1 00:00:05,800 --> 00:00:09,399 Speaker 1: Today two sides of Tesla that nobody's talking about. Michael 2 00:00:09,440 --> 00:00:11,600 Speaker 1: Thompson from Fear and Greed Business News, and I'm joined 3 00:00:11,640 --> 00:00:14,840 Speaker 1: by Stefan from Olds dot Co, the Alternative market Research 4 00:00:14,880 --> 00:00:16,840 Speaker 1: that you need to be a better investor. Stefan Hullo, 5 00:00:17,000 --> 00:00:19,759 Speaker 1: welcome back, Good to see you, ma'am. Look before we 6 00:00:19,800 --> 00:00:22,599 Speaker 1: get going, of course, this is not investment advice. This 7 00:00:22,640 --> 00:00:24,639 Speaker 1: podcast is just a starting point for your research, and 8 00:00:24,640 --> 00:00:28,760 Speaker 1: you should get professional advice before investing. Today we are 9 00:00:28,840 --> 00:00:34,440 Speaker 1: talking about Tesla, and no really, we're not talking about 10 00:00:34,440 --> 00:00:37,040 Speaker 1: the parts of the EV maker that you might expect. 11 00:00:37,080 --> 00:00:39,720 Speaker 1: It's had a pretty rough run lately. You've had Elon 12 00:00:39,840 --> 00:00:43,199 Speaker 1: Musk has been very busy with his role with the 13 00:00:43,360 --> 00:00:47,080 Speaker 1: US government heading up the Department of Government Efficiency Cutting Costs. 14 00:00:47,760 --> 00:00:52,400 Speaker 1: Tesla's share prices slumped, sales of evs in the US, 15 00:00:52,440 --> 00:00:55,560 Speaker 1: in Europe and China have declined. That's both a backlash 16 00:00:55,600 --> 00:00:58,440 Speaker 1: to musks politics will so simply because it's more competition 17 00:00:58,760 --> 00:01:01,600 Speaker 1: in the industry. You've got kind of Chinese company BYD 18 00:01:01,760 --> 00:01:04,319 Speaker 1: absolutely booming, and it's not the only one that is 19 00:01:04,360 --> 00:01:07,679 Speaker 1: going well. But that's kind of just the background, right, 20 00:01:07,720 --> 00:01:09,560 Speaker 1: That's not what we're here to talk about there are 21 00:01:09,600 --> 00:01:13,600 Speaker 1: two elements of Tesla that just don't seem to get 22 00:01:13,640 --> 00:01:16,840 Speaker 1: a lot of attention. We're gonna start with one that 23 00:01:16,880 --> 00:01:23,520 Speaker 1: I know very little about. It is autonomous vehicles and 24 00:01:23,560 --> 00:01:27,520 Speaker 1: the way that they see the world around them. How 25 00:01:27,560 --> 00:01:29,120 Speaker 1: does Tesla do it at the moment. 26 00:01:30,760 --> 00:01:34,399 Speaker 2: So when we're talking about autonomous vehicles, you know, the 27 00:01:34,600 --> 00:01:37,520 Speaker 2: vehicles need to sense the environment, right, and there's a 28 00:01:37,520 --> 00:01:41,440 Speaker 2: couple of different ways that they can do this. There's radar, 29 00:01:42,040 --> 00:01:45,200 Speaker 2: there's kind of like a series of cameras, and then 30 00:01:45,280 --> 00:01:50,080 Speaker 2: there's something called lightar, which stands for light detection and ranging. Now, 31 00:01:50,840 --> 00:01:55,840 Speaker 2: lightar is basically considered to be kind of the best 32 00:01:56,000 --> 00:01:59,040 Speaker 2: way to sense the environment, and the reason is because 33 00:01:59,040 --> 00:02:02,400 Speaker 2: it doesn't just kind of guess the distance between the 34 00:02:02,480 --> 00:02:08,040 Speaker 2: vehicle and other objects. It accurately measures the distance. This 35 00:02:08,320 --> 00:02:10,519 Speaker 2: is kind of like the pinnacle of the technology. And 36 00:02:10,800 --> 00:02:15,560 Speaker 2: this technology used to be very expensive. So years ago, 37 00:02:15,880 --> 00:02:20,520 Speaker 2: as Elon was building Tesla, he decided that lydar wasn't 38 00:02:20,639 --> 00:02:24,000 Speaker 2: quite right for Tesla's too expensive and it was unnecessary, 39 00:02:24,040 --> 00:02:27,160 Speaker 2: So he created something called Tesla Vision, which explicitly does 40 00:02:27,200 --> 00:02:28,240 Speaker 2: not use ldar. 41 00:02:28,680 --> 00:02:30,680 Speaker 1: It doesn't have it has a good sound to it 42 00:02:30,680 --> 00:02:33,880 Speaker 1: doesn't a Tesla vision. It fits neatly into the kind 43 00:02:33,880 --> 00:02:36,320 Speaker 1: of the Alon musk mythology, doesn't it. 44 00:02:36,320 --> 00:02:39,800 Speaker 2: It does, And there's nothing wrong with Tesla vision in 45 00:02:39,840 --> 00:02:45,080 Speaker 2: of itself. It's been shown to be safer than humans truly. 46 00:02:45,800 --> 00:02:49,000 Speaker 2: But what's happening right now is that the cost of 47 00:02:49,080 --> 00:02:53,440 Speaker 2: LDAR is plummeting. There's a lot of factors for this. Basically, 48 00:02:53,680 --> 00:02:56,440 Speaker 2: the Chinese are able to make it much much cheaper, 49 00:02:56,960 --> 00:02:59,360 Speaker 2: like literally ten times cheaper than it used to be 50 00:02:59,520 --> 00:03:03,040 Speaker 2: just seven years ago. So now Elon's decision not to 51 00:03:03,160 --> 00:03:07,160 Speaker 2: use light R is you know, questionable to say the least. 52 00:03:07,280 --> 00:03:11,320 Speaker 1: Okay, and so instead he developed this system of Tesla vision. 53 00:03:11,440 --> 00:03:16,919 Speaker 1: So it's essentially cameras that are surveying what is happening 54 00:03:17,000 --> 00:03:21,880 Speaker 1: ahead and to the side and using what AI kind 55 00:03:21,880 --> 00:03:25,200 Speaker 1: of processing in order to determine kind of what's a hazard, 56 00:03:25,520 --> 00:03:29,480 Speaker 1: what is going to pose a risk to the vehicle, 57 00:03:29,880 --> 00:03:32,160 Speaker 1: and then from there the vehicle's able to make a 58 00:03:32,160 --> 00:03:33,959 Speaker 1: decision as to what action it takes. 59 00:03:34,720 --> 00:03:37,880 Speaker 2: That's right, And like we said about AI, that's basically 60 00:03:37,920 --> 00:03:40,960 Speaker 2: Tesla's bat you know, they're saying that the cameras that 61 00:03:41,040 --> 00:03:45,640 Speaker 2: they use plus AI is enough. There's no need for lasers, 62 00:03:45,680 --> 00:03:48,000 Speaker 2: there's no need for light R, there's no need for 63 00:03:48,800 --> 00:03:52,240 Speaker 2: with the industry called redundancy, just you know, since the data, 64 00:03:52,520 --> 00:03:55,720 Speaker 2: since the environment, collect enough data and train the model, 65 00:03:56,400 --> 00:04:00,440 Speaker 2: and then kind of that's good enough for the that's 66 00:04:00,440 --> 00:04:03,520 Speaker 2: good enough for society. But that's not what the rest 67 00:04:03,520 --> 00:04:07,360 Speaker 2: of the industry is kind of moving towards now. And 68 00:04:07,400 --> 00:04:11,360 Speaker 2: again it's because lightar doesn't guess, it actually measures. It 69 00:04:11,400 --> 00:04:15,080 Speaker 2: doesn't care about light and color and contrast and snow 70 00:04:15,120 --> 00:04:18,480 Speaker 2: and rain. And this is where the advantage of light 71 00:04:18,600 --> 00:04:22,160 Speaker 2: art really shines, because you know, most of the technologies 72 00:04:22,200 --> 00:04:24,479 Speaker 2: out there can on a clear, sunny day that they 73 00:04:24,560 --> 00:04:27,520 Speaker 2: all basically kind of work pretty much the same. The 74 00:04:27,640 --> 00:04:30,640 Speaker 2: problem is with the edge cases. What happens when it's 75 00:04:30,680 --> 00:04:33,839 Speaker 2: snowing like crazy, What happens when there's a half tipped 76 00:04:33,880 --> 00:04:35,920 Speaker 2: over cone in the middle of the road. What happens 77 00:04:36,120 --> 00:04:38,960 Speaker 2: if there's someone who's wearing black running across the street 78 00:04:38,960 --> 00:04:42,600 Speaker 2: in the middle of the night. This is where Tesla's 79 00:04:42,720 --> 00:04:45,760 Speaker 2: vision can fall short compared to something like light are 80 00:04:45,839 --> 00:04:48,880 Speaker 2: And this is what could prove problematic for Tesla down 81 00:04:48,920 --> 00:04:49,320 Speaker 2: the line. 82 00:04:51,320 --> 00:04:54,719 Speaker 1: How accurate then do we have kind of statistics. I 83 00:04:54,760 --> 00:04:56,719 Speaker 1: know there's an awful lot of testing going on in this, 84 00:04:56,760 --> 00:04:59,240 Speaker 1: and there's a lot of reporting standards that are required 85 00:04:59,240 --> 00:05:03,279 Speaker 1: that in terms of any incidents that happen involving vehicles 86 00:05:03,640 --> 00:05:07,320 Speaker 1: where someone is injured or killed or any kind of 87 00:05:07,360 --> 00:05:12,880 Speaker 1: significant accident has to be reported at Tesla's vehicles using 88 00:05:13,600 --> 00:05:17,000 Speaker 1: Tesla Vision. Are they still reaching kind of high levels 89 00:05:17,040 --> 00:05:17,560 Speaker 1: of safety? 90 00:05:18,440 --> 00:05:23,560 Speaker 2: Absolutely so, But there's a caveat here. So according to Tesla, 91 00:05:24,480 --> 00:05:28,640 Speaker 2: their autopilot full self driving FSD as they call it, 92 00:05:28,680 --> 00:05:31,280 Speaker 2: which really shouldn't be called PEP, but we'll get into 93 00:05:31,320 --> 00:05:35,080 Speaker 2: that some other day, it crashes about once every seven 94 00:05:35,120 --> 00:05:41,200 Speaker 2: point four million miles driven. That is extremely safe compared 95 00:05:41,240 --> 00:05:43,800 Speaker 2: to human drivers in the US, which crash once every 96 00:05:43,839 --> 00:05:45,440 Speaker 2: six hundred and seventy thousand miles. 97 00:05:45,960 --> 00:05:47,799 Speaker 1: Oh wow, that is significantly better. 98 00:05:48,240 --> 00:05:50,960 Speaker 2: Yeah, Like humans are not as good as the machine's 99 00:05:50,960 --> 00:05:53,040 Speaker 2: are driving. Let's just like get that out of the way. 100 00:05:53,160 --> 00:05:54,359 Speaker 2: No matter what you might think. 101 00:05:55,040 --> 00:05:57,080 Speaker 1: I happen to think I'm a very very good driver, 102 00:05:57,640 --> 00:05:59,880 Speaker 1: but I would still actually back a computer over me 103 00:06:00,040 --> 00:06:00,479 Speaker 1: any day. 104 00:06:01,160 --> 00:06:03,039 Speaker 2: Yeah, And like I think the sooner we kind of 105 00:06:03,040 --> 00:06:05,960 Speaker 2: acknowledge that, the better, But The problem is that this 106 00:06:06,000 --> 00:06:11,280 Speaker 2: is Tesla's own data. They're kind of self reporting. There's 107 00:06:11,320 --> 00:06:14,599 Speaker 2: not a ton of transparency with this stuff, and so 108 00:06:14,600 --> 00:06:19,479 Speaker 2: we're kind of relying on Tesla to just report accurately. Meanwhile, 109 00:06:19,520 --> 00:06:23,040 Speaker 2: the other companies Waymow and Crews are reporting some spectacular 110 00:06:23,120 --> 00:06:26,280 Speaker 2: numbers with their vehicles using lid oar as well. So 111 00:06:27,160 --> 00:06:29,320 Speaker 2: it's it kind of remains to be seen. All of 112 00:06:29,360 --> 00:06:32,760 Speaker 2: this stuff is much safer than humans, but it's a 113 00:06:32,839 --> 00:06:36,920 Speaker 2: little murky, and it's actually becoming less transparent under the 114 00:06:36,920 --> 00:06:42,440 Speaker 2: Trump administration, who is reducing accident reporting requirements, and Tesla's 115 00:06:42,440 --> 00:06:45,200 Speaker 2: seen to benefit from this from this change. 116 00:06:46,080 --> 00:06:50,159 Speaker 1: Okay, so assuming then that the best outcome would actually 117 00:06:50,200 --> 00:06:53,000 Speaker 1: be a mix, right, I'm assuming that that if you 118 00:06:53,040 --> 00:06:56,159 Speaker 1: were to have Tesla Vision, so you're using cameras with 119 00:06:56,400 --> 00:07:00,599 Speaker 1: AI processing, but then actually having a backup of lighter 120 00:07:00,760 --> 00:07:04,680 Speaker 1: so lasers essentially measuring the depth of what's around it 121 00:07:04,720 --> 00:07:07,760 Speaker 1: and what there's radar as well. That's another option as well. 122 00:07:08,320 --> 00:07:11,680 Speaker 1: Wouldn't the best outcome to be having a mix? And 123 00:07:11,800 --> 00:07:14,560 Speaker 1: is it just what an ideological opposition to it? Now? 124 00:07:14,680 --> 00:07:17,240 Speaker 1: If cost is no longer a factor because light is 125 00:07:17,240 --> 00:07:22,280 Speaker 1: getting cheaper, is it just this almost the ideals of 126 00:07:22,320 --> 00:07:26,000 Speaker 1: a purist who wants a system to just be based 127 00:07:26,040 --> 00:07:28,480 Speaker 1: on vision and AI is that's what's stopping this. 128 00:07:29,080 --> 00:07:33,240 Speaker 2: It's tough to say what's in Elon's mind and what 129 00:07:33,960 --> 00:07:37,559 Speaker 2: Tesla's thinking about light are they're a little mom about 130 00:07:37,600 --> 00:07:41,200 Speaker 2: it lately. You know, I think that there's probably some 131 00:07:41,400 --> 00:07:44,960 Speaker 2: ideological basis for staying the course. But the thing is 132 00:07:45,200 --> 00:07:49,960 Speaker 2: Tesla doesn't need to switch to light that there's other options. 133 00:07:50,040 --> 00:07:52,880 Speaker 2: They could kind of shore up the weaknesses and Tesla 134 00:07:52,960 --> 00:07:56,640 Speaker 2: vision through a combination of other technologies. Thermal imaging is 135 00:07:56,640 --> 00:07:59,560 Speaker 2: one of them, for example, right, so normal imaging is 136 00:07:59,560 --> 00:08:02,640 Speaker 2: similar to light ar doesn't really care about snow and 137 00:08:03,480 --> 00:08:04,800 Speaker 2: rain and stuff like that. 138 00:08:05,280 --> 00:08:07,720 Speaker 1: Well, that scenario that you that you mentioned before of 139 00:08:07,800 --> 00:08:10,040 Speaker 1: some undressed all in black running across the road at 140 00:08:10,080 --> 00:08:13,720 Speaker 1: not thermal imaging would pick up straight away this hate 141 00:08:14,040 --> 00:08:15,640 Speaker 1: signature coming exactly right. 142 00:08:15,600 --> 00:08:18,360 Speaker 2: And so then that doesn't work for all obstacles in 143 00:08:18,400 --> 00:08:20,320 Speaker 2: the road, but it definitely works for for you know, 144 00:08:20,360 --> 00:08:22,760 Speaker 2: animals and humans and stuff like that. So you know, 145 00:08:22,800 --> 00:08:25,440 Speaker 2: it's possible that Tesla will embrace some sort of hybrid 146 00:08:25,600 --> 00:08:28,040 Speaker 2: Tesla vision in the future. I think that they may 147 00:08:28,200 --> 00:08:31,960 Speaker 2: have to going forward, because you know, it's going to 148 00:08:32,000 --> 00:08:35,400 Speaker 2: be tough to compete with light our vehicles and companies 149 00:08:35,400 --> 00:08:38,040 Speaker 2: that are using it. It's just that it's a fundamentally 150 00:08:38,080 --> 00:08:42,079 Speaker 2: better technology and it's it's not expensive anymore, so you know, 151 00:08:42,120 --> 00:08:43,320 Speaker 2: it'll be really interesting to watch. 152 00:08:43,920 --> 00:08:47,240 Speaker 1: Absolutely. Now I mentioned that there are two angles to 153 00:08:47,240 --> 00:08:50,240 Speaker 1: today's conversation, two aspects of Tesla the people aren't really 154 00:08:50,240 --> 00:08:53,960 Speaker 1: talking about. The alo One is combon credits and and 155 00:08:54,000 --> 00:08:57,320 Speaker 1: this is something that it feels like, unto you really 156 00:08:57,320 --> 00:08:58,959 Speaker 1: have a good look at Tesla, you don't realize this 157 00:08:59,080 --> 00:09:02,360 Speaker 1: that that the companytually makes a lot of money selling 158 00:09:02,480 --> 00:09:03,600 Speaker 1: carbon credits, doesn't it. 159 00:09:04,640 --> 00:09:06,439 Speaker 2: Yeah, so let's take a step back and just talk 160 00:09:06,440 --> 00:09:08,360 Speaker 2: about carbon credits from them, because they're a little kind 161 00:09:08,360 --> 00:09:13,200 Speaker 2: of misunderstood. Carbon credits are basically government issued permits that 162 00:09:13,440 --> 00:09:19,040 Speaker 2: allow companies to pollute, right and as these credits are 163 00:09:19,400 --> 00:09:22,360 Speaker 2: either you know, bought or sold on basically a carbon 164 00:09:22,400 --> 00:09:26,000 Speaker 2: credits market that's developed over the past decade or so. 165 00:09:26,000 --> 00:09:30,280 Speaker 2: So Tesla makes electric vehicles, they produce a lot of 166 00:09:30,320 --> 00:09:33,280 Speaker 2: credits which they don't need, and so they basically sell 167 00:09:33,320 --> 00:09:36,040 Speaker 2: them to the market. They're essentially selling them to legacy 168 00:09:36,040 --> 00:09:39,880 Speaker 2: automakers that still produce gas powered cars. 169 00:09:40,200 --> 00:09:44,439 Speaker 1: So it essentially stays within the industry effectively that they 170 00:09:44,440 --> 00:09:46,760 Speaker 1: are selling it, not to other kind of pollutas kind 171 00:09:46,760 --> 00:09:49,440 Speaker 1: of palaestations and things like that. It is largely being 172 00:09:49,440 --> 00:09:53,120 Speaker 1: sold to other people within the industry who are producing 173 00:09:53,200 --> 00:09:54,120 Speaker 1: traditional vehicles. 174 00:09:54,480 --> 00:09:57,080 Speaker 2: It can be I think those are the logical buyers. 175 00:09:57,080 --> 00:09:59,520 Speaker 2: It doesn't actually have to be, though, which is interesting. 176 00:09:59,520 --> 00:10:02,400 Speaker 2: It depends on the specific carbon credit market. It's not 177 00:10:02,440 --> 00:10:05,440 Speaker 2: like there's a single market out there. But but what's 178 00:10:05,440 --> 00:10:07,840 Speaker 2: funny about these credits is that they kind of just 179 00:10:08,280 --> 00:10:11,600 Speaker 2: they're granted by regulators and they don't cost Tesla anything 180 00:10:11,640 --> 00:10:14,160 Speaker 2: to produce. It's kind of just like free money, right, 181 00:10:14,920 --> 00:10:17,320 Speaker 2: And so Tesla's kind of built up a pretty lucrative 182 00:10:17,400 --> 00:10:22,280 Speaker 2: side business selling these carbon credits. So last year Tesla 183 00:10:22,320 --> 00:10:26,560 Speaker 2: earned two point eight billion dollars from selling carbon credits. 184 00:10:26,600 --> 00:10:30,880 Speaker 2: Now that's not actually a huge part of their revenue. 185 00:10:30,920 --> 00:10:34,840 Speaker 2: That's only three percent of Tesla's revenue. But because these 186 00:10:34,880 --> 00:10:37,360 Speaker 2: credits are able to be just conjured out of thin air, 187 00:10:37,400 --> 00:10:40,920 Speaker 2: there's no production cost to them. It's actually it's forty 188 00:10:40,960 --> 00:10:44,080 Speaker 2: percent of Tesla's profit, which is huge. That's a big 189 00:10:44,160 --> 00:10:48,000 Speaker 2: chunk of Tesla's profit. That Mutual would be profitable without them, 190 00:10:48,080 --> 00:10:50,720 Speaker 2: but the margins would be a lot lower. 191 00:10:51,880 --> 00:10:54,800 Speaker 1: So it's essentially just a byproduct. It is essentially something 192 00:10:54,840 --> 00:10:57,560 Speaker 1: that is that it takes nothing extra for them to 193 00:10:57,600 --> 00:11:01,040 Speaker 1: do this. Is there we talked about at the top 194 00:11:01,080 --> 00:11:03,920 Speaker 1: of the show about the fact that styles have slumped. 195 00:11:05,080 --> 00:11:07,880 Speaker 1: Is there a risk then that Tesla's not going to 196 00:11:07,880 --> 00:11:12,280 Speaker 1: be able to keep up with the commitments that it 197 00:11:12,360 --> 00:11:14,839 Speaker 1: is made in terms of selling carbon credits if they're 198 00:11:14,880 --> 00:11:17,760 Speaker 1: not producing as many vehicles because the demand isn't there. 199 00:11:18,320 --> 00:11:22,559 Speaker 2: Yeah, that's the catch, right, So Tesla is actually committed 200 00:11:23,000 --> 00:11:25,960 Speaker 2: to sell about four point seven billion dollars worth of 201 00:11:25,960 --> 00:11:30,280 Speaker 2: these credits over time. So if their vehicles sail slow, 202 00:11:30,360 --> 00:11:34,559 Speaker 2: which is happening as we speak in Europe, in California, 203 00:11:35,559 --> 00:11:39,480 Speaker 2: especially in the EU and in China, demand is softening, 204 00:11:39,600 --> 00:11:44,640 Speaker 2: Tesla may not generate enough new credits to fulfill those 205 00:11:44,679 --> 00:11:49,880 Speaker 2: promises those contracts. If that's the case, it may you know, 206 00:11:50,000 --> 00:11:52,959 Speaker 2: not just eat into their their profit, but they may 207 00:11:53,000 --> 00:11:55,960 Speaker 2: actually have to buy credits from others, turning you know, 208 00:11:56,200 --> 00:12:00,920 Speaker 2: pure margin windfall into a potential cash liability. Again, it 209 00:12:00,960 --> 00:12:04,040 Speaker 2: remains to be seen. You know, we'll watch very closely 210 00:12:04,080 --> 00:12:07,120 Speaker 2: over the coming quarters Tassela's numbers. But you know, this 211 00:12:07,160 --> 00:12:10,720 Speaker 2: is kind of an underdiscussed area of the business. Definitely 212 00:12:10,720 --> 00:12:11,400 Speaker 2: to keep your eye on. 213 00:12:11,760 --> 00:12:14,040 Speaker 1: It is such a fascinating company, isn't it. There are 214 00:12:14,040 --> 00:12:16,960 Speaker 1: so many aspects to this business, and when you're adding 215 00:12:17,000 --> 00:12:19,360 Speaker 1: all the political angles to it and the fact that 216 00:12:19,400 --> 00:12:22,720 Speaker 1: it is a player globally, there is just so much 217 00:12:23,320 --> 00:12:25,520 Speaker 1: to learn and to discuss about Tesla. 218 00:12:26,280 --> 00:12:28,600 Speaker 2: We'll save the political chat for another time, for sure, 219 00:12:28,960 --> 00:12:29,800 Speaker 2: maybe over a beer. 220 00:12:29,840 --> 00:12:31,839 Speaker 1: Hey, I suspect that's a good idea, all right. To 221 00:12:31,880 --> 00:12:33,480 Speaker 1: find out more, you can head to altz dot co, 222 00:12:33,640 --> 00:12:35,800 Speaker 1: sign up to the newsletter. This episode is actually based 223 00:12:35,840 --> 00:12:38,800 Speaker 1: on an edition of that newsletter, and start learning about 224 00:12:38,800 --> 00:12:41,600 Speaker 1: the future of alternative investing. Thank you very much to FUN. 225 00:12:42,240 --> 00:12:42,880 Speaker 2: Thank you, Michael. 226 00:12:43,040 --> 00:12:45,400 Speaker 1: I'm Michael Thompson from Fear and Greed business news for 227 00:12:45,440 --> 00:12:51,800 Speaker 1: people who make their own decisions. Thanks for your company.