1 00:00:01,079 --> 00:00:04,640 Alright, welcome to the Neuvottelija channel. 2 00:00:04,640 --> 00:00:09,000 This is a bit of a glimpse into Neuvottelija Sisäpiiri. 3 00:00:09,000 --> 00:00:16,199 Neuvottelija Sisäpiiri is this kind of exclusive, or 4 00:00:16,199 --> 00:00:21,680 extra content, and it has several tiers. You get access to it when you go 5 00:00:21,680 --> 00:00:27,439 over to YouTube and subscribe to the Insider content. 6 00:00:27,439 --> 00:00:31,679 There's typically an extra discussion of about 10 minutes 7 00:00:31,679 --> 00:00:37,559 with the guests, which only you get to see. And on top of that there's 8 00:00:37,559 --> 00:00:42,399 a useful feature, that when you contact me after that, say 9 00:00:42,399 --> 00:00:48,520 on WhatsApp, we check on LinkedIn, say, that you're a real person, then 10 00:00:48,520 --> 00:00:52,719 then you can get into Neuvottelija Sisäpiiri, where there are now 11 00:00:52,719 --> 00:01:00,039 around 170 people and several of my guests. And there we then discuss 12 00:01:00,039 --> 00:01:05,119 these things properly, like adults. I'm one of the moderators there. 13 00:01:05,119 --> 00:01:11,000 There are only real people there. Messages disappear in seven days and no one 14 00:01:11,000 --> 00:01:16,439 represents their organization, unless they specifically want to. 15 00:01:16,439 --> 00:01:21,280 And then there's an extra feature that you can't really find elsewhere in Finland. So 16 00:01:21,280 --> 00:01:25,880 I'm actually a pretty good AI user. Throughout the year I've 17 00:01:25,880 --> 00:01:32,119 specifically been developing an AI called Samantha. It's OpenClaw-based, running on its own 18 00:01:32,119 --> 00:01:36,920 computer, an AI agent. It has its own kind of Hermes research 19 00:01:36,920 --> 00:01:42,640 agent, a Hermes agent system. It has the best language model available, and 20 00:01:42,640 --> 00:01:48,000 for example these helper models then for this Hermes agent 21 00:01:48,000 --> 00:01:53,159 through. It has a very well-developed memory, three-tiered 22 00:01:53,159 --> 00:01:59,119 and then you can ask it whatever you want. And in my opinion it answers very well 23 00:01:59,119 --> 00:02:05,200 for example about investing, politics, in general 24 00:02:05,200 --> 00:02:09,039 science, armchair economists, 25 00:02:09,039 --> 00:02:13,520 about basically anything, and people have fun there, and I'm trying to train it 26 00:02:13,520 --> 00:02:19,560 like an actual employee. Uh, I'm going to demo it now. I was 27 00:02:19,560 --> 00:02:24,599 in Tampere for the second time within a week. The first time, of course, I went to celebrate, or 28 00:02:24,599 --> 00:02:30,280 to watch Tappara win another championship. I bought this kind of 29 00:02:30,280 --> 00:02:37,680 nice gold pin here for my jacket lapel, but now I went again 30 00:02:37,680 --> 00:02:43,720 to the Tampere investment fair. Tampere-talo was completely full of people who 31 00:02:43,720 --> 00:02:49,000 wanted to discuss investing and look at these Finnish listed companies 32 00:02:49,000 --> 00:02:53,400 and I was there as an invited panelist for the investment club panel 33 00:02:53,400 --> 00:02:58,879 on using AI in investing. It was moderated by Aleksi Kopponen. 34 00:02:58,879 --> 00:03:04,879 Also there were Iida Hyynnilä and Timo Heikkilä and me. And now I'll show 35 00:03:04,879 --> 00:03:09,959 here the demo that I showed there too, in the hall. So here 36 00:03:09,959 --> 00:03:15,360 Samantha, my main agent, uh, gets a challenge, that first 37 00:03:15,360 --> 00:03:20,120 I talk with Ville Skogberg, the founder of Nebula, about whether we 38 00:03:20,120 --> 00:03:25,120 have both held Alphabet for years and whether it's been a good investment. So 39 00:03:25,120 --> 00:03:29,640 then first we discuss that, well, the results were really good, so is this 40 00:03:29,640 --> 00:03:34,239 still a good investment, and then we concluded that it's not worth selling at this point. 41 00:03:34,239 --> 00:03:40,120 especially since you can't even own Anthropic or OpenAI through the stock exchange. But 42 00:03:40,120 --> 00:03:43,000 then we start thinking about the fact that there's a bit of 43 00:03:43,000 --> 00:03:47,640 bubble-like characteristics to this AI boom. The Americans are putting in hundreds 44 00:03:47,640 --> 00:03:51,879 of billions of dollars into this AI race, so what, which are the 45 00:03:51,879 --> 00:03:57,560 stocks that could benefit from it. And Samantha then discusses with other 46 00:03:57,560 --> 00:04:03,040 AI working-group bots about possible sectors. 47 00:04:03,040 --> 00:04:07,120 The task is that this investment could be more like 48 00:04:07,120 --> 00:04:11,120 a value investment rather than a growth investment. That is, buying at a reasonable price 49 00:04:11,120 --> 00:04:14,439 probably somewhat weaker growth. We go through the use of AI 50 00:04:14,439 --> 00:04:19,600 in investing, and on stage come Sami Miettinen, Iida Hyynnilä and Timo 51 00:04:19,600 --> 00:04:23,960 Heikkilä. Welcome. Judging by the names, Aleksi Kopponen 52 00:04:23,960 --> 00:04:27,560 was the one who actually came on stage first, but without further ado 53 00:04:27,560 --> 00:04:32,680 let's bring Iida Hynnilä on stage first. Welcome. 54 00:04:33,320 --> 00:04:38,880 Then, uh, established Tappara man Sami Miettinen, and then investor 55 00:04:38,880 --> 00:04:43,120 Timo Heikkilä. Since we've already gone through Gemini here too, we can probably state 56 00:04:43,120 --> 00:04:46,759 roughly what they are. So they're very similar services 57 00:04:46,759 --> 00:04:51,960 to each other, like with ChatGPT, but is there really much 58 00:04:51,960 --> 00:04:55,960 difference between them from an ordinary user's point of view? Would Sami like to comment on 59 00:04:55,960 --> 00:04:58,639 this? Yeah, I have all of them in use, for the 60 00:04:58,639 --> 00:05:02,600 reason that, well, there's this kind of game going on, where Anthropic has been 61 00:05:02,600 --> 00:05:06,560 so popular and so good specifically in office use, because of its Cowork 62 00:05:06,560 --> 00:05:11,320 feature, that a huge number of users have come to it, and they've started 63 00:05:11,320 --> 00:05:16,039 solving this problem with capitalism. That is, they've started removing the queue 64 00:05:16,039 --> 00:05:20,840 model, where everyone got to use it almost unlimited for the monthly fee, and 65 00:05:20,840 --> 00:05:24,560 moved to that kind of API pricing. So, so you have to pay for the extra usage 66 00:05:24,560 --> 00:05:28,039 like real hard euros, which can be tens or hundreds on top of 67 00:05:28,039 --> 00:05:32,720 the monthly fee. This is a thing, then, OpenAI and Gemini are 68 00:05:32,720 --> 00:05:35,880 still a bit cheaper, which maybe shows then that they weren't as 69 00:05:35,880 --> 00:05:39,120 popular, but all of them are currently almost equally 70 00:05:39,120 --> 00:05:42,840 good in terms of capability. I actually use OpenAI's GPT-5 the most. It's 71 00:05:42,840 --> 00:05:46,560 the most cost-effective. So there's no big difference between them, but I would 72 00:05:46,560 --> 00:05:52,400 say that Anthropic is the one that gets you furthest in office work, 73 00:05:52,400 --> 00:05:55,919 because they've built a really good stack for how those Office 74 00:05:55,919 --> 00:06:00,000 Microsoft Office files, for example, get analyzed, or Excel files like that. That 75 00:06:00,000 --> 00:06:03,880 my multiples analysis was this kind of Claude Excel analysis, which by the way is 76 00:06:03,880 --> 00:06:07,639 in investing. I mean, since many of us are this kind of gut-feeling investor, where 77 00:06:07,639 --> 00:06:10,880 you just throw money at it because it sounded like a good company, because it had good business, 78 00:06:10,880 --> 00:06:15,280 so already this kind of finding relative valuation multiples kind of 79 00:06:15,280 --> 00:06:18,479 anchors the investment in reality, whether this really is 80 00:06:18,479 --> 00:06:22,280 nine times EBITDA for this value, so that's like kind of hard data 81 00:06:22,280 --> 00:06:26,639 and Claude is better at that than these others. Let's take a look at how the bots 82 00:06:26,639 --> 00:06:31,599 perform on this. So here they're kind of considering this initial thesis, whether 83 00:06:31,599 --> 00:06:36,720 Alphabet is still a good investment. There are grounds for this, because you find 84 00:06:36,720 --> 00:06:42,520 this kind of processor, TPUs, cloud, also this Google Search and other 85 00:06:42,520 --> 00:06:47,199 services. And then like a really solid infrastructure. Then from here 86 00:06:47,199 --> 00:06:50,520 you find these surrounding things, data center power and 87 00:06:50,520 --> 00:06:56,440 cooling equipment, power grid, data center operators. 88 00:06:56,440 --> 00:07:02,240 So these bots find these, then, network and data, traffic, infra, 89 00:07:02,240 --> 00:07:07,120 chips and memory layers. And then they start digging into, at the same time, 90 00:07:07,120 --> 00:07:10,800 thinking about what might be found here. And it throws out some, like, actual 91 00:07:10,800 --> 00:07:16,479 listed companies. Vertiv, Eaton, Schneider, GE Vernova, Siemens Energy, 92 00:07:16,479 --> 00:07:22,319 Quanta Services, Prysmian, Nexans, Arista, Broadcom, and now let's bring in a second AI. 93 00:07:22,319 --> 00:07:26,360 This is now a manual run from me, so I type into Claude's Excel these 94 00:07:26,360 --> 00:07:30,160 company names and then ask it to do this kind of investment banker 95 00:07:30,160 --> 00:07:34,000 relative multiples analysis of all these companies. Now it starts 96 00:07:34,000 --> 00:07:39,680 gathering this data via Claude, market cap, forecast, 97 00:07:39,680 --> 00:07:43,440 profitability, revenue, debt ratio 98 00:07:43,440 --> 00:07:48,319 and then we build this relative value matrix from all of these. So, that is 99 00:07:48,319 --> 00:07:52,479 like, say, this kind of enterprise value relative to 100 00:07:52,479 --> 00:07:57,280 EBITDA. And here now we find this Nexans 101 00:07:57,280 --> 00:08:01,080 seems to be this kind of value-play candidate, so 102 00:08:01,080 --> 00:08:05,560 smaller than the others, this kind of 8-billion valuation, and yet these multiples are 103 00:08:05,560 --> 00:08:11,039 more moderate, and of course the growth is a bit weaker too. Well then I throw 104 00:08:11,039 --> 00:08:16,560 this at the other bots now, three of them here. There are several of this Excel and 105 00:08:16,560 --> 00:08:21,159 then these bots read Excel fluently, so they analyze this, Samantha's 106 00:08:21,159 --> 00:08:25,000 work, or sorry, the one I made, this Excel 107 00:08:25,000 --> 00:08:29,879 in the Claude in Excel application, and then give recommendations based on 108 00:08:29,879 --> 00:08:35,000 that. And then here, this Ville's Donna bot 109 00:08:35,000 --> 00:08:38,279 also thinks that, as a value investor, this Nexans could be 110 00:08:38,279 --> 00:08:42,640 useful. Then I still give the final word on this to Samantha, that, 111 00:08:42,640 --> 00:08:46,920 now go through this Excel again and read the other bots' analyses and then give me 112 00:08:46,920 --> 00:08:52,120 a final investment proposal, which I could then present here 113 00:08:52,120 --> 00:08:58,279 at the Tampere investment fair, and Samantha then starts ticking away 114 00:08:58,279 --> 00:09:02,959 and also starts leaning toward this Nexans. It's a good infra thesis and 115 00:09:02,959 --> 00:09:09,880 a cheap price. This could be, well, a trap, meaning it could well be that [unclear] doesn't then see it 116 00:09:09,880 --> 00:09:14,320 in the orders, but there's also this kind of traditional cable business here. There's 117 00:09:14,320 --> 00:09:18,440 a bit of history to it. This is, well, the cable company left out of 118 00:09:18,440 --> 00:09:21,680 Nokia's Alcatel-Lucent acquisition. And this is not 119 00:09:21,680 --> 00:09:26,480 investment advice. I'm putting out to the market this 120 00:09:26,480 --> 00:09:30,399 buy order. I'm shading the price down a bit, in case it could be a bit 121 00:09:30,399 --> 00:09:34,320 cheaper, and let's see if it gets filled at that. 122 00:09:34,320 --> 00:09:39,640 If it goes through, then I'm actually putting in €4,900 into this investment, and that 123 00:09:39,640 --> 00:09:43,399 goes through, and this really isn't investment advice, but now there are 30 124 00:09:43,399 --> 00:09:48,480 shares of this stock, Nexans, a French company. 125 00:09:48,480 --> 00:09:54,000 Well, this is a demonstration of how I, and many of my friends there in Sisäpiiri, do 126 00:09:54,000 --> 00:09:58,399 analysis. Whether it's buying a stock, it's not enough that you just prompt it. 127 00:09:58,399 --> 00:10:03,959 It's not enough either that you just run this kind of Excel pass on it, there's this kind of 128 00:10:03,959 --> 00:10:07,600 second opinion, another opinion, or a third and a fourth, from other bots and 129 00:10:07,600 --> 00:10:12,600 their owners, so, so that then really tests the thesis. It doesn't, though, 130 00:10:12,600 --> 00:10:16,760 guarantee anything. This could even be a bad investment, and I did promise that I'll offer 131 00:10:16,760 --> 00:10:20,279 free tokens and beers if this works out, but otherwise it's entirely 132 00:10:20,279 --> 00:10:26,560 my own screw-up if this, if this doesn't fly. But you can do this over there in Neuvottelija's 133 00:10:26,560 --> 00:10:31,680 Sisäpiiri. I'd recommend it, try it, join there, get in touch with me 134 00:10:31,680 --> 00:10:36,120 and then apply for access to this Neuvottelija Sisäpiiri, and there then 135 00:10:36,120 --> 00:10:42,320 you can meet this Samantha, that is, my AI worker, and then use 136 00:10:42,320 --> 00:10:49,519 her know-how for your questions. Thanks, and no pressure. If it's not your 137 00:10:49,519 --> 00:10:53,440 thing, then I'd appreciate it if you liked the channel and hit 138 00:10:53,440 --> 00:10:59,600 subscribe. And, uh, I really do enjoy going out there to talk about AI and 139 00:10:59,600 --> 00:11:03,519 show it, and go to things like that. It's always nice to visit Tampere, not 140 00:11:03,519 --> 00:11:08,959 just for watching Tappara, but that kind of investment fair over there 141 00:11:08,959 --> 00:11:12,680 at Tampere-talo is just a perfect example of an event where I'm 142 00:11:12,680 --> 00:11:18,160 very happy to be a speaker. Thanks.