An alternative investment thesis for AI

Bryce: [00:00:15] Buckle up and welcome to another fun filled episode of Shares Journey, the podcast that chronicles our exciting voyage into the world of investments. From those of you who are just dipping your toes into the financial waters, to those sailing close to the Warren Buffett Wind, our goal is to smash those investing obstacles and guide you from your first stock purchase to collecting dividends to first time listeners. A hearty hello and welcome aboard. We should note, while we are certified financial navigators, we are not privy to your personal treasure map. Hence, any information shared here says the purpose is to entertain and offer general advice only. Enjoy the journey, me hearties. As always, I'm joined by my pirate buddy Ren. How's it going? 

Alec: [00:01:01] Oh, like wow, what a soulful you got for us. We weren't recording. You have to do that. 

Bryce: [00:01:11] What was the theme? 

Alec: [00:01:12] So I'm here with my good mate, ChatGPT. Well, I'm guessing pirate themed. 

Bryce: [00:01:20] I think so. Is that right, Sascha? 

Sascha: [00:01:22] Do you know what? I just chucked it in to my chatgpt. Maybe after the countless months. 

Alec: [00:01:29] What did you chuck it in and ask it?

Sascha: [00:01:31] I said. I said, can you please rewrite Bryce's intro and just make it. Hang on, Let me actually read. I said, Can you try something different? 

Alec: [00:01:46] Oh. 

Sascha: [00:01:47] Yeah. 

Bryce: [00:01:47] Wow. And it's. 

Sascha: [00:01:48] Good. So I've asked quite a few questions. I said, Can you rewrite this podcast intro for me? First one was boring, so I decided not, do it. Then I said, Can you make it more fun? And then I said, Can you try something different? So down that track lies Pirates. 

Bryce: [00:02:05] There we go. So for those that are really confused with what's going on. Welcome to equity mates. If you've just joined us for the first time, congratulations on starting your journey. Ren is my co-host here and we've just done the intro in Pirate thing. 

Alec: [00:02:19] So it's textually relevant. It is contextually right, because we're talking about AI and obviously there's a lot of hype when it comes to investing in AI. There's potentially a better way to think about it, a different way to think about it that we sort of heard from a couple of experts. So we wanted to really bring to the fore and chat about because you don't just have to invest in the companies making these A.I. chat bots. But before we get into that, probably two things. First of all we should just clarify the disclaimer, because I'm not sure a pirate themed disclaimer passes. 

Bryce: [00:03:00] Yes, we are licensed 

Alec: [00:03:00] Was also where we are. 

Bryce: [00:03:01] Financial. A certified financial navigator. Yeah. 

Alec: [00:03:04] Yeah. 

Bryce: [00:03:05] So we are licensed, but we are not aware of your personal circumstances. So any information on this show is for education and entertainment purposes only. Any advice is general advice. 

Alec: [00:03:17] And the other bit of housekeeping that we just want to quickly share. You probably have heard it on the podcast or maybe seen it on social media is we wrote a second book and all we did was copy the manuscript for our first book and asked ChatGPT to make it Pirates and Hold on. So know this book. We know that there's a lot of stress when it comes to money at the moment, and a lot of people, ourselves included, sort of wonder what is enough when it comes to investing. It can be very hard. When you start investing, you always feel like you've got to do more. You've got to find a way to make more returns, find the next company, you know, save more so you can invest more. And we really got stuck on this idea of what is enough when it comes to investing. So we wrote a book about it. It's called Don't Stress, Just Invest, and it is available for pre-order now. The link in the show notes it. It'll be available wherever books are sold on the 22nd of August. But if you could pre-order it now, we'd love that. We hope it helps anything. Or more to add. 

Bryce: [00:04:21] Don't just buy one for a friend or family member who is feeling anxious or unsure about how to start investing. This will give them the confidence in the steps to start in a way that is less stressful. 

Alec: [00:04:33] Guaranteed. 

Bryce: [00:04:33] Guaranteed. Yeah. Money back. 

Alec: [00:04:35] Guarantee from Bryce. 

Bryce: [00:04:36] From your paycheque. 

Alec: [00:04:39] All right, so anyway, let's talk. I am hearty. Okay, now, now, I guess the question is, are you going to continue this ChatGPT alternative intro like next week? Are we going to get like a Yankee thing? 

Bryce: [00:04:53] For episodes that aren't yet recorded? I think it's something that we could refine and bring into it. Yes.What I would say is that it would probably have been better if I tried to do a pirate accent as well. 

Alec: [00:05:06] Do it now. 

Bryce: [00:05:06] No. 

Alec: [00:05:08] I also think I don't think pirates have. And like pirates could be from anywhere. 

Bryce: [00:05:13] Have you know what? Pirates of the Caribbean. 

Alec: [00:05:14] They have accents, but like. But. But that's not all pirates. 

Bryce: [00:05:18] For a guy who. Was saying that. 

Alec: [00:05:19] You know what I'm saying? Awfully carried away. 

Bryce: [00:05:22] So just leave it to me, Sascha. Well, well. 

Alec: [00:05:27] Well. Look, all I'm saying is we've done about 750 episodes of equity mates investing, podcasts, and other 300 of get started investing. I've heard your intros a lot. Yes. And it's kind of nice for you to bring some variety to our relationship after all this time. 

Sascha: [00:05:43] I think we should get stuck in the Pirate Lane. We could go. 

Bryce: [00:05:46] Let's not get stuck in the past. 

Sascha: [00:05:47] Yeah, We could go to the Alternate Universe as well. 

Alec: [00:05:50] One is saying what a pirate is like now. I was.

Bryce: [00:05:54] I read that we didn't take Shakespeare next. 

Alec: [00:06:00] Yeah, yeah, yeah. Good. Shakespeare. Southern America. 

Bryce: [00:06:03] Right. So. All right. Deal. For the episodes that haven't yet had a recorded intro. I will commit to working with our Sascha producer to come up with some chat-led thematic interest.

Alec: [00:06:17] Yeah, well, you're actually not involved in a lot of episodes in the coming weeks, so it will actually be when you're back. 

Bryce: [00:06:23] Nice.

Alec: [00:06:23] Yeah. Anyway, let's get into it. AI cause if we keep waffling on like this, I will take our jobs. So I think let's start with what we've been hearing from a couple of experts. We've. I sat down with Andrew Page and he said something like this and the You're In Good Company podcast interviewed Cathie Wood of Ark Invest and she said something similar. We'll play the clips. But in a nutshell, forget the companies that make AI even for Cathie, Forget the companies that make the picks and shovels. I forget in videos they sold Nvidia. 

Bryce: [00:07:00] Yeah. Mind you, she sold it after getting in at $5. 

Alec: [00:07:04] Yeah. And and. 

Bryce: [00:07:05] So. 

Alec: [00:07:06] For people not familiar with its current share price. 

Bryce: [00:07:08] 400 and something I think or high three hundreds. 

Alec: [00:07:11] Yeah. 

Bryce: [00:07:11] 424 20. So she got in at five.

Alec: [00:07:14] Like they did or. 

Bryce: [00:07:15] Yeah, they did. 

Alec: [00:07:19] Well let's not pretend like 80 something extra. 

Bryce: [00:07:22] When I actually listen to it saying that it was like, wow. She, that was in 2014 she bought and the face like I'd love to go back and understand what she said was the thesis at that point in time. But like that is that's pretty cool. It makes you kind of think like, you know, what she's talking about at the company that she's buying now, next ten years. What's that going to be?

Alec: [00:07:43] Yeah, well, if you want to know the companies she's talking about now, head over to the You're In Good Company podcast because they have a two part interview with her on their fate. But I guess the point was, forget the companies that are making these chatbots like Microsoft Open AI and you can invest in via Microsoft or Google with BYOD. Even the picks and shovels play like Nvidia. Arguably, a lot of money has flowed into that space. There is another thesis that you can hold when it comes to II, and it's all about data, because the thing that A.I. has allowed everyone to do is just crunch data at spades and at scales that haven't been possible before. And there are really interesting emerging use cases as a result. And so rather than us explaining it, let's let some experts explain it. Starting with Andrew Page. 

Andrew: [00:08:36] I'm personally, I'm really a bullish AI. I mean, this is amazing technology. And even since ChatGPT three came out, like we've seen the evolution, like it is so rapid here. So I think it's huge, right? I think I think as a as a sector, I think we've gone. We've crossed the chasm a little bit from what is theoretically possible to actually having workable demos beyond demos of it actually happening. So. So there's legitimacy to it, but people jump on bandwagons really quickly. A lot of companies we've come across are talking about it more. I think one insight that's worth remembering is that it's not that these small ASX companies need to develop the is that this is plug and play technology where to a large extent you're not developing the large language models, but you're applying them in much the same way that, you know, these companies didn't invent the Internet back at the turn of the century but, but used it. So I think a lot of companies will be using it competitively. You're going to be at a disadvantage. I'm talking about over a period of years here. Right. So there is legitimacy to it. But the question you've got to ask is, what edge does this give you? And from that angle, my hot take is that companies best able to leverage it in a way more so than their competitors are those that have proprietary datasets. Hmm. Anyone can point an eye at a set of data if it's public, and we can all extract useful information from that. At least that's the potential. But when I if I involved Para and I own all the breast imaging images data, you know, if I'm a catapult, my own or the sports analytics data, if I'm in virus. Wait, I know you know that it actually I can use it. As much as anyone else can. But as we know with AI, a key part of it is the data set that you can train it on and then extracted from it. 

Alec: [00:10:20] Makes sense. 

Bryce: [00:10:21] Makes a lot of sense. I think the key obviously is the proprietary bit. It's not companies that just have access to publicly available data and can crunch it. It's those companies that own a huge amount of data is the key here. And we'll get to some of the companies in a moment. But before we do that, let's have a listen to what Cathie's views are on this. 

Cathie: [00:10:42] Nvidia is doing astonishing things. We got $5. It was a 5 to $10 billion market cap in 2014, and we've ridden it all the way up. And most of the funds we took it out of flagship because flagships are very concentrated and we see many other much less expensive AI plays. It still meets our 15% compound annual rate of return expectation, but barely, whereas these others are now again, our research could be wrong. I always have to say that poor compliance. But you know, these other names we think are 40-50% compound annual rate because they've been killed as in videos gone up. Doesn't make any sense to us. To give you an example, Tesla. So in video is it 25 times sales, not earnings sales. Tesla is at six times sales. Many people don't know the name. Is it two times sales? The most important competitive advantage once you've got a visionary leader, air expertise and domain expertise is proprietary data. And so you'll see every company in our portfolio is there, at least in part with an AI angle in mind. It is their data. Tesla has oil. That's right. And it's becoming even more true that that has been a saying for a little while, but it's ever more true now. So Tesla has more miles of real world driving data than all of the auto companies and tech companies going after transportation in the world put together and probably orders of magnitude more. And that's because it has 4 million plus robots roaming around the world. I have two of them, a model three and a model Y, and they're collecting data every day, sending it back to Tesla, saying, okay, here's a disengagement. Something didn't go right. Let's study that. And they're using AI and in a profound way that is going to create autonomous autonomous driving. 

Alec: [00:13:03] So Bryce, I think that's one example of this data thesis, and I think at its core, it's the functionality of AI, at least in this thesis view, is going to be a commodity. Yes. Like maybe someone, maybe someone will produce a chat bot, that is, but that doesn't get things wrong because I'll tell you what, the amount of things that I'm finding Googlebot, especially getting wrong, is pretty wild. 

Bryce: [00:13:27] That actually I'm the same some, some really direct questions. It nails like who won the premiership in 2022. I can get that that's just a Google search. Yeah but like. 

Alec: [00:13:37] That's carved into my mind forever. I'll never forget that. 

Bryce: [00:13:40] Sorry bet. Like when it tries to do a chat, take a vibe and kind of synthesise a whole bunch of information and present it back. Half the time I've found that it is wrong. 

Alec: [00:13:51] Yeah, I eyestrain this morning. I've. I was so. I know this chart exists somewhere. It's a chart that shows the different stock markets that outperformed in each decade. And like the point of the chart shows that it's very rare for countries to outperform decade after decade. And I've seen it. I can explain to you what it looks like. But I couldn't couldn't find a spot like 20 minutes on Google trying to find it for an episode to get started investing. We're about to record. And then I was like, Maybe I'll be able to help me here. I went to ask Google to finally find the website Google but couldn't find me. The website gave me a whole bunch of options which were wrong there. I just decided, All right, well, these guys have been trained on data. They have a keyboard, access to the Internet ChatGPT has data up to 2019 or 21, 28, 2021. These guys can just tell me the information and I can recreate it. They both were just wildly wrong. ChatGPT couldn't tell me. It just was like, I don't have that information. I was like, What? What was the best performing stock market between 1990 ten? I didn't have that information. Googlebot just kept telling me it was America for every decade, which is a serious bias, not correct. So anyway, back to the IPO. Maybe someone develops a chatbot that is just. Financially better, but that doesn't feel like the path we're going down. So you could have a pick and shovels play, invest in Nvidia or you say what companies have data that will benefit from the power of this. 

Bryce: [00:15:28] Yeah. And by benefit we mean like their business operations that we're going to become more competitively or more competitive or that competitive advantage is going to become more powerful. Like their business then is to converse their competitors. Yeah, their ability to grow leverages that data. 

Alec: [00:15:47] Like Cathy's example, is a classic example. There's all these companies fighting to be the first to get true self-driving. And she believes that the amount of data that Tesla has been collecting gives it an unfair Advantage over its competitors. 

Bryce: [00:16:03] Yeah. Yeah. So this got us thinking which companies have heaps and heaps of proprietary data.

Alec: [00:16:10] Out of left field companies that we can talk about? 

Bryce: [00:16:13] Yeah, well, there are the obvious. There's all of the social media companies Meta, Bytedance, Twitter.

Alec: [00:16:20] Yeah, they have a portfolio of human interaction which is unrivalled yet. And Elon, the amount of strategic mistakes, well, I don't know if there's going to be mistakes in hindsight, but have you seen what he's doing on Twitter? 

Bryce: [00:16:34] He's limiting how many posts people can say. 

Alec: [00:16:37] Yeah, because he's worried about it being scrape. Yeah. Yeah. That's like my. Anyway.

Bryce: [00:16:44] Yeah. Why does he care if it's scrapped? That's what the I've kind of have researched. 

Alec: [00:16:48] Book is like this exact thing. 

Bryce: [00:16:51] He wants to protect. 

Alec: [00:16:52] The data of like, different interactions in Twitter and all of like everything that they are collecting is proprietary like is an advantage for Twitter if they're the only ones that are analysing it. But if researchers can just scrape Twitter data and do their own research without paying Twitter, then he feels like he's being stolen. 

Bryce: [00:17:12] Yeah, yeah, yeah. I mean, I get it. But like, come. 

Alec: [00:17:16] On, part of me is like, fine, let them scrape but have ads. And then all these impressions count as us out in front. 

Bryce: [00:17:27] Oh, yeah. The advertisers will love that. 

Alec: [00:17:29] They don't need to know. 

Bryce: [00:17:32] So that all the social media obviously, we know Google has. 

Alec: [00:17:37] We do know. 

Bryce: [00:17:37] Has a massive amount of data. 

Alec: [00:17:40] Yeah. In particular search data, like imagine the amount of insight you can gain on what humans are doing and thinking. You overlay that with different events and then you track search data like, Right. Is so much insight. Hmm. Yeah. 

Bryce: [00:17:59] And then there's the devices side, like the apples of the world. And what they know about us is going to be incredible. Yeah. Reams of data. 

Alec: [00:18:11] Just one data point. Like imagine Apple pointing towards the data they have on hundreds of millions of iPhone users.

Bryce: [00:18:22] Mm hmm. Nuts.

Alec: [00:18:25] Hopefully I don't look at yours. 

Bryce: [00:18:27] That is fine, to and from work every day. But that that's the obvious one. So we've been sitting here and having a think about what are the companies that have pretty interesting datasets that they can train AI. So before we do that, we're going to take a very quick break and then get back. Welcome back to Equity Mates. Melodies we are discussing. We are discussing AI and more importantly, companies that have large proprietary data sets and how they might be able to leverage AI to develop a strong competitive advantage. Now, we've heard Cathie talk about Tesla and how it can leverage all of the driving data to get an advantage in true self-driving. We've heard Andrew Page talk about Volpara and the breast imaging data that it gets. Ren, Let's go through our list of just companies that we think have huge amounts of data that if they can plug in, AI will potentially be able to judge up their business. [00:19:32][64.7]

Alec: [00:19:33] So here's an interesting one. Well, I think it's interesting. I don't bias the jury match group and also Bumble and Grinder all publicly listed, but Match Group is the biggest. So they Match Group owns Match.com. Tinder hinge at about 40. I think they own 42 dating platforms. In total, the volume of human interaction is staggering when you think about it. Like every swipe, every message, every rose purchased and every super like and all of that stuff. Imagine if you could point at that and you start getting insights around. And I'm sure they already do. I'm sure there's a lot of algorithms that decide who's getting shown to who and all of that stuff. But just like how predictive I could be in terms of love.

Bryce: [00:20:28] Yeah, I mean, I would think that, that yeah, to your point, like. 

Alec: [00:20:33] They would already have a go door to be like yeah sure. 

Bryce: [00:20:35] Yeah. Background. Yeah. But it could get to the point where they're just the first person that opens you gets fed is the perfect person They. 

Alec: [00:20:43] Yeah they launched it where there's only one match. Yeah. And maybe it's like we talked to them. 

Bryce: [00:20:48] You'd have to pay and they charge a fortune.

Alec: [00:20:50] $1,000,000? Yeah. Hey, you pay for a lot. Maybe you open the app, and if they haven't found the perfect one, it's just, like, still looking. True.

Bryce: [00:21:01] Yeah. Yeah, yeah, yeah. Your time's not now. 

Alec: [00:21:04] Because it's like on Tinder. On Tinder. There would be times where that. I know that serving up subpar matches, but it's like, hey, the game. Is it just. Yeah. So people are. Yeah, yeah, yeah. 

Bryce: [00:21:14] Then they change their model to be like the we, we will guarantee the first person which is the one you got to pay. 

Alec: [00:21:21] For $1,000,000 subscription with a money back guarantee. If you don't get married. [

Bryce: [00:21:26] They wouldn't, they wouldn't have the money back guarantee with AI. 

Alec: [00:21:30] All right. That's right. That's what they. What about you? You got. 

Bryce: [00:21:32] It. Yeah. So obviously, we both worked at Woollies and Kohl's and this was going on. I wouldn't say it was an issue. Obviously, the reams of data on customers that both of those retailers have through their rewards programs, through every single product that is scanned, through checkout, through their websites, through, you know, that all the other businesses that they have going on, credit cards, insurances, everything, just the the picture that they can paint of their customers is enormous. And I know when we were when I was working at Woollies, you know, eight years ago, so there was a there was a general feeling that there was almost like there is so much data we don't know what to do with. Yeah, yeah. Like, how do we even higher the right people and to build systems that and this is no like indictment on the people working there but it was just so much data it's like where do we how do we even leverage this. And so Woollies ended up buying half of Quantum, which was just a data analytics company. And I think now they own that majority. Purely so that they had this company just crunching, crunching data and trying to deliver insights. And so you can see how if they can, you know, really leverage either the level of detail and mapping of customers and exactly what you want when you're going to want it, how you're going to buy it, like it becomes pretty powerful.

Alec: [00:22:55] Yeah, I think they Woollies now own 75% of it and founders and employees on the other 25, right? Yeah. 

Bryce: [00:23:03] So yeah I mean give it given that between them they pretty much have half the population age millions and millions of people shopping. 

Alec: [00:23:13] Yeah. And I think you can probably bucket the credit card companies and the banks into this as well. Like just, just customer data and the insights you can gain. I think the other interesting one for Woolies and Coles is they have like other data sources that you probably don't think that much about, but one that I often think about is CCTV, because I has now reached human level object recognition. I think in some tests it's actually better than humans at recognising objects. So in theory, well, I could be trained on video or images and analyse it and draw insights from it. It doesn't. Just have to be taxed. And so Woollies has 1000 something supermarkets and hopefully they're saving all that CCTV. I don't know. It's the big cloud cloud storage bill, but but all of a sudden it's like how a customer's moving through our stores. What are they? What's catching their eyes, shelf height, spacing of aisles, all of that stuff. And all of a sudden you say, I, I've got a thousand supermarkets old, slightly constructed differently. What insights can I draw and also perfect. 

Bryce: [00:24:27] What floor plan? 

Alec: [00:24:28] What experiments can I run? Like let's test different levels of lighting. Does that thing where they like mist water onto the fruit and veg. How much does that increase sales and you know like and then you can start dividing by demographic and you can overlay like socioeconomic factors over this stuff and you can think, look at how humans are actually moving through stores in different parts of the country and stuff like that.

Bryce: [00:24:53] So again, I think for people sitting there going, Oh, you can do all that, you can do that now. And so one of the roles that I was doing at Woollies was all of that store format sort of strategy and thinking through things like floor plan design and that sort of stuff. But it's the power of the air comes in, the analysis of it.

Alec: [00:25:08] I think every single thing that we're going to talk about today that applies for yeah, it's artificial intelligence. Intelligence exists. It's just time consuming and expensive. 

Bryce: [00:25:18] Yeah. Or near impossible

Alec: [00:25:20] Yeah, yeah, yeah. The, you know, like a given enough money and enough time. At some point over the course of history, self-driving cars will be developed. But the beauty of what Cathie Wood is arguing is that AI accelerates that for Tesla, pun intended. 

Bryce: [00:25:38] What else?

Alec: [00:25:40] So for me, a really interesting one is the pharmaceutical space. So the Pfizer, Eli Lilly, AstraZeneca, Merck, Johnson Johnson, the volume of data they have from clinical trials and in particular failed clinical trials is immense. And you know, humans have been poring over that data and trying to figure out what went wrong or, you know, are there any any possible like second avenues, You know, like the classic example is Viagra was not they didn't set out to make an erection pill. It was for something, heart disease or something. And they did the clinical trial and there was this side effect and they were like, oh, this is a multibillion dollar drug. And, you know, like, what could I going back over all that data find that humans missed? So that's a really interesting one. But also drug discovery going forward is a really interesting one. And this is less about proprietary data, but I'm just finding a way to shoehorn it in because it's so interesting to talk about. There have been some new drugs discovered by and it's just like I was given ungodly amounts of data to crunch. A new antibiotic was found, um, and I think quite a powerful antibiotic was found by Google who have launched Ice Morphic Labs, which is using A.I. to search for new treatments. And I think if you combine the computing power of A.I. with all of the data that these pharmaceutical companies already have, like it, it will be a pretty powerful combination. 

Bryce: [00:27:14] Very powerful. It's pretty exciting to say the way. Right? Right in the midst of it. Yeah. Yeah. It's pretty amazing to be alive. Anyway, One for me. Ren is in sports now. There's not specific companies that I think Catapult might be one here in Australia, but I like. 

Alec: [00:27:33] Yeah, yeah, they will be the biggest.

Bryce: [00:27:35] Well, it's also like combining all the sports, like if you talk about AFL for example, but it applies everywhere, you know, what's the company that crunches this, The game data. Champion data. So you combine that with the player data with nutrition and all of a sudden the teams can start painting this picture of, you know, you had 25 handballs and you ran this far and like just, I don't know what it could end to be, but like the perfect player optimisation or the like Yeah, like, I don't know, yeah. What the outcome would be, but they're always trying to optimise for fitness and you know, the right players on at the right time and all that sort of stuff. So I think if the volume of data that's out there in sports is there where they all, you know, it's pretty data driven at the moment, all the teams have game analysts. You know, if you're plugging in all the video footage of all the games into A.I. and telling it to analyse the perfect plays and all that sort of stuff, like you can see it becoming quite powerful.

Alec: [00:28:34] The fascinating thing for me is how much like sport has become so analysed and you know, in every sport, analytics has really taken off. Obviously the Oakland A's were big with this whole Moneyball strategy of analytics. We're saying it. Really sort of take off in the NFL with some teams and like strategy is really determined by analytics in some cases. And it's happening more and more in every sport. But for me, the question is like with this stuff. Will we? Will we at some point realise there's a limit to it? And you, like you can't analyse your way to a perfect time and it's like at some point it's just like human ingenuity and just like, just do it all. Like to, to will yourself to the next contest. Absolutely. 

Bryce: [00:29:30] Yeah. But it'll tell you the right people to put in those positions to give yourself the best chance.

Alec: [00:29:34] So I guess, like, can you measure that? Can you measure like someone being clutch. 

Bryce: [00:29:38] Yes. Out of that. 

Alec: [00:29:40] Yeah. Yeah. Like the potential to be clutch. 

Bryce: [00:29:42] It's like one of the characteristics of all the people that were clutch and of across the last hundred years of football maybe. 

Alec: [00:29:48] Yeah, maybe it's like analysing the characteristics. Yeah. Understanding mental fortitude they have.

Bryce: [00:29:52] What food did they eat before?

Alec: [00:29:53] Understanding like maybe you sequence their genome and you understand that like these genes lead to B.

Bryce: [00:29:59] Plus. Anyway, all right, let's get through a couple of others.

Alec: [00:30:04] I think there's a couple of obvious ones. So airlines, pricing and stuff like that. Yeah. Amazing surprise, figuring out who is going to pay what. McKinsey did a big paper we concluded in the show notes if people want to read it, it's called Notes from the Air Frontier. They said they've spoken to hundreds of companies and analysed it, but never actually include any of the companies. It's always anyway, it's proprietary data. Yeah, yeah, true. McKinsey and Bain and those guys. How much do they look at their data? Yeah. Yeah. Anyway, they looked at all these different industries and asked, Where does the analytical power of A.I., where will it add the most value and travel massively over index they reckon will more than double the revenue in the travel industry. So travel was number one, transport and logistics number two and transport and logistics. You can sort of understand route optimisation, making sure every ship flying truck is full and then it's full with the right stuff to minimise costs on the other end and all of that stuff. And then one more brace as we close it out. I think we need to mention agriculture. Yeah, and we've spoken about this company before, but I think this is the company to watch in this space. It's not necessarily going to be a great investment, but the one to watch if you're interested in this space is Deere. 

Bryce: [00:31:28] Yeah, yeah. We've spoken about it a couple of times on the show, but they're already using AI and, well, kind of Internet of Things, plugging up and using technology to optimise farming. And as I guess they can, if they start plugging in, I take that to a whole new level. 

Alec: [00:31:48] Yeah. So right now they are really focussed on collecting heaps of data and delivering that to farmers, you know, soil, moisture, content, humidity of the air presence of pests, how much fertiliser, you know, to spray all of the stuff like analysing nutrients in the soil, all of that stuff. They're also trying to develop self-driving tractors, but even if they don't have self-driving tractors, they're still collecting data on traditionally driven tractors as they're driving around a field. All of that data gets sucked into the platform and then it tries to deliver insights to farmers. But for Deere, they're sucking all that data from farms globally and season after season after season, and that bank of data, as more of these sensors and stuff get rolled out because it's still early days for this whole story, what insights that will yield will be really interesting. 

Bryce: [00:32:43] Yeah, it's Fascinating. 

Alec: [00:32:44] Also, pun intended. 

Bryce: [00:32:46] Nice. Well, I think the I guess the point of this is that they're you know, eyes are certainly opening up a lot of investment opportunities. It's getting a lot of people excited. They're the obvious place which we said at the top of the episode which is investing in the AI companies. Then there's the picks and shovels. But, you know, you can really see how this opens up opportunities for companies with such large volumes of data that traditionally, you know, they've had that data there and have been trying to optimise it and use it and gain competitive advantage, but is only going to improve their ability to do that. So fascinating space, really exciting time, you know, no wonder it's creating such hype and companies are out there raising at ridiculous valuations. So yeah. [00:33:30][43.9]

Alec: [00:33:31] Can I tell you one story about a AI start up before we leave that I think sort of illustrates the point. It illustrates why NVIDIA is getting such a high valuation. There's a start up called infection and no inflection important. L there. Heard of it? [00:33:49][18.1]

Bryce: [00:33:50] I don't think so. [00:33:50][0.5]

Alec: [00:33:51] So they raised a $1.3 billion funding round. See this? Yeah, they're like an early stage company. I think the, the team behind it a pretty good but they raised money from Microsoft and Nvidia and a whole bunch of other companies 1.3 billion. There was some back of the envelope maths done on a podcast I was listening to and they reckon 900 million of that 1.3 billion is going to need to go basically back to India to buy. GPU to build the compute power to build the start up. It's crazy. I think Nvidia. GPU $30,000 off the shelf, but there's such a backlog if you want to buy it in the secondary market, about 45 grand. And so all of these start-ups like how there was a generation of SAS start ups like software as a service start ups that VSOs used to complain that they would fund them and then all that money would just go to Google and Facebook in ads and people got really annoyed at that. There's this generation of AI Start-ups. How much of the money that they raise is going to have to just go to like Nvidia and AI and computers? 

Bryce: [00:34:59] Well, I think I read that they needed to buy something like 20,000 of these things. Yeah. Yeah. Which is at 45 grand is 900 million. 

Alec: [00:35:07] Would that make sense. Yeah, yeah, yeah. 

Bryce: [00:35:10] Unbelievable.

Alec: [00:35:11] Yeah. So like, that's great. 

Bryce: [00:35:12] Business model for Nvidia. 

Alec: [00:35:13] Yeah. Yeah. 

Bryce: [00:35:16] So maybe after all of that, it is investing in video. 

Alec: [00:35:19] Well, again, like we spoke about this last week on the show, like your returns as an investor is a function of the quality of the business that you invest in and how much they can grow and the price you pay. Yeah, business. And that's the question within the video. 

Bryce: [00:35:34] Now, we'd love to hear your input on any companies that you think could also benefit from large volumes of proprietary data. Join the Equity Mates Community on Facebook Equity Mates Discussion Group and share it with the community. We'd love to hear your thoughts. And yeah, let's go on this journey together.

Alec: [00:35:51] Let's let's uncover some gems. 

Bryce: [00:35:53] Yes, Yes. Now we're going to continue, as we said at the top with AI data driven introductions over the next couple of weeks. So stay tuned Ren, I'm going to surprise you every time. But we will leave it there and pick it up next week. 

Alec: [00:36:08] Ahoy, mi hottie.