventures, was interesting. Their CEO said that they previously hosted a fireside chat with Andrew Nguyen, who's the founder of deeplearning.ai.

7:50So obviously very famous person in the AI space. And this was hosted at an event in South Korea. But afterwards, they shared that event on LinkedIn. And from that, someone at Salesforce Ventures actually saw it. And then they said they had a whole bunch of Zoom calls. And they got like a soft commitment. But the whole funding process took about eight months to actually roll out. and then they were able to close that. But I thought it was so interesting. It's like hosting some big famous person and then posting it on LinkedIn is like the best way for a startup to raise money. Thought that was very interesting.

8:22I may have to take a page out of their book for my startup AI box. This new round of funding that they're going to be using is basically used to accelerate their R &D. Specifically, they said in developing automated evaluation tools, they're doing this for enterprise AI and also to help them scale their global go-to-market strategies. as well as talking about a little earlier. They're very focused right now on Korea. I think that they say they want to expand to Japan and the US. They have about 150 employees in Korea. And they also have a presence in Silicon Valley. I think they might have opened like a little office or hired some people there in March.

8:57So starting to expand. Very interesting. I'm really excited about this company, to be honest. Very bullish on the data labeling side of things. Love kind of the journey where they're coming from and the industry they're going into, right? scale AI taking on$14 billion. This is obviously a very big industry. So excited to see what they do with it and how they're able to grow. Thanks so much for tuning into the podcast. If you enjoyed the episode, make sure to leave a rating and review, leave a comment or like the video on YouTube if you're watching it there. Really appreciate every single one of you.

9:27And if you want to check out AIbox.ai, it's an awesome way to save money on tons of different AI models and get access to everything all in one place. So I'll leave a link in the description to that as well, AIbox.ai. Catch you next time.