The Exchanges

Q Awesome. So let's say you're quite successful here, right? Let's say you're investing in lots of very successful companies. Good problem to have Does that kind of change the composition of what LG is? Because you're gonna have, like, basically stakes and lots of, like, very promising new companies, and so where do you go from there?

A Yeah. So, let me explain to you why we even started this from the beginning, more specifically than just a vision. So, LG is a, uh, is a, uh, is a collection of, uh, many, many large companies. Actually, LG Electronics is just one of them. LG, you know, LG Umbrella, there are about 64 companies. It changes every year plus minus, but there are about 64 companies, including, including LG Chemical, uh, you know, LG Energy Solutions, and, and all these things. Even, they even have a telco in Korea. Um, there are vastly, vast, the vast majority of those companies are hardware and manufacturing focused, ok? Our job, my job, is to create six fifths and six sixth of those multi-billion dollar companies that are very different from the composition of LG that they have right now. Okay. Much more software focused, much more service oriented businesses. So you ask the question, uh, will LG's, uh, you know, business composition change? Absolutely. If you're successful, and that's exactly what it won't happen. Doesn't mean manufacturing is going to go away. It's going to still be there. It's going to grow, but we're going to have additional and new, uh, uh, business lines that it's going to probably grow much faster.

AI assessment note: “will LG's, uh, you know, business composition change? Absolutely.”

Q So what does LG bring to the table then?

A So we have, uh, so, uh, money, obviously the investment is one piece of it. Startups will be happy with that. We also have, yes, most of the, uh, the partnerships we get into with the startups, it comes to the revenue sharing. So whatever we do together, uh, they get a cut, we get a cut. So that's how usually it's structured. I mean, some startups prefer straightforward licensing. We are happy to discuss about that, but most of the cases we're going to Grow with, because we want to grow with startups together. We come up with some kind of a revenue sharing agreement. So third, this is actually huge. LG, and it's not just LG, LG Nova has established a lot of, ah, ah, ah, the partnerships with, ah, ah, large corporations, non-LG corporations, and also non-LG organizations, including, ah, West Virginia, a state of West Virginia, which is, which is, was a huge deal. So when startups work with us, they become the first customer. So for startups, usually I did my startup myself, and there are three things they struggle, and those three things are critical in the success of startups. So first, obviously money. They need money. Second is usually the first customer. You need the first customer, and that's really, it sometimes makes or breaks, uh, that makes breaks the, the success of the startups. So, okay. We provide opportunity for the first customer to those startups, along with us, …

AI assessment note: “money, obviously the investment is one piece of it... revenue sharing”

Q There's like the clearly defined ones, but we hear all the time. I mean, AI, you're on AI, but we hear all the time. Metaverse and, you know, this and that. So what do you think is quantum? Um, I don't want to give you the answers on this one, but I'm just kind of curious. What do you find to be like the most promising new area of technology?

A Yeah. I think Alex, uh, I get that question a lot. Okay. I always, uh, respond as saying that You really need to look at the business areas, not technology areas, because at the end of the day, all the technologies are enablers, from my perspective. And, and whether this is a, a really promising technology or not, it's not decided by the technology itself. It's decided by how and where you apply. So, for example, AI, yeah, is the interesting technology. Everybody's on it. Great. Um, AI itself, will it make money itself, the model, and LLM? Uh, some will, I believe, but not in the scale that we're looking at. Really, it's going to come down to where we apply the AI, and it doesn't matter which sector it is. It could be healthcare. It could be energy. It could be retail. It could be anything, but where can you create this step function? Where can you create this quantum leap by applying this I think there's words when it comes down to. So, and the reason I can tell you this is because I went through probably almost similar process when I was working on IOT, Internet of Things. I mean, everybody, 20 years ago, thought that was going to be the game changer. And it was a game changer in a sense that if you look at your home today, I don't know how many. 20 years ago, I didn't have any sensors at home. Now I have probably 50 sensors at home. You know, you just, you just creeped in, y…

AI assessment note: “You really need to look at the business areas, not technology areas”

Q practice, that's hard. I mean, that is hard work. And I don't know, to, to be honest, if there's like a natural way to do it from within a company that's as large as you say. So how, how does the process work? Okay. Do you, are you funding startups? Are you starting products within the company? Uh, what exactly do you do to bet on this net new technology?

A Yeah. So we do everything we just mentioned. However, we combine those different pieces into a streamlined process. I think that's probably what is the most unique perspective and unique point of LG Nova. So, LG Electronics, again, is a huge, large company, but they are very much hardware-minded. It's because the manufacturing is the bread and butter of LG Electronics. So, it is not that natural to think about doing non-hardware businesses as a company, as a whole, and there are probably less expertise on non-hardware businesses Uh, uh, than, uh, expected or than, uh, ideal. So, uh, when he created this new division, and I was first, I joined LG only three and a half years ago, so I'm new too. Um, so when I came in, I looked at what I should do, and, and LG actually asked me to design the process, asked me to think about what you should do to make it work, because traditionally, Ah, these hardware companies have a hard time transforming themselves into non-hardware companies. So, what he thought about was pretty simple. Ok, we may not have all the expertise in-house because LG's hardware company. We may not have all the, ah, the collaborators and partners that we can get to because we are a hardware company. Let's get that from outside. When I say get that, we're not saying that we should bring them into our umbrella as a whole. It's more like, let's find a Swiss bot. That LG c…

AI assessment note: “So we do everything we just mentioned. However, we combine those different pieces”

Q to run ahead pretty fast. And we use technology sometimes to paper over the problems. Um, and also, there's a pretty good ROI for a business if you can solve something, so generally, that's where new technology tends to see the return on investment first. So Saku, I'm curious to hear from your perspective, the enterprise opportunity in AI, is that bigger than consumer now, and how big is it?

A So I wouldn't say it's a bigger or smaller, but I completely agree to what Norma said. Uh, AI is essentially an enabler. That's what I call enabling novels. Usually, enabling technology does not make a lot of money. I mean, there, so if you go back to, uh, uh, sort of like a, uh, early days of internet, so there's a lot of investment in the fiber optics. So I kind of see this current buildable infrastructure in AI as so like at the days of fiber optic. They invested tons of money, and they eventually end up with, ended up with over-topacity. It took multiple decades, after 10 years or more, to get to the point they find the right application, like video streams. So, at MIPA, eventually, there will be a lot of the application-focused startups or vertical-focused startups that leverage, say, I think that's sort of really real market is, but I also understand, without infrastructure, we cannot get there. So, I think we are in that kind of journey of

AI assessment note: “So I wouldn't say it's a bigger or smaller”

Q Look, all sounds good in theory, but We want to know where, what happens when the rubber meets the road. Okay. So like, can you give us an example of a company that you've worked with that you've been able to apply this philosophy with?

A Yeah. Yeah. Yeah. So first, yes, in theory, everything sounds good. In reality, there's a lot of work. Probably there are double amount of work to do to make this thing work. And here's why. If you are a startup, you just want to sell your product. I mean, that's, that's what you want to do. You don't have a time to You know, mingle with a big customer, a big, like, you know, partners like LG. So, uh, what they do want to do is they just sort of go to go to the customer and sell it. The problem with that model is obviously, as we just discussed, customers may not have a lot of, uh, uh, faith in small companies, and that's what LG brings to the table, but to make it work, they have to work with LG first. They have to work with LG to create a joint business model or a model, LG's business model, that incorporates startups into it, and that has to make money. That's where the product market fit becomes, ah, ah, sort of like a scrutinized and validated, because LG will not gonna do business with the startup, unless we think there's a product market fit, basically. So, in some sense, that's a good thing for startups, because they don't have to, like, you know, if they, the disastrous situation for startup is they talk to a hundred customers, and none of them wants to buy it. Right. LG becomes sort of like a first gate or first validation point from status perspective. Now, that take…

AI assessment note: “So I'll give you an example. We have a healthcare company we just spun out”