This $1.5 Billion Ruling Just Exposed AI's Next Bottleneck.

A $ 1.5 billion dollar settlement shows data has real value and it could be the next big bottleneck for the AI growth story. Joining us today is Dan Nias, the CEO of Mode Mobile, who is an expert on the data economy. I'm so excited to dive into this topic today because every retail investor is looking for that next big growth investment area and data in the AI story is such an interesting area where really that growth and that value is just starting to be understood and this brand new lawsuit really goes to show there is true value and some really large dollar value being placed on the data used in these AI models. So, let's start there, Dan. Let's talk about this brand new settlement that was just announced this week. >> Yeah. So, what we found out today, uh, and this is a pretty crazy story. So, Anthropic, the company behind Claude, you know, one of the most popular, uh, AI models out there, they bought something like 7 to N million books and created their own personal library. And the goal of buying these books was essentially to take page by page and suck up the content of these books. up until recently um that was kind of viewed as like fair use which essentially doesn't mean that it infringes on copyrights. But what these courts recently said essentially is that it actually does and then they had to pay out a $ 1.5 billion settlement to all these authors of these books. Now what this means for uh data right and you know as the saying goes data is the new oil and I think that this is uh opening up the human data market is going to be a1 trillion dollar market in by 2033. And so I think one of the things that's uh interesting about this story um it shows something that has pretty much been okay on the internet over the last like 25 years which is essentially hey big tech has all these platforms. They're allowed to suck up any internet uh information they want to. You are using these products and if the product is free it means that you're the product. And so essentially they're able to get smarter because these models are essentially training on your information and you did not get a scent of it, right? And they made trillions of dollars. And so that is changing, right? And it's these court rulings that are happening. And this isn't just in the United States. This is also happening in Germany. Similar to what happened with data regulation with, you know, GDPR and the California Privacy Act. So consent to data really is one of the biggest uh hot topics that's coming. And because this is an arms race, right? This is the most important time. And it's a it's a crazy time to be alive, you know, as a as a person and especially as an investor because we're in the midst of the modern-day industrial revolution and data is at the forefront of it and, you know, happy to dive into that, but it is uh it is definitely a historic day for, you know, the data market, I would say, you know, with these some of these rulings. >> Yeah, I think this this ruling that just came out, I mean, it really goes to show that this is going to continue to be a legal nightmare for many of these AI model companies that have really just been taking all of this information. And you think about many of the biggest hyperscalers out there. Well, they already have access to a ton of information. You have Google, you have Meta, you have even Amazon having all this consumer information at their hand at their fingertips. And that is a large part of what made their company so successful. And so looking at data as a form of how to monetize it is a really interesting conversation. And I think I want to start with what companies are actually doing this. Are we seeing a market for it now? Are we at the very early stages of trying to monetize on all of this human data? >> Yeah, I I definitely think that we are in the early stages for a couple reasons. The first is people are just waking up to the fact that their data is actually worth a lot, especially in the AI era because you have to really think about it. So the AI models today are pretty smart, right? Uh but they still hallucinate. And so now we're in this uh you know period of time where essentially open AI anthropic have sucked up the entire internet over the last like 25 years but now in order to get the next generation of the models they need to refine the learning of those models. What that means essentially is like they need to fine-tune. Like if you think about what's happening in the space, if you ask chat a question, say we're going to talk about astrophysics or something uh really insane that you know or nuclear reactors, someone has to know the answer of that, you know, and and so what happens is there are companies out there that are actually getting uh professionals that are you know uh physics uh you know masters and they're paying them to basically give refinement learning to these models and then the models get smarter and then they learn. But you have to essentially reconstruct the entire internet with these AI models. And it's not just in the uh chat interfaces. Like if we talk about humanoids and robotics, the way that a robot learns is that you need millions of hours of videos of you doing laundry at your house or uh cutting up vegetables because the next generation of robots will be trained on millions of hours of human data uh in order to do those mundane tasks. But you have to really create everything that humans know how to do. And the outcome of this is it's it's trillions, tens of trillions of dollars that will change. And so that's why it's such an arms race. And because consent to data is so important now, there's a big opportunity for consumers to make a lot of money uh from their data, right? And so I think that that's what is most exciting. And you're seeing this in the business models. Reddit actually recently licensed its data for AI training. So all those comments on Reddit for $60 million a year to Google, I believe, actually to the their major model. And so $60 million a year off of content that consumers are making on Reddit, you know, all those posts and all those likes. And that's a pretty big business line because it's 100% gross margin to Reddit. And so this is just one example and there's many more of it. >> Yeah. And we're going to get into how Mode Mobile is kind of using that uh consumer economy for data and giving it back to consumers. We'll get to that too, but there's so many things to unpack from what you just said. I think starting with the robot conversation is a great one because we already have AI models and and people out there who maybe use AI themselves can tell yes there's lots of hallucinations. Yes, there's a lot of madeup information or things that aren't quite human patterns um that really you can tell it's AI and I think AI models are constantly working to to improve and be less noticeable about that AI versus human disconnect. And so let's talk a little bit about how big of a deal robotics is going to be in this human information stage. I think robotics is a topic that our investors are very interested in right now. How big of a role will data play with robotics developments? >> Look, there's three things that make up the an AI algorithm, right? It's the uh chips, right? That is uh necessary. The compute, right? And that's why Nvidia is a I don't know what it is these days, five, six trillion dollar company, right? Um and then we have the algorithm. So that's actually the software of how all this works and operates. And the third component that people are waking up to, right, is the data piece. Without that it would not work. And so when we start thinking about things like robotics, when we start thinking about things like agents, right, which is essentially are kind of virtual agents that are going and doing these tasks, right? It could be an operator, it could be a developer, um, you know, it's very nuanced, but whether it's robotics, whether it's agents, you essentially will need more and more consumer data. And usually this is stuff that is very refined because think about this like in order to have a robot at your house to understand how to do those mundane tasks that I was just talking about. You need millions of hours of what's called egocentric video. Egocentric video is essentially when your phone is here and then essentially it's like from the first person point of view like the ego of this person, right? And so you have both hands um like doing things and it trains in all these different environments and I'm talking about per task. You need millions of hours per task and you need to recreate that in a bunch of different modalities across different careers. So think about of a repair man that is doing tires. Think about uh a manufacturer worker. Think about, you know, a fireman. Robots will come and it'll come over the next decade. But in order for them to get to where they need to be, they need new data to refine the learnings because there's only so much video out there that's egocentric of people chopping up vegetables or of people doing laundry or of people sweeping. And so it's such a massive opportunity and the companies that are supplying the data, there are some companies that are multi-billion dollar companies that have been supplying this data that are newcomers into the space. Um, an example of this is Facebook, right? Facebook bought a 51% stake in a company called Scale AI, not a publicly traded company. That company was started 5 years ago and uh they bought that at, you know, a $29 billion valuation. There's another company called Merkore which is a you know essentially a marketplace that effectively brings those uh specialized people those those physicians those physics uh those investment bankers to refine the learning of these AI uh agents or sorry these AI models and that company went from $1 billion in ARR to $2 billion in AR in four months this year and the they it was founded by four 23 year olds right like very young kids that are starting that and so this space is really moving and the companies that have been kind of operating in the space and really understanding that attention are the most important things that we own in society um are the companies that are going to be the big winners in the space. >> Yeah, the attention and data piece is so huge for where people are all at right now and that is on their phones and on their computers and and constantly doing things and we're going to get into why that's important too. I have one other question just curiosity question to touch on from your original answer and that's talking about security and business is realizing that so much of this information is going into these AI models. We just had a a video just a couple weeks ago talking about Palunteer CEO really talking about how open AI and anthropic um are both risks for you know capitalistic companies corporations because if you want your data and your information to be proprietary if you're putting it all out on these models you're giving away that information. I'm curious to see how big of a deal you think that story is going to continue to evolve as more and more companies jump on board with these AI models >> as a business owner, right? Uh yeah, when we're using these services, some of them do have enterprise plans where they say they can't train offer data. It's a setting that you have to set and yeah, obviously we don't want to give that information for free because that's worth something. You know, one of the biggest things that's happening and this is over the last two weeks is there are companies that are paying for companies data corpuses. That is what so what that means is think about how a company operates when we go and try and make a decision right you know it's like oh we need to do this product feature then there's a a ticket that's created then these developers will code that then uh there's some sort of problem and then there's all these communications that happen in your internal work communications that could be through email it could be through things like Slack um but ultimately a decision comes from that now think about that compounded over 10 years of a company's existence across hundreds of employees it's literally like a ton of information There are companies that are paying for the data corpus of companies millions of dollars in order to train the next generation of agents that will then go and be able to be the HR recruiter that will go and be that developer that will go be that chief operations officer because it's a much larger market for people to be able to take the careers um of some of these uh kind of mid-level and senior level kind of executives and the companies to operate smoother. So that's what they are essentially doing. And so your data is worth a ton of money. And then when you put your data out there and you see claude or open ass saying, "Hey, uh, so and so, hey Bridget, come and give me your health data and we're going to give you this great longevity report." But you're effectively signing up to give something that costs money that they would actually have to pay potentially hundreds of dollars for and you're just giving that for free. And so that's a huge deal. And I think that's what he means by that where anytime that you upload your finances, anytime that you upload your health data, anytime you upload anything that's valuable, you're effectively giving away free money that otherwise should go back to you and definitely should go back to your company if you're a business owner. >> Well, let's get right to that and talking about how Mode is helping people gain back some of the the value that's in their information and in their their habits, their patterns, too. >> Yeah. So the premise of mode and and where we started was really this idea that people spend when we first started the company I think it was 39 hours now we're getting closer to 50 hours a week on their smartphone. Um to put that in context Bridget if you sleep 8 hours a day there's 112 hours left. Okay so if you were spending 40 to 60 hours a week on your smartphone you are spending one half of your waking life on this device. And when you think about what you're doing on the device and whether you're scrolling, buying things, uh you know, responding to messages, um you are essentially being served advertisements, you being served a variety of uh different uh data points that you're essentially feeding into these platforms. And who's making all that money? And so essentially our whole thesis as a company was if people are going to spend this much time on this, uh certainly there's an opportunity for them to get some of that money back and then you can make the smartphone freer better. So that was really the foundation of where our company started and then we've extrapolated it into a ton of other opportunities where we now acquire other apps and have a very similar business model where some people can't afford to pay for a premium service and we can basically like hey if you give us this insight or you do this task we will give you um our service for free and so far mode has been able to facilitate over a billion dollars in earnings and savings back to consumers uh through this business model and we've scaled to over 100 million monthly active users across our entire ecosystem all with this idea of being built alongside everyday people. >> Let's talk about how that business concept that you have ties into this data economy and how so many companies are willing to pay for um and really put value behind this data and this information. >> Two years ago, um this is before like I think Chatrib4 came out, right? I think that was like the big internet moment where people like, "Oh my gosh, like this is amazing." There wasn't all these rules like this was like the wild west, right? And so essentially these uh companies were just sucking up data as they always have, right? the Facebooks of the world and the Tik Toks, etc. And then people woke up and it's like, hey, like this is like potentially the greatest technological innovation that we've seen in 300 years. Um, and this needs to be controlled and people need to be able to consent to this because these companies are essentially taking advantage of people. And that's where this whole thing is coming with consented data, right? And so, um, that's where we're really excited. And we've always been in the business of rewarding people for their attention. And we use data in order to reward them for their attention. But then we started really thinking, hey, like we could do a lot more in the data space. So we could let people share their data and that and whether that's filming videos of themselves doing uh those chores at home, if they want to do that, that's great. If they want to share their wearable information, if they want to share what they're streaming on Spotify or Netflix, they can do those things. Everything's optin. The whole idea really is give power back to the consumer. Um, and there's a lot of money on the table here, and companies now have to pay for it because of rulings like the one we saw today with Open AI. There's another major one that the Wall Street Journal is suing all the major labs from ripping all its work over the last, you know, couple decades. And so, this is something that you're seeing over and over again, and you're not going to see less of it. You know, the it's only going to get more and more regulatory pressure. And so, companies will have to have a budget of getting that consented licensed data. And internally, we are seeing a lot of that in terms of the companies that we're working with better than getting this data as well. >> Yeah. Let's talk about what all of this means for investors because I think you're making a really good case for this being a bottleneck for the growth of AI, the growth of robotics. Let's talk about how investors can benefit from knowing, okay, we know that data is starting to have a real value. It's starting to have a real legal implication for a lot of these AI models. And I I think investors might be thinking about at least those who watch our channel regularly. We had talked about this similar topic maybe six months or more ago talking about a company called Datava. DVLT is the ticker there. Um and that one struggled a little bit but a similar concept of looking to find value in the data that is used for these AI models and for the growth of the AI story. So for investors uh what's your advice for how to really take advantage and get ahead of investing in this area that's still very early in its uh growth story >> when people started really noticing that hey AI is here to stay what did they all go and do let's go buy Nvidia stock and then Nvidia surged from like a trillion dollars to like four in a very quick period of time which is pretty insane and became the most valuable company in the world um then people especially investors started thinking okay we have all these chips what should we invest in And then they're like, "Oh, like we should invest in energy stocks. We should invest in rare earth minerals. We should invest in everything that makes up that's needed to run a data center effectively, right?" And so now there was all these like tertiary investments that now people are thinking about. It's like really the picks and shovels example. That last piece of the pigs and shovels that I feel like people are still uh haven't the alpha here is those data companies that are providing that data, the human data to these models. And that's why I'm saying that this market is going to grow to a1 trillion plus dollar um market opportunity here in the coming years and there's going to be some clear winners in the space especially the companies that are diving heads first into the space. Now if I were investor no matter being biased in my own company um the things that I would think about um is I would look at how far is this company in its journey because this space is growing and it's moving fast but it is very finicky you know because one week it might be really big for the AI models to get uh workflow data another week it might be big to get egocentric data kind of like the videos I was just saying another week they need image refinement and so the con the market's always moving and so it's hard to have consistency in the revenue The other problem that you see in this space is that there's a lot of revenue concentration. And what I mean by that is that effectively there's like seven big frontier lab models. And so you only have like seven clients. Now they have unlimited money, but you only have a certain amount of clients. And any business owner that's revenue is dependent on two or three clients. There's risks to that business, right? So they could be doing great one year and then suddenly their revenue is contag. So what I would look at is how much revenue is this company doing? Um are they profitable? um do they have other business lines and other streams of income that are not just data oriented? So for us that's what we've always focused on is you know we earn money through you know subscriptions and advertising but we also now have this data practice because we're sitting on so much information that has been just within our company and now we can go work with those providers and we have way better unit economics because we are not dependent on just data revenue. You know we make money on multiple different places. So, as an investor, these are things I would look at. And, you know, I believe uh D uh or data vault was the company. They're still kind of newer in the space if you look at their earnings uh you know, ratio essentially of where they're at. And so, those are the type of things that I would look at if I were an investor investing in the human data market these days. >> Yeah, I think it's important that you call out that there's a lot of new companies entering the space because it is still very early in the story and we know that whenever there's a new area of a massive development, not every company's going to win. Some are going to lose. they will not all still be here 10 years down the road, but you're going to see a lot of names entering the space. And one thing that we see when there is a new area of growth in any part of the AI story that's that the hyperscalers, the ones who have the biggest pocketbooks often times just acquire and and eat up some of these companies that are doing this data farming or or whatever, however it is they're getting their data or they just build their own. Do you think that those large players are going to be a part of kind of shaping what this data economy looks like in the future? >> Yeah, and and that's actually already happening. I mean that I think that example with Facebook and Scale AI is a perfect example. So this company Scale AAI was I believe founded in 2017 or 2019. It wasn't it's not too old of a company and they actually were one of the first uh vendors that were doing what's called like data labeling. Think about a capture. You know those sites that are like hey I like we call all the traffic lights here. Um and so that essentially is data labeling. And while you were thinking that you were doing security to get into a site to make sure that you were an actual human, not a bot, really what you're doing is data labeling and helping these models get better. And so this company was doing that, but hiring a bunch of like gig workers that can basically say, is this a cat or is this a dog? Is this a hamster or is this a cat? And then all these humans are doing that for like a few cents, you know, essentially. And uh they scaled that business and it got more sophisticated as these models got better. But what happened was about a year ago when Meta bought you know scale it was actually contracting with all the other metadata competitors because all these big companies are have their own models and essentially every other frontier lab was like we don't want to work with scale anymore because now Meta owns them and so there's a conflict of interest and so what's happened is now um all these frontier labs are actually working with multiple different companies to collect their data um because they don't want to have that same risk happen again where one of the big guys buy someone else and then suddenly who was supplying that data no longer can supply that anymore. And so it is definitely an arms race. It's not necessarily a winners take all, but if you think of like what happened with the social networks um or like with Google and Fang and all these companies, it's like there are going to be some clear winners and there's not room for like 50 of them. There's probably going to be like five clear winners or maybe less. But the the stakes are high. I mean, this is again like biggest technology uh impact since the the industrial revolution, you know, I think bigger than the internet, you know, at this point. >> Yeah. And I think that ruling from the courts for Anthropic just this week goes to show that there's high dollar tag value attached to data and information. I think like you said, we're going to see a lot more of that. So I think it's something for investors to pay attention to. Always watching where the money is flowing and there is absolutely money being attached to the value of information and data and you can't ignore that. I think that's one thing I want to touch on too is your thoughts on timeline really the the whole economy the the court system catching up with the value of data and that becoming a true uh large commercialized part of the AI story. >> There's a company called Cloudflare. Cloudfare helps power about 1/5if of the internet today um through you know how it works with you know publishers and things of that nature. The amount of traffic uh from AI bots and agents surpass human traffic to sites. And so what that means is what are those agents and bots doing? They're doing tasks for people that are operating, you know, the AI itself um or they're crawling the internet for new information. So on September 15th, Cloudflare is blocking that traffic so that agents can no longer do that for the sites that it powers. What that means is that people that own those websites as publishers, companies like Market B for example, will suddenly now be able to better monetize their data and their content. And these AI agents won't just be able to strip your content and put it into its its uh its algorithm which creates a new income opportunity for sites like market. This is happening not just on the human level. It's happening on the company level. It's happening at every level, right? And so I think that that's going to happen much much faster. I would say hopefully over the next 5 years uh we'll see these things actually really materialize at scale where it becomes like a household topic in a day. >> I'm curious to hear from our viewers how many people have heard of mode mobile or maybe are even using one of their phones. Let me know in the comments. And Dan, thank you so much for all the information today. Investors who are interested in your company, you're not quite public yet. Let's talk about uh investment opportunities for Mode. >> Yeah. So, uh yeah, Mode's a unique company. As I as I mentioned earlier, uh you know, our our our thought process is really, you know, built alongside everyday investors and consumers. And one of the things that we've done very uniquely is um we've been a company that's primarily funded by, you know, it it user base and and everyday retail investors. Um, so MO today has about 64,000 shareholders, more than most publicly traded companies. And we've raised over $und00 million uh through what is known as reggga crowdfunding. So you have to get qualified by the SEC. You know, have audited financials. So it's kind of like it's it's a preipo kind of opportunity, but it's almost like the minor leagues before going public. And you know, we have our ticker symbol reserved and that is our intention is to take this company public in the markets. Um, but the whole thesis really has been, you know, if we're taking a step back like, and and this is really important for your audience. When SpaceX went public, the first time that retail investors got a chance to invest in it, for most people, I'm not talking about the qualified funds or even accredited investors that invested through like some SPV of this the cousin of this guy, you know, that got an allocation, it was at a $1.75 trillion valuation. All the alpha's gone, right, at 1.75 trillion. So, who made a lot of money? Those early investors, those VCs, and all those people. If you think about what's happening with Anthropic and OpenAI today, these companies a month uh you maybe five months ago were trading at 300 billion dollar valuation and now they're talking about a trillion dollar valuation. And so by the time that goes public, all the alpha's gone and so our whole thesis has been allow investors to take part of your uh journey along the way. And we've been main street before Wall Street. Not that we have anything against Wall Street. We want to obviously get institutional investors involved and you know grow the company to a outsized outcome. But really, it's been like, you know, can we do something different and build alongside everyday people? So, if they want to learn more about it, they can go to invest.modemmobile.com and uh they can learn a little bit about uh what we're doing and our mission and we'd be happy to answer any questions if people have it. You know, we have investor relations so that they can reach out to as well. >> Yeah, we've got that QR code on the screen and a link in the description for those that are interested. And if you want to learn more about private investing and how that can work for investors like yourselves, we did a whole video on private investments just a couple of months ago. Make sure to watch that whole interview