1 00:00:11,800 --> 00:00:14,960 Speaker 1: This is Wall Street Week. I'm David Weston bringing you 2 00:00:15,200 --> 00:00:19,160 Speaker 1: stories of capitalism this week, the world of coffee and 3 00:00:19,200 --> 00:00:22,200 Speaker 1: what global demand and a changing climate may mean for 4 00:00:22,280 --> 00:00:26,280 Speaker 1: our daily habit and creativity from a machine, what jenn 5 00:00:26,320 --> 00:00:29,600 Speaker 1: of Ai means for the world of art and entertainment. 6 00:00:29,920 --> 00:00:33,120 Speaker 1: But we start with a story about popularity, the sort 7 00:00:33,120 --> 00:00:36,760 Speaker 1: of overnight popularity that comes when market players burst onto 8 00:00:36,760 --> 00:00:39,760 Speaker 1: the scene because of big calls they make or new 9 00:00:39,800 --> 00:00:43,640 Speaker 1: products they create, only some of which stand the test 10 00:00:43,840 --> 00:00:44,320 Speaker 1: of time. 11 00:00:46,600 --> 00:00:49,760 Speaker 2: In a world of stayd predictors of the stock market, 12 00:00:49,960 --> 00:00:53,760 Speaker 2: Lane Garzawelli stands out after a series of orn target 13 00:00:53,840 --> 00:00:57,440 Speaker 2: forecasts made her arguably the most widely followed Wall Street guru. 14 00:00:58,000 --> 00:01:00,840 Speaker 3: She was fired last year by Lehman Bros. Reportedly for 15 00:01:00,880 --> 00:01:03,520 Speaker 3: a combination of being bullish indeed a bit too much 16 00:01:03,560 --> 00:01:06,800 Speaker 3: so when the firm was being bearish way too much so, 17 00:01:07,520 --> 00:01:09,000 Speaker 3: and for being too expensive. 18 00:01:09,440 --> 00:01:13,080 Speaker 1: Wall Street analyst Elaine Garzarelli became a superstar in the 19 00:01:13,080 --> 00:01:16,360 Speaker 1: world of finance in nineteen eighty seven when she correctly 20 00:01:16,400 --> 00:01:19,640 Speaker 1: predicted an imminent collapse of the US stock market, took 21 00:01:19,680 --> 00:01:22,600 Speaker 1: her fund to cash, and was proven right just one 22 00:01:22,640 --> 00:01:25,880 Speaker 1: week later. On Black Monday, when the Dow Jones Industrial 23 00:01:25,959 --> 00:01:29,400 Speaker 1: Average dropped twenty two point six percent in a single 24 00:01:29,520 --> 00:01:30,720 Speaker 1: trading session. 25 00:01:30,680 --> 00:01:33,880 Speaker 4: No question about it. She called it absolutely spot on. 26 00:01:34,240 --> 00:01:37,880 Speaker 4: Her fund actually went up quite considerably in value on 27 00:01:38,000 --> 00:01:41,040 Speaker 4: Black Monday because she put on all these puts as protection. 28 00:01:41,200 --> 00:01:45,360 Speaker 1: Bloomberg Senior editor for Markets and Opinion columnists John Authurs 29 00:01:45,680 --> 00:01:49,320 Speaker 1: has been reporting on the markets and forecasters for four decades. 30 00:01:49,720 --> 00:01:54,360 Speaker 4: Did it work out that well? Not really, because the 31 00:01:54,400 --> 00:01:58,960 Speaker 4: whole point of getting one really big call spectacularly right 32 00:01:59,080 --> 00:02:02,080 Speaker 4: like that is the n equals one. If you get 33 00:02:02,080 --> 00:02:05,680 Speaker 4: what I mean. There are only so many opportunities in 34 00:02:05,720 --> 00:02:08,640 Speaker 4: a lifetime to get to call a freak event like 35 00:02:09,160 --> 00:02:12,000 Speaker 4: Black Monday. Having done it once, there wasn't going to 36 00:02:12,000 --> 00:02:13,000 Speaker 4: be another Black Monday. 37 00:02:13,639 --> 00:02:16,880 Speaker 1: In the decades since Black Monday, other investors and fund 38 00:02:16,919 --> 00:02:20,480 Speaker 1: managers have made spectacular calls that have brought fame and 39 00:02:20,760 --> 00:02:24,640 Speaker 1: yes often fortune, at least for a time. One investor 40 00:02:24,720 --> 00:02:27,280 Speaker 1: stole the spotlight in twenty twenty thanks to the gold 41 00:02:27,360 --> 00:02:30,600 Speaker 1: rush into big tech, once again showing the benefits of 42 00:02:30,639 --> 00:02:33,320 Speaker 1: being in the right place at the right time. We 43 00:02:33,440 --> 00:02:37,120 Speaker 1: have some funds, like for example, our investment Yes, which 44 00:02:37,200 --> 00:02:40,080 Speaker 1: did extremely well for a period of time. 45 00:02:40,400 --> 00:02:45,160 Speaker 4: And made Kathy Wood, its manager, probably the most famous 46 00:02:45,200 --> 00:02:47,760 Speaker 4: investor for a couple of years at least. Yes, I 47 00:02:47,800 --> 00:02:50,120 Speaker 4: think one of the points that you begin to grasp 48 00:02:50,360 --> 00:02:53,160 Speaker 4: more and more of the years covering finance is that 49 00:02:53,200 --> 00:02:55,000 Speaker 4: if you really want to make it big, if you 50 00:02:55,040 --> 00:02:56,919 Speaker 4: really want to make a big return, you have to 51 00:02:57,000 --> 00:03:01,040 Speaker 4: take a big risk. That's basically an inescapable fact. If 52 00:03:01,080 --> 00:03:03,240 Speaker 4: you want to be safe, you can be safe, but 53 00:03:03,280 --> 00:03:05,960 Speaker 4: you're not going to be as rich as somebody else 54 00:03:06,200 --> 00:03:08,560 Speaker 4: is who has taken a chance and it's come off. 55 00:03:08,800 --> 00:03:12,800 Speaker 4: In the case of ARC Investment, it very preciently was 56 00:03:12,840 --> 00:03:17,520 Speaker 4: in early on Tesla. It was heavily into bitcoin, bitcoin 57 00:03:17,600 --> 00:03:21,320 Speaker 4: related stocks, heavily into a number of very small companies 58 00:03:21,320 --> 00:03:26,440 Speaker 4: that grew very fast in the exciting conditions during the pandemic. 59 00:03:26,840 --> 00:03:30,160 Speaker 4: So ARC at one point, I think quadruples during the 60 00:03:30,200 --> 00:03:34,920 Speaker 4: pandemic year. Some such astronomically strong performance. 61 00:03:35,800 --> 00:03:38,560 Speaker 1: In recent years, a broad market rally has been good 62 00:03:38,680 --> 00:03:41,200 Speaker 1: news for most investors, but that can come at the 63 00:03:41,280 --> 00:03:43,400 Speaker 1: cost of specific fund performance. 64 00:03:43,760 --> 00:03:47,960 Speaker 4: It looks like a rocket or trajectory that is zoomed 65 00:03:48,080 --> 00:03:52,640 Speaker 4: up and zoomed back down again. If you stand back 66 00:03:52,680 --> 00:03:57,120 Speaker 4: and think about whether you should treat somebody like Cathywood 67 00:03:57,120 --> 00:04:01,760 Speaker 4: as an expert who can call the market liably every time. 68 00:04:02,520 --> 00:04:05,200 Speaker 4: If you're going to have a return like that, it 69 00:04:05,280 --> 00:04:07,400 Speaker 4: implies that you're very good at what you do, which 70 00:04:07,440 --> 00:04:09,800 Speaker 4: in her case is tech investing. This is a person 71 00:04:09,800 --> 00:04:14,920 Speaker 4: whose talents aligned perfectly with capturing this one moment. That 72 00:04:14,960 --> 00:04:17,400 Speaker 4: doesn't mean that we should assume that they can guide 73 00:04:17,480 --> 00:04:19,039 Speaker 4: us further in the future. 74 00:04:19,880 --> 00:04:23,400 Speaker 1: Big contrarian calls can be very popular because of the 75 00:04:23,440 --> 00:04:26,159 Speaker 1: attention they attract and because of the money they make 76 00:04:26,200 --> 00:04:28,960 Speaker 1: for those who take the leap of faith. But betting 77 00:04:28,960 --> 00:04:32,279 Speaker 1: against the markets also comes at a price, certainly when 78 00:04:32,279 --> 00:04:34,720 Speaker 1: it comes to calls that, unlike those of a Lane 79 00:04:34,760 --> 00:04:38,359 Speaker 1: Garzarelli and Kathy Wood, don't come true, but also in 80 00:04:38,400 --> 00:04:41,839 Speaker 1: trading off drama for slow, more quiet progress. 81 00:04:42,320 --> 00:04:47,159 Speaker 4: The people who do best tend to be contrarians. They 82 00:04:47,160 --> 00:04:50,160 Speaker 4: have to be, and most of the time, by definition, 83 00:04:50,279 --> 00:04:54,360 Speaker 4: contrarians don't do that well, otherwise they wouldn't be contrarian. 84 00:04:54,520 --> 00:04:57,720 Speaker 4: And I think that's another fact that people tend to 85 00:04:57,800 --> 00:05:00,640 Speaker 4: forget that if you're really going to make a lot 86 00:05:00,680 --> 00:05:05,600 Speaker 4: of money, you probably do need to partic a contrarian bet. 87 00:05:06,320 --> 00:05:10,600 Speaker 4: But generally speaking, people aren't stupid, the market isn't wildly 88 00:05:10,680 --> 00:05:15,600 Speaker 4: wrong most of the time. Being very contrarian will often 89 00:05:15,680 --> 00:05:16,359 Speaker 4: lose you money. 90 00:05:17,160 --> 00:05:20,279 Speaker 1: It's not just individual market players who can gain enormous 91 00:05:20,279 --> 00:05:24,920 Speaker 1: popularity relatively quickly. That popularity can also come to new 92 00:05:24,920 --> 00:05:29,320 Speaker 1: ways of approaching investing overall, things like so called smart beta. 93 00:05:29,880 --> 00:05:33,520 Speaker 4: This was a great idea that got people very excited 94 00:05:33,560 --> 00:05:37,640 Speaker 4: for a while and has generally now become It's a 95 00:05:37,680 --> 00:05:39,600 Speaker 4: phrase I almost never hear anymore. 96 00:05:40,120 --> 00:05:42,880 Speaker 1: The term smart beta is said to have been coined 97 00:05:42,920 --> 00:05:45,719 Speaker 1: by the consulting firm Willis Towers Watson in two thousand 98 00:05:45,760 --> 00:05:48,599 Speaker 1: and six to describe funds that merge the benefits of 99 00:05:48,680 --> 00:05:52,599 Speaker 1: active and passive management. Smart beta funds don't have a 100 00:05:52,640 --> 00:05:56,280 Speaker 1: manager rebalancing holdings, but they have rules and factors that 101 00:05:56,320 --> 00:05:57,920 Speaker 1: play the role of a manager. 102 00:05:58,560 --> 00:05:58,720 Speaker 5: Well. 103 00:05:58,760 --> 00:06:03,279 Speaker 6: Beta really just measures how much a strategy or a 104 00:06:03,320 --> 00:06:07,280 Speaker 6: stock moves, and alpha is the performance of the stock 105 00:06:07,440 --> 00:06:10,400 Speaker 6: relative to that sensitivity to market movements. 106 00:06:11,200 --> 00:06:14,440 Speaker 1: Rob Arnett is the founder of Research Affiliates, which launched 107 00:06:14,480 --> 00:06:16,920 Speaker 1: its Fundamental Index in two thousand and four. 108 00:06:17,400 --> 00:06:21,240 Speaker 6: The inspiration for it was Fundamental Index, which was our 109 00:06:21,279 --> 00:06:25,719 Speaker 6: idea back in two thousand and four, Fundamental Index simply says, 110 00:06:25,760 --> 00:06:29,400 Speaker 6: why should we weight stocks according to how popular, beloved 111 00:06:29,400 --> 00:06:32,760 Speaker 6: and expensive they are? Why not weight stocks according to 112 00:06:32,800 --> 00:06:36,000 Speaker 6: how big they are in the economy. Now, what that 113 00:06:36,240 --> 00:06:39,200 Speaker 6: does is several things. Firstly, if a stock is a 114 00:06:39,240 --> 00:06:42,719 Speaker 6: growth stock priced at a premium multiple, it reweights it down. 115 00:06:43,279 --> 00:06:45,760 Speaker 6: If it's a value stock priced at a deep discount, 116 00:06:45,880 --> 00:06:49,559 Speaker 6: it rewaights it up. That's not because these aren't good 117 00:06:49,640 --> 00:06:53,440 Speaker 6: companies and these aren't troubled companies. That's because the good 118 00:06:53,480 --> 00:06:55,920 Speaker 6: news is already in the price. The bad news is 119 00:06:55,960 --> 00:06:58,560 Speaker 6: already in the price, so it's harmless to reweight it. 120 00:06:59,320 --> 00:07:02,520 Speaker 1: Because they folks on value stocks, Smart beta funds may 121 00:07:02,600 --> 00:07:04,960 Speaker 1: have missed the moment for the tech stocks known as 122 00:07:04,960 --> 00:07:07,640 Speaker 1: the Magnificent Seven, which have been driving the S and 123 00:07:07,680 --> 00:07:10,840 Speaker 1: P five hundred higher recently. Of the more than nine 124 00:07:10,920 --> 00:07:14,960 Speaker 1: hundred smart Beta ETFs, about six hundred have no exposure 125 00:07:15,000 --> 00:07:18,600 Speaker 1: to the Magnificent seven stocks. According to Bloomberg Intelligence. 126 00:07:18,400 --> 00:07:25,480 Speaker 6: Rules based strategies have had outflows. The genuinely smart forms 127 00:07:25,520 --> 00:07:27,800 Speaker 6: of smart beta have been doing much better than that. 128 00:07:28,040 --> 00:07:31,760 Speaker 6: It leans heavily towards cheap stocks, well cheap stocks have 129 00:07:31,840 --> 00:07:36,160 Speaker 6: been out of favor for the last fifteen years. If 130 00:07:36,200 --> 00:07:41,120 Speaker 6: you had a growth bias, you beat your value counterparts 131 00:07:41,240 --> 00:07:43,880 Speaker 6: for the last fifteen years off and on. Not every year, 132 00:07:43,920 --> 00:07:47,640 Speaker 6: of course, but over the fifteen years by a very 133 00:07:47,680 --> 00:07:51,880 Speaker 6: wide margin. And so anything with a bias towards value 134 00:07:52,120 --> 00:07:55,400 Speaker 6: will have had that headwind and will have struggled accordingly. 135 00:07:56,040 --> 00:07:59,720 Speaker 1: While some trading strategies or financial instruments might go in 136 00:07:59,760 --> 00:08:02,520 Speaker 1: and out of fashion, sometimes what we think of as 137 00:08:02,600 --> 00:08:05,360 Speaker 1: new products in the market really aren't new at all, 138 00:08:05,640 --> 00:08:06,360 Speaker 1: like SPACs. 139 00:08:07,040 --> 00:08:11,360 Speaker 5: David in the early nineties, for those of us who 140 00:08:11,360 --> 00:08:15,440 Speaker 5: are old enough to remember, the US had somewhat of 141 00:08:15,440 --> 00:08:19,880 Speaker 5: the recession, and so there was a capital shortage, and 142 00:08:20,000 --> 00:08:25,320 Speaker 5: two very smart lawyers who represented an industrial company that 143 00:08:25,480 --> 00:08:30,560 Speaker 5: was short of capital and that needed to raise additional 144 00:08:30,600 --> 00:08:34,160 Speaker 5: capital in the public markets came up with the device 145 00:08:34,720 --> 00:08:36,640 Speaker 5: that we now know as the SPAC. 146 00:08:37,160 --> 00:08:40,679 Speaker 1: Betsy Cohen is a financial technology pioneer who founded Internet 147 00:08:40,760 --> 00:08:44,560 Speaker 1: banking for Bank Corp. In nineteen ninety nine and now 148 00:08:44,600 --> 00:08:47,160 Speaker 1: serves as chairman of the Cohen Circle, which she co 149 00:08:47,240 --> 00:08:50,600 Speaker 1: founded in twenty fifteen to bring more than ten SPACs 150 00:08:50,720 --> 00:08:51,800 Speaker 1: to market. 151 00:08:51,640 --> 00:08:58,080 Speaker 5: In a way, the SPAC format and structure has really 152 00:08:58,320 --> 00:09:02,000 Speaker 5: always remained the same and is meeting the same needs, 153 00:09:03,040 --> 00:09:08,599 Speaker 5: and those needs are a capital shortage within a particular area. 154 00:09:08,720 --> 00:09:11,600 Speaker 1: The number of SPACs coming to market peaked in twenty 155 00:09:11,679 --> 00:09:14,440 Speaker 1: twenty one, with six hundred and thirteen just that year, 156 00:09:14,960 --> 00:09:17,920 Speaker 1: and dropped to just thirty one in twenty twenty three. 157 00:09:18,360 --> 00:09:21,800 Speaker 1: If we go back two years twenty twenty two, it 158 00:09:21,880 --> 00:09:24,319 Speaker 1: seemed that there were an awful lot of SPACs. It 159 00:09:24,440 --> 00:09:26,120 Speaker 1: was really almost dominating the market. 160 00:09:26,240 --> 00:09:27,480 Speaker 5: It seemed like a lot of fun. 161 00:09:29,160 --> 00:09:32,040 Speaker 1: It's come down a fair amount since then. Why has 162 00:09:32,080 --> 00:09:32,720 Speaker 1: it come down? 163 00:09:33,360 --> 00:09:39,360 Speaker 5: I think there are several reasons. One of many of 164 00:09:39,360 --> 00:09:45,040 Speaker 5: the SPACs did not find appropriate targets, and secondly, there 165 00:09:45,160 --> 00:09:48,719 Speaker 5: was an excise tax that was imposed if you did 166 00:09:48,920 --> 00:09:53,160 Speaker 5: close the spec, and so that was a financial liability 167 00:09:53,240 --> 00:09:58,280 Speaker 5: that had not been thought of at the beginning of 168 00:09:58,320 --> 00:10:06,160 Speaker 5: the process. So I think that there were enough cases 169 00:10:06,480 --> 00:10:11,240 Speaker 5: of failure in the market that a company would come 170 00:10:11,280 --> 00:10:16,320 Speaker 5: to market and then miss its first earnings projections that 171 00:10:16,559 --> 00:10:21,360 Speaker 5: it became a much riskier vehicle to use. 172 00:10:21,840 --> 00:10:25,760 Speaker 1: Whether it's the latest star investor or hot strategy. What 173 00:10:25,920 --> 00:10:29,839 Speaker 1: draws us to these overnight success stories is just that 174 00:10:29,880 --> 00:10:32,840 Speaker 1: they make for a good story, one that the financial 175 00:10:32,880 --> 00:10:35,680 Speaker 1: media wants to tell and one that the audience wants 176 00:10:35,720 --> 00:10:36,000 Speaker 1: to hear. 177 00:10:36,640 --> 00:10:40,920 Speaker 4: We as journalists, you make a much better story when 178 00:10:40,960 --> 00:10:43,319 Speaker 4: you really do tell tell it with a hero, with 179 00:10:43,400 --> 00:10:47,280 Speaker 4: a person, with a with a with the beginning in 180 00:10:47,320 --> 00:10:49,160 Speaker 4: the middle and an end. You want you want to 181 00:10:49,160 --> 00:10:51,040 Speaker 4: be able to make it into a narrative. Then people 182 00:10:51,040 --> 00:10:53,400 Speaker 4: will enjoy what you're showing them. Then people will understand 183 00:10:53,400 --> 00:10:57,720 Speaker 4: the point you're making. But you might help people fall 184 00:10:57,760 --> 00:11:02,080 Speaker 4: into a narrative fallacy we we the media maybe part 185 00:11:02,400 --> 00:11:07,720 Speaker 4: of building up people only to then have to knock 186 00:11:07,760 --> 00:11:08,200 Speaker 4: them down. 187 00:11:09,040 --> 00:11:12,480 Speaker 1: Today, ETFs have become popular on Wall Street right now. 188 00:11:12,520 --> 00:11:15,240 Speaker 1: They seem to be everyone's answer to just about everything. 189 00:11:15,760 --> 00:11:17,880 Speaker 1: But how long will the love affair last? 190 00:11:18,440 --> 00:11:21,480 Speaker 4: It would appear that they are in this country now. 191 00:11:21,520 --> 00:11:26,480 Speaker 4: In many ways, the ETFs can be a superior product. 192 00:11:27,720 --> 00:11:28,120 Speaker 7: You could. 193 00:11:28,160 --> 00:11:30,439 Speaker 4: You know that they can be much more nimble, they 194 00:11:30,440 --> 00:11:33,160 Speaker 4: can trade that much more rapidly, and therefore they can 195 00:11:33,200 --> 00:11:36,840 Speaker 4: be of more different uses to people. But certainly my 196 00:11:37,920 --> 00:11:44,640 Speaker 4: fear with exchange traded funds it's There's a line from 197 00:11:45,120 --> 00:11:47,800 Speaker 4: one of David Bowe's albums begins with flees the size 198 00:11:47,800 --> 00:11:50,959 Speaker 4: of rats, eats on rats, the size of cats, that 199 00:11:50,200 --> 00:11:54,120 Speaker 4: that that ultimately they are Everybody is just taking a 200 00:11:54,160 --> 00:11:58,680 Speaker 4: different bite of what's ultimately the same thing. That you're 201 00:11:58,760 --> 00:12:03,000 Speaker 4: just churning these assets around in a different way without 202 00:12:03,200 --> 00:12:06,720 Speaker 4: really little The complaints I was making about smart baits 203 00:12:06,760 --> 00:12:09,880 Speaker 4: earlier that you without really adding anything. 204 00:12:12,080 --> 00:12:15,520 Speaker 1: In a world of growing and rapidly changing markets, it's 205 00:12:15,559 --> 00:12:18,360 Speaker 1: no surprise that new people and new ways of investing 206 00:12:18,440 --> 00:12:22,600 Speaker 1: show up regularly, or that we eagerly welcome the next bright, 207 00:12:22,800 --> 00:12:26,040 Speaker 1: shiny object. But it's one thing to rush to them. 208 00:12:26,320 --> 00:12:29,320 Speaker 1: It's another to stay with them too long, because in 209 00:12:29,360 --> 00:12:32,199 Speaker 1: the long run, we are always reminded that that which 210 00:12:32,280 --> 00:12:36,079 Speaker 1: varies greatly from the mean shall eventually return to it. 211 00:12:36,960 --> 00:12:40,080 Speaker 1: Coming up, the big business of hot bruise, we taught 212 00:12:40,120 --> 00:12:43,400 Speaker 1: coffee supply and demand. Next on Wall Street Week. 213 00:12:44,400 --> 00:12:47,840 Speaker 8: You're listening to Bloomberg Wall Straight Week with David Weston 214 00:12:47,960 --> 00:13:01,880 Speaker 8: from Bloomberg Radio. This is Bloomberg Wall Street Week with 215 00:13:02,080 --> 00:13:08,360 Speaker 8: David Weston from Bloomberg Radio. 216 00:13:11,000 --> 00:13:14,400 Speaker 1: This is a story about our habits, those rituals we 217 00:13:14,440 --> 00:13:17,760 Speaker 1: follow every day and come to take for granted, even 218 00:13:17,800 --> 00:13:21,480 Speaker 1: as we start sharing those rituals with hundreds of millions 219 00:13:21,480 --> 00:13:22,200 Speaker 1: of other people. 220 00:13:27,480 --> 00:13:32,000 Speaker 4: This is a fine cup of coffee, Shannon Ravioli, not Roney. 221 00:13:32,240 --> 00:13:35,359 Speaker 9: If you want the thing you loved, you did it. 222 00:13:35,520 --> 00:13:39,319 Speaker 8: Congratulations, world's best cup of coffee. 223 00:13:40,480 --> 00:13:44,839 Speaker 10: We are taking around the three billion cups every day 224 00:13:45,280 --> 00:13:50,080 Speaker 10: in coffee. These means around one hundred and seventy two 225 00:13:50,400 --> 00:13:53,400 Speaker 10: million bags of sixty kilos each bag. 226 00:13:54,240 --> 00:13:58,640 Speaker 1: Venusian Naguera is executive director of the International Coffee Organization, 227 00:13:59,040 --> 00:14:01,000 Speaker 1: a group created more more than a half a century 228 00:14:01,040 --> 00:14:04,560 Speaker 1: ago to encourage trade between countries. She says, the trend 229 00:14:04,559 --> 00:14:07,160 Speaker 1: in coffee consumption is moving in one direction. 230 00:14:07,640 --> 00:14:11,480 Speaker 10: The demand is growing around the world, mainly in Asia 231 00:14:11,559 --> 00:14:15,560 Speaker 10: and also in what we call to the producing countries, 232 00:14:15,920 --> 00:14:20,120 Speaker 10: the producing countries that use it to drink a lot 233 00:14:20,160 --> 00:14:24,200 Speaker 10: of coffee. In the past, where only Ethiopia and Brazil, 234 00:14:24,560 --> 00:14:29,040 Speaker 10: now you have many other producing countries taking a lot 235 00:14:29,080 --> 00:14:35,520 Speaker 10: of coffee. And additionally to that we have now China, Philippines, Vietnam, 236 00:14:35,800 --> 00:14:40,680 Speaker 10: Indonesia taking a lot of coffee together with South Korea, 237 00:14:40,960 --> 00:14:42,920 Speaker 10: Japan and other countries in Asia. 238 00:14:43,920 --> 00:14:48,240 Speaker 1: Wock you All these habits being cultivated around the world 239 00:14:48,320 --> 00:14:51,480 Speaker 1: add up to big business. According to the analysis from 240 00:14:51,600 --> 00:14:55,280 Speaker 1: SMP Global. The annual revenue of the industry worldwide has 241 00:14:55,320 --> 00:14:58,160 Speaker 1: soared from around two hundred billion dollars in twenty nineteen 242 00:14:58,480 --> 00:15:01,280 Speaker 1: to know you four hundred and six billion dollars today. 243 00:15:01,920 --> 00:15:05,960 Speaker 1: But as coffee demand continues to rise, production has been inconsistent. 244 00:15:06,400 --> 00:15:10,080 Speaker 1: In the last two years. Consumption outstripped supply as bad 245 00:15:10,120 --> 00:15:13,440 Speaker 1: weather took a toll on coffee farms that helped send 246 00:15:13,520 --> 00:15:17,120 Speaker 1: prices of Arabica being futures surging more than eighty percent 247 00:15:17,400 --> 00:15:19,760 Speaker 1: in the span of a year, from one hundred and 248 00:15:19,800 --> 00:15:22,520 Speaker 1: forty six in September of twenty twenty three to a 249 00:15:22,600 --> 00:15:25,200 Speaker 1: high of almost two hundred and seventy four this year. 250 00:15:29,040 --> 00:15:32,920 Speaker 11: Coffee as an industry really starts in the late nineteenth century. 251 00:15:32,960 --> 00:15:35,000 Speaker 11: That's really when we see the start of trading, so 252 00:15:35,040 --> 00:15:39,120 Speaker 11: the New York Coffee Exchange opens in the eighteen eighties. 253 00:15:39,480 --> 00:15:42,440 Speaker 1: Jonathan Morris is an expert in the field, dubbed the 254 00:15:42,520 --> 00:15:46,640 Speaker 1: Coffee Historian. He's the author of Coffee, a Global History 255 00:15:46,840 --> 00:15:49,240 Speaker 1: and a professor at the University of Hertfordshire. 256 00:15:49,880 --> 00:15:53,920 Speaker 11: Now we have a much more expanded global market, so 257 00:15:54,000 --> 00:15:57,160 Speaker 11: coffee consumption has been rising. A lot of emulation in 258 00:15:57,200 --> 00:16:02,360 Speaker 11: that rise so essentially that's what we're seeing, and the 259 00:16:02,520 --> 00:16:05,520 Speaker 11: demand for coffee in that sense is ever growing. 260 00:16:06,080 --> 00:16:08,560 Speaker 1: A good part of their growth comes from the Starbucks 261 00:16:08,560 --> 00:16:11,200 Speaker 1: of this world, a phenomenon that may have taken off 262 00:16:11,200 --> 00:16:15,160 Speaker 1: in Seattle, Washington, but it is turned into an international trend. 263 00:16:16,480 --> 00:16:19,920 Speaker 11: So what's really changed coffee in the last sort of 264 00:16:19,960 --> 00:16:23,600 Speaker 11: thirty forty years is obviously the growth of the if 265 00:16:23,640 --> 00:16:28,000 Speaker 11: you like, the international coffee shop chains, that kind of format. 266 00:16:28,560 --> 00:16:32,080 Speaker 11: And I say it's changed it not because it's accounted 267 00:16:32,120 --> 00:16:35,640 Speaker 11: for a huge proportion of consumption, although it's certainly driven 268 00:16:35,720 --> 00:16:38,840 Speaker 11: that up, but rather because of the amount of emulation 269 00:16:38,920 --> 00:16:42,280 Speaker 11: that that's created across the market. So coffee as a 270 00:16:42,320 --> 00:16:45,240 Speaker 11: beverage has become much more popular across the globe. 271 00:16:45,320 --> 00:16:47,480 Speaker 1: It's not just that more of us are spending more 272 00:16:47,520 --> 00:16:50,880 Speaker 1: of our time in our favorite coffee shops. Increasingly, entire 273 00:16:50,960 --> 00:16:54,640 Speaker 1: countries are getting into the game, quickly developing their own 274 00:16:54,760 --> 00:16:58,359 Speaker 1: habits of turning to coffee instead of their traditional beverages. 275 00:16:58,720 --> 00:17:01,640 Speaker 11: So China as a market alone is now probably something 276 00:17:01,760 --> 00:17:05,000 Speaker 11: like in the seventh or sixth in the world in 277 00:17:05,119 --> 00:17:09,400 Speaker 11: terms of volume, and of course the growth across particularly 278 00:17:09,440 --> 00:17:12,600 Speaker 11: Southeast Asia as a whole has really driven the big 279 00:17:12,640 --> 00:17:14,679 Speaker 11: expansion of coffee in the last few years. 280 00:17:17,600 --> 00:17:21,040 Speaker 1: Steven Sutton's job is to meet that demand while strengthening 281 00:17:21,119 --> 00:17:25,040 Speaker 1: the supply chain his New York coffee shops specializing getting 282 00:17:25,080 --> 00:17:28,920 Speaker 1: beans to customers at breakneck speeds. It can take large 283 00:17:29,000 --> 00:17:31,439 Speaker 1: chains up to a year to get coffee from farm 284 00:17:31,560 --> 00:17:35,040 Speaker 1: to cup. Sutton's de Vosion sells beans that have been 285 00:17:35,080 --> 00:17:38,600 Speaker 1: harvested less than a month before hitting shelves. To do that, 286 00:17:38,800 --> 00:17:42,200 Speaker 1: he uses FedEx to overnight the beans straight from Bogata 287 00:17:42,240 --> 00:17:45,760 Speaker 1: to Brooklyn, where he roasts around ten pounds of them 288 00:17:45,800 --> 00:17:50,280 Speaker 1: every week. Within days, they're in cafes, executive dining rooms, 289 00:17:50,280 --> 00:17:53,000 Speaker 1: and high end restaurants like eleven Madison Park. 290 00:17:53,600 --> 00:17:57,720 Speaker 12: We actually opened doors in two thousand and six, and 291 00:17:57,800 --> 00:18:01,480 Speaker 12: the whole idea was to do some different locally in 292 00:18:01,560 --> 00:18:06,399 Speaker 12: Colombia for the farmers and finding absolutely the best coffees 293 00:18:06,960 --> 00:18:11,040 Speaker 12: that were lost at that time because of the internal 294 00:18:11,080 --> 00:18:15,160 Speaker 12: conflict of Colombia. So we decided very early on that 295 00:18:15,240 --> 00:18:18,240 Speaker 12: we were to concentrate our efforts of understanding how to 296 00:18:18,359 --> 00:18:23,080 Speaker 12: go to the farmer, how to say maneuver the territory 297 00:18:23,960 --> 00:18:27,960 Speaker 12: and its difficulties at that time so we can get 298 00:18:28,000 --> 00:18:31,320 Speaker 12: to the farmer pay them actually fair wages, because one 299 00:18:31,359 --> 00:18:35,040 Speaker 12: thing is fair trade and one thing is fair money wages. 300 00:18:35,359 --> 00:18:38,840 Speaker 1: Sun's direct relationship with farmers has been both good practice 301 00:18:38,880 --> 00:18:42,879 Speaker 1: and good business. Better sourcing can mean better coffee, and 302 00:18:42,920 --> 00:18:44,840 Speaker 1: that's been a staple of his success. 303 00:18:45,359 --> 00:18:49,720 Speaker 12: Actually getting the taste that they got at origin, and 304 00:18:49,840 --> 00:18:52,440 Speaker 12: that until now it's pretty much unheard of. So we 305 00:18:53,040 --> 00:18:57,639 Speaker 12: averaged let's call it around fifteen days old coffees before roasting. 306 00:18:57,960 --> 00:19:01,280 Speaker 1: So that sounds remarkable, but also sounds remark expensive. Potentially, 307 00:19:01,440 --> 00:19:05,600 Speaker 1: what's the difference in the price of the fresh coffee, 308 00:19:05,640 --> 00:19:08,760 Speaker 1: the freshly roasted and ground as opposed to things that 309 00:19:08,840 --> 00:19:09,879 Speaker 1: take six months. 310 00:19:10,080 --> 00:19:14,119 Speaker 12: So there's there is a difference, but it's not a 311 00:19:14,200 --> 00:19:16,360 Speaker 12: huge difference. I mean, you're probably going to be paying 312 00:19:16,400 --> 00:19:18,720 Speaker 12: a dollar more for our coffee. If you take out 313 00:19:18,800 --> 00:19:22,200 Speaker 12: a lot of the middle people and a lot of 314 00:19:22,240 --> 00:19:25,679 Speaker 12: the normal process of coffee, you'll end up finding that 315 00:19:25,840 --> 00:19:29,080 Speaker 12: number one, the money can actually go to the farmer. 316 00:19:29,520 --> 00:19:32,280 Speaker 1: Suton says that making sure everyone gets their fair share 317 00:19:32,320 --> 00:19:34,520 Speaker 1: of the pie is one key piece of the puzzle, 318 00:19:34,960 --> 00:19:38,159 Speaker 1: but another is protecting the supply chain. As the rising 319 00:19:38,240 --> 00:19:41,800 Speaker 1: impact of climate change takes a toll on growing conditions. 320 00:19:44,320 --> 00:19:51,080 Speaker 13: The coffee market has been suffering with supply trucks, especially 321 00:19:51,119 --> 00:19:56,199 Speaker 13: giving weather, because of weather problems we had in Brazil. 322 00:19:56,560 --> 00:19:59,680 Speaker 1: Carlos Costa is the chief commercial officer at Hedge Point 323 00:19:59,680 --> 00:20:04,200 Speaker 1: Globe Markets and its spent years analyzing soft commodities like coffee, 324 00:20:04,520 --> 00:20:07,240 Speaker 1: whose futures point to more confidence in the short term 325 00:20:07,280 --> 00:20:09,560 Speaker 1: trade than in the long term investment. 326 00:20:10,119 --> 00:20:13,359 Speaker 14: Cost of production all days in Brazil it's around seven 327 00:20:13,440 --> 00:20:16,720 Speaker 14: hundred has per bag, seven hundred and fifty for Arabica, 328 00:20:17,320 --> 00:20:21,159 Speaker 14: and the price is trading at fourteen hundred fifteen hundred, 329 00:20:21,680 --> 00:20:25,440 Speaker 14: so they are making a quite good amount of profits 330 00:20:25,520 --> 00:20:29,040 Speaker 14: if you compare cost of production and price of selling. 331 00:20:29,400 --> 00:20:32,280 Speaker 14: So farmers are making good money out of that in 332 00:20:32,320 --> 00:20:38,280 Speaker 14: the supply chain. If you see other participants, traders, the 333 00:20:38,320 --> 00:20:41,280 Speaker 14: margin of the trading is not you know, for commodity 334 00:20:41,320 --> 00:20:44,480 Speaker 14: trading is not that much. We're talking about three five 335 00:20:44,560 --> 00:20:48,120 Speaker 14: percent bottom line. But looking ahead and look into the future, 336 00:20:48,320 --> 00:20:52,600 Speaker 14: our sentiment, as is the volatility my continue to stay 337 00:20:52,640 --> 00:20:55,920 Speaker 14: on the high level because of the supply and straints 338 00:20:55,960 --> 00:20:59,440 Speaker 14: we have nowadays and also the lower level of inventories, 339 00:20:59,480 --> 00:21:02,199 Speaker 14: which bring more concerns in the market as well. But 340 00:21:02,720 --> 00:21:06,160 Speaker 14: the main history here is that the world has been 341 00:21:06,200 --> 00:21:10,840 Speaker 14: consuming more coffee year over year, and supplies has not 342 00:21:10,960 --> 00:21:15,159 Speaker 14: been following that growth in the men both because of 343 00:21:15,280 --> 00:21:21,000 Speaker 14: weather situations that affected supplies, but because you also haven't 344 00:21:21,080 --> 00:21:25,080 Speaker 14: saw so much growth in the majority of the origins 345 00:21:25,119 --> 00:21:26,439 Speaker 14: in terms of area. 346 00:21:26,480 --> 00:21:30,000 Speaker 1: Although production has rebounded this year, climate change means supply 347 00:21:30,160 --> 00:21:34,840 Speaker 1: volatility isn't going away and demand will only continue to rise. 348 00:21:35,480 --> 00:21:38,320 Speaker 1: For people like Steven Sutton, who see the whole process 349 00:21:38,359 --> 00:21:41,520 Speaker 1: from start to finish, meeting that demand in the long 350 00:21:41,600 --> 00:21:44,119 Speaker 1: term means investing in the next generation. 351 00:21:44,359 --> 00:21:47,359 Speaker 12: We are very active in doing things for the community 352 00:21:47,440 --> 00:21:51,800 Speaker 12: and the farmer to protect our stakeholders and where we're 353 00:21:51,800 --> 00:21:54,440 Speaker 12: getting our coffee from us we go to a local 354 00:21:54,440 --> 00:21:59,840 Speaker 12: school in Ghaccetta and we are teaching these kits about 355 00:22:00,480 --> 00:22:03,240 Speaker 12: how to do better coffee, how to get better economics. 356 00:22:03,359 --> 00:22:06,879 Speaker 12: We give them tools from a young age not only 357 00:22:06,960 --> 00:22:11,680 Speaker 12: to help them create a business, but also to help us, 358 00:22:12,000 --> 00:22:16,600 Speaker 12: as pretty much all the stakeholders inside of the coffee process, 359 00:22:16,760 --> 00:22:19,520 Speaker 12: to protect where the source is coming from. We like 360 00:22:19,600 --> 00:22:23,080 Speaker 12: to start building something that's more sustainable, and we think 361 00:22:23,240 --> 00:22:26,920 Speaker 12: that not necessarily is what should be done. I think 362 00:22:26,920 --> 00:22:29,840 Speaker 12: it's part of it, but I think it's also part 363 00:22:29,840 --> 00:22:33,400 Speaker 12: of how we farm and how we teach these farmers 364 00:22:33,440 --> 00:22:36,439 Speaker 12: how to protect their farm not only by having coffee 365 00:22:36,440 --> 00:22:39,440 Speaker 12: but other things and protecting their environment. If we can 366 00:22:39,520 --> 00:22:43,639 Speaker 12: do that in a large scale, then I think we 367 00:22:43,720 --> 00:22:47,000 Speaker 12: will have a good future in the coffee world. If 368 00:22:47,040 --> 00:22:51,639 Speaker 12: we can't, then I foreseeing twenty fifty coffee being a 369 00:22:51,720 --> 00:22:52,960 Speaker 12: complete luxury. 370 00:22:53,880 --> 00:22:57,280 Speaker 1: Up next, How artificial intelligence is changing the world of 371 00:22:57,440 --> 00:23:02,480 Speaker 1: art and entertainment, the view from AI and artists themselves. 372 00:23:03,080 --> 00:23:04,640 Speaker 1: Next on Wall Street Week. 373 00:23:13,840 --> 00:23:18,000 Speaker 8: This is Bloomberg Wall Street Week with David Weston from 374 00:23:18,200 --> 00:23:19,119 Speaker 8: Bloomberg Radio. 375 00:23:25,359 --> 00:23:29,240 Speaker 1: This is a story about creativity, something we normally consider 376 00:23:29,280 --> 00:23:33,080 Speaker 1: to be a quintessentially human trait, but something that machines 377 00:23:33,119 --> 00:23:39,679 Speaker 1: increasingly seem to be encroaching on. You really think you 378 00:23:39,680 --> 00:23:43,240 Speaker 1: can just walk away from all this? There's no other choice. 379 00:23:43,880 --> 00:23:45,200 Speaker 11: There's always a choice. 380 00:23:45,720 --> 00:23:47,160 Speaker 12: You just don't like the alternatives. 381 00:23:47,840 --> 00:23:48,680 Speaker 7: What would you have me do? 382 00:23:48,800 --> 00:23:52,560 Speaker 1: This video was made entirely by AI. It was generated 383 00:23:52,600 --> 00:23:56,879 Speaker 1: by Runway, which builds AI tools from multimedia content. Crista 384 00:23:56,920 --> 00:23:59,880 Speaker 1: Baal Bealezuela is the company's founder and CEE. 385 00:24:01,280 --> 00:24:05,200 Speaker 9: You can think of generated AI as a version of CGI. 386 00:24:05,280 --> 00:24:06,919 Speaker 9: It's a better version of the things we've done in 387 00:24:06,920 --> 00:24:09,760 Speaker 9: the past. Imagine a world where you have a system 388 00:24:09,880 --> 00:24:13,119 Speaker 9: that can create imagery on a real time basis. It 389 00:24:13,119 --> 00:24:15,040 Speaker 9: feels like a video game, feels like a movie, but 390 00:24:15,080 --> 00:24:17,040 Speaker 9: you're generating things that you've never seen. 391 00:24:16,840 --> 00:24:22,399 Speaker 1: Before all over the world. Filmmakers, it's frightening on so 392 00:24:22,400 --> 00:24:28,960 Speaker 1: many levels that so many people who will lose their jobs. Musicians, 393 00:24:29,240 --> 00:24:33,639 Speaker 1: the way they are learning and the company is developing, 394 00:24:33,840 --> 00:24:37,040 Speaker 1: it's going to be a threat visual artists. 395 00:24:37,720 --> 00:24:41,160 Speaker 15: Every single professional artist I know has been affected by this, 396 00:24:41,680 --> 00:24:43,080 Speaker 15: and not a good way. 397 00:24:43,960 --> 00:24:46,880 Speaker 1: All worry about what generative AI could mean for their 398 00:24:46,960 --> 00:24:49,720 Speaker 1: creative work, and it turns out that they have good 399 00:24:49,760 --> 00:24:53,400 Speaker 1: reason to worry. A report from January twenty twenty three 400 00:24:53,520 --> 00:24:56,479 Speaker 1: concludes that more than two hundred thousand people in the 401 00:24:56,600 --> 00:25:00,480 Speaker 1: entertainment industry could be adversely affected by AI in twenty 402 00:25:00,520 --> 00:25:05,040 Speaker 1: twenty six, nearly a third of the entire workforce. But 403 00:25:05,200 --> 00:25:08,359 Speaker 1: for those who can use AI, it's already becoming a 404 00:25:08,440 --> 00:25:10,160 Speaker 1: valuable tool in the industry. 405 00:25:10,640 --> 00:25:13,640 Speaker 9: Screenwriters are using Runway, there's a sort of BFx. Artist 406 00:25:13,680 --> 00:25:15,680 Speaker 9: are using Runway, and so every part of the production 407 00:25:15,840 --> 00:25:20,320 Speaker 9: cycle has avenues and spaces where you can augment and 408 00:25:20,400 --> 00:25:24,480 Speaker 9: incorporate it and improve the process using AI. 409 00:25:25,480 --> 00:25:29,320 Speaker 1: Runway is growing fast, training its models to animate and 410 00:25:29,440 --> 00:25:33,600 Speaker 1: alter already existing images in videos, and its adoption in 411 00:25:33,640 --> 00:25:37,200 Speaker 1: the industry is little wonder given what generative AI can 412 00:25:37,280 --> 00:25:39,080 Speaker 1: mean for a company's bottom line. 413 00:25:39,280 --> 00:25:41,719 Speaker 9: So this is your studio, Yes, this is our studio, 414 00:25:41,760 --> 00:25:43,560 Speaker 9: and so today I'm going to show you a with 415 00:25:43,800 --> 00:25:46,959 Speaker 9: creating video that involves no prompts, there's no text, and 416 00:25:47,000 --> 00:25:48,960 Speaker 9: the idea there is that you can take the emotional, 417 00:25:49,000 --> 00:25:51,840 Speaker 9: the expression, the performance of the actor and apply to 418 00:25:52,119 --> 00:25:54,800 Speaker 9: your animated character or live action kind of like element. 419 00:25:55,200 --> 00:25:58,800 Speaker 9: So to me, when I read a few lines, go 420 00:25:58,920 --> 00:25:59,159 Speaker 9: for it. 421 00:26:00,640 --> 00:26:04,720 Speaker 7: No and you're scared. 422 00:26:06,880 --> 00:26:10,800 Speaker 11: Okay, discussed it so discussed like smells really bad. 423 00:26:12,640 --> 00:26:14,400 Speaker 9: So now now what we're going to do is we're 424 00:26:14,440 --> 00:26:16,439 Speaker 9: going to take that performance that Jamie just did and 425 00:26:16,480 --> 00:26:18,760 Speaker 9: we're going to map the un scanned kind of the 426 00:26:18,840 --> 00:26:22,720 Speaker 9: face to translate that set of expressions and emotions you 427 00:26:22,840 --> 00:26:25,720 Speaker 9: just perform for us into an animated character. And that 428 00:26:25,760 --> 00:26:29,240 Speaker 9: happens almost in a roting basis. It's a couple thousand 429 00:26:29,280 --> 00:26:31,720 Speaker 9: dollars for every second of a triple a movie or 430 00:26:31,760 --> 00:26:33,480 Speaker 9: like a good movie. Right, there's a lot of money 431 00:26:33,480 --> 00:26:36,840 Speaker 9: being put into just making a few seconds. The approach 432 00:26:36,960 --> 00:26:39,720 Speaker 9: it takes is like, you can have systems that can 433 00:26:39,800 --> 00:26:42,480 Speaker 9: aid you in creating all of those things in much 434 00:26:42,560 --> 00:26:45,960 Speaker 9: shorter times for a fraction of the cost. And so 435 00:26:46,080 --> 00:26:49,800 Speaker 9: if rendering might take you traditional methods a couple thousand dollars, 436 00:26:50,119 --> 00:26:52,399 Speaker 9: rendering for us is just like less than a dollar. 437 00:26:53,440 --> 00:26:56,840 Speaker 1: To take advantage of that kind of cost savings, Entertainment 438 00:26:56,840 --> 00:26:59,680 Speaker 1: companies are planning to ramp up their use of generative 439 00:26:59,680 --> 00:27:03,600 Speaker 1: AI a lot. A recent survey showed companies plan to 440 00:27:03,680 --> 00:27:07,040 Speaker 1: use AI extensively in the next three years on everything 441 00:27:07,040 --> 00:27:11,000 Speaker 1: from sound design and cinematography to voice acting and writing, 442 00:27:11,480 --> 00:27:14,040 Speaker 1: which may be good for the companies, but isn't so 443 00:27:14,160 --> 00:27:15,439 Speaker 1: good for the creators. 444 00:27:16,600 --> 00:27:18,600 Speaker 15: You know what sucks is like I like these colors, 445 00:27:18,640 --> 00:27:22,160 Speaker 15: and I like the idea, but I would never paint 446 00:27:22,200 --> 00:27:25,639 Speaker 15: it like that. It's so boring. She has absolutely no 447 00:27:25,720 --> 00:27:26,760 Speaker 15: emotion in her face. 448 00:27:28,080 --> 00:27:31,680 Speaker 1: Kelly mcckernin is a classically trained artist in Nashville who 449 00:27:31,680 --> 00:27:34,879 Speaker 1: built a following with a distinctive style of illustration and 450 00:27:34,960 --> 00:27:39,600 Speaker 1: watercolor portraits. But two years ago Kelly started finding images 451 00:27:39,640 --> 00:27:42,159 Speaker 1: on the Internet that looked eerily familiar. 452 00:27:43,119 --> 00:27:47,000 Speaker 15: Summer twenty twenty two was when I first saw some 453 00:27:47,200 --> 00:27:52,240 Speaker 15: images coming out of Dolly and mid Journey and a 454 00:27:52,280 --> 00:27:56,880 Speaker 15: couple of other AI generators. It was very confusing because 455 00:27:57,119 --> 00:27:59,960 Speaker 15: I wanted to know how they did that. At this point, 456 00:28:00,640 --> 00:28:04,199 Speaker 15: there are more images online using my name as a 457 00:28:04,280 --> 00:28:09,040 Speaker 15: prompt than there are actual artworks I've made. So because 458 00:28:09,040 --> 00:28:13,560 Speaker 15: of this, between twenty twenty two and twenty twenty three, 459 00:28:13,680 --> 00:28:16,880 Speaker 15: I saw my income drop by thirty percent. So after 460 00:28:16,960 --> 00:28:20,280 Speaker 15: ten years as a professional artist, I'm no longer able 461 00:28:20,320 --> 00:28:22,240 Speaker 15: to make a full time living off of my work. 462 00:28:23,560 --> 00:28:26,680 Speaker 15: For twenty years, I've had artwork online, and so all 463 00:28:26,680 --> 00:28:31,000 Speaker 15: that art is hoovered up by these companies who believe 464 00:28:31,200 --> 00:28:35,520 Speaker 15: because it's publicly available it's fair use. They should have 465 00:28:36,080 --> 00:28:40,000 Speaker 15: properly licensed these images. We should have been compensated and 466 00:28:40,040 --> 00:28:43,080 Speaker 15: at the very least given our consent, but that didn't happen. 467 00:28:44,640 --> 00:28:48,200 Speaker 1: McKernan has joined with other artists to sue several generitive 468 00:28:48,240 --> 00:28:53,160 Speaker 1: AI firms, including Runway, for claims of copyright violation stemming 469 00:28:53,160 --> 00:28:56,200 Speaker 1: from the company's use of images of their work taken 470 00:28:56,200 --> 00:29:00,840 Speaker 1: from the Internet and used to train AI models entertainment. 471 00:29:00,880 --> 00:29:04,760 Speaker 1: IP lawyer Jennifer Wallace believes McKernan and other creators have 472 00:29:04,880 --> 00:29:05,800 Speaker 1: a strong case. 473 00:29:06,920 --> 00:29:10,680 Speaker 16: If the AI companies want to enter into licenses with 474 00:29:11,160 --> 00:29:14,720 Speaker 16: the companies that own the copyrighted material, they absolutely can 475 00:29:14,760 --> 00:29:17,800 Speaker 16: do that. But that's not what they're doing. And it's 476 00:29:18,040 --> 00:29:19,920 Speaker 16: kind of the model of the tech industry where I'd 477 00:29:19,920 --> 00:29:21,680 Speaker 16: ask for forgiveness, not permission. 478 00:29:23,520 --> 00:29:27,480 Speaker 15: What I was afraid of happening did happen where instead 479 00:29:27,520 --> 00:29:30,840 Speaker 15: of a company reaching out to me, or perhaps a 480 00:29:31,480 --> 00:29:36,400 Speaker 15: small publisher reaching out and wanting to commission me for 481 00:29:36,640 --> 00:29:40,640 Speaker 15: book cover, for example, they could instead save a lot 482 00:29:40,680 --> 00:29:43,360 Speaker 15: of money by just going into one of these programs 483 00:29:43,600 --> 00:29:46,720 Speaker 15: and typing in the image they want and then saying 484 00:29:46,840 --> 00:29:50,000 Speaker 15: in the style of Kelly mcckernan instead of hiring me, 485 00:29:50,560 --> 00:29:53,440 Speaker 15: And this is exactly what started to happen. There were 486 00:29:54,000 --> 00:29:58,000 Speaker 15: well over ten thousand images through the Journey alone by 487 00:29:58,040 --> 00:30:01,280 Speaker 15: the end of twenty twenty two that I could publicly 488 00:30:01,360 --> 00:30:04,840 Speaker 15: search where my name was used to create images. 489 00:30:08,280 --> 00:30:13,440 Speaker 16: So there's three ways that the industry is going about it. Right. 490 00:30:13,520 --> 00:30:17,960 Speaker 16: There's federal legislation that creates a new IP right for 491 00:30:18,480 --> 00:30:24,560 Speaker 16: artificially generated name, image, and likeness. There's licenses. So sag 492 00:30:24,680 --> 00:30:28,720 Speaker 16: Aftra just entered into an agreement with an AI company 493 00:30:28,720 --> 00:30:32,320 Speaker 16: that generates very realistic name, image, and likeness and voice 494 00:30:32,400 --> 00:30:35,560 Speaker 16: replicas of its actors, and it entered into a contract 495 00:30:35,600 --> 00:30:38,400 Speaker 16: that allows the actors to decide if they want to 496 00:30:38,440 --> 00:30:41,640 Speaker 16: allow this company to be able to replicate their voice 497 00:30:41,800 --> 00:30:46,440 Speaker 16: and to set their own prices for it, and then 498 00:30:46,800 --> 00:30:50,760 Speaker 16: you have the litigation that's going through the court systems. 499 00:30:51,440 --> 00:30:54,440 Speaker 1: Valezuela contends that the copyright claim is based on a 500 00:30:54,480 --> 00:30:58,480 Speaker 1: fundamental confusion about how his generative AI models work, a 501 00:30:58,520 --> 00:31:02,320 Speaker 1: confusion indicated by the very word copy right, because he 502 00:31:02,360 --> 00:31:05,920 Speaker 1: says generative AI is not copying, it's learning. 503 00:31:06,600 --> 00:31:09,000 Speaker 9: It's good to remember that these models don't copy data. 504 00:31:09,040 --> 00:31:11,440 Speaker 9: They're not trying to replicate existing data. Like the goal 505 00:31:11,880 --> 00:31:13,680 Speaker 9: for a good model is not to create a scene 506 00:31:13,680 --> 00:31:16,560 Speaker 9: that already aciss there's no point in it. There's no database, 507 00:31:16,600 --> 00:31:19,720 Speaker 9: so you're not pulling from existing scenes. What you're doing 508 00:31:19,880 --> 00:31:22,520 Speaker 9: is you can think about it as a as a student. 509 00:31:23,000 --> 00:31:26,360 Speaker 9: You're showing that student many many different films, and you're 510 00:31:26,440 --> 00:31:29,120 Speaker 9: learning the principles of how a camera moves, what different 511 00:31:29,160 --> 00:31:31,640 Speaker 9: lenses You can use what a performer looks like, and 512 00:31:31,680 --> 00:31:35,080 Speaker 9: then you're asking that model to create new versions of 513 00:31:35,120 --> 00:31:37,480 Speaker 9: what you want. But the things that are creating have 514 00:31:37,600 --> 00:31:41,040 Speaker 9: never been seen before, have never been generated before. 515 00:31:41,760 --> 00:31:46,640 Speaker 7: I'm very skeptical. I could put the history of William 516 00:31:46,720 --> 00:31:51,160 Speaker 7: Randolph Hirst into a large learning model and I wouldn't 517 00:31:51,320 --> 00:31:53,080 Speaker 7: get Citizen. 518 00:31:52,760 --> 00:31:58,840 Speaker 1: Kain, whatever the legal issues involved. Jennathan Taplan says generative 519 00:31:58,880 --> 00:32:02,120 Speaker 1: AI poses a real threat not to the top of 520 00:32:02,160 --> 00:32:04,760 Speaker 1: the creative food chain, but to the vast majority of 521 00:32:04,800 --> 00:32:07,560 Speaker 1: creative artists and writers who make up the core of 522 00:32:07,560 --> 00:32:11,120 Speaker 1: the entertainment industry, people of whom we may never have heard. 523 00:32:11,880 --> 00:32:15,120 Speaker 7: It's not necessarily a real threat to Bob Dylan, but 524 00:32:15,200 --> 00:32:18,600 Speaker 7: it's a real threat to the what I would call 525 00:32:18,640 --> 00:32:24,200 Speaker 7: the working musician who may get one thousand dollars a 526 00:32:24,320 --> 00:32:27,600 Speaker 7: month or a quarter from Spotify. 527 00:32:28,840 --> 00:32:32,400 Speaker 1: In the end, the issues surrounding generative AI and its 528 00:32:32,400 --> 00:32:35,600 Speaker 1: effect on the entertainment industry likely do not come down 529 00:32:35,640 --> 00:32:39,760 Speaker 1: to the law or even the financial consequences for individual artists. 530 00:32:40,440 --> 00:32:44,640 Speaker 1: It's all about creativity and whether generative AI holds out 531 00:32:44,640 --> 00:32:48,640 Speaker 1: the potential for increasing human creativity or drowning it under 532 00:32:48,640 --> 00:32:51,200 Speaker 1: a seat of duplicative content we don't need. 533 00:32:51,840 --> 00:32:55,320 Speaker 9: We've always seen AI as a baseline for a new 534 00:32:55,480 --> 00:32:58,280 Speaker 9: media format, a new way of storytelling, a new set 535 00:32:58,320 --> 00:33:00,880 Speaker 9: of creative an artist. The gold is not technology. We 536 00:33:00,920 --> 00:33:03,320 Speaker 9: don't build models for the sake of building models. But 537 00:33:03,360 --> 00:33:06,160 Speaker 9: it's important to understand that technology serves a human need. 538 00:33:06,360 --> 00:33:09,120 Speaker 9: And if you don't emphasize the human need, then there's 539 00:33:09,160 --> 00:33:12,080 Speaker 9: no goal. And so we've always on emphasize that aspect 540 00:33:12,120 --> 00:33:14,960 Speaker 9: of FAID, and our aspect is to create great storytelling 541 00:33:14,960 --> 00:33:16,080 Speaker 9: tools more than anything else. 542 00:33:17,840 --> 00:33:21,920 Speaker 1: There's little doubt that AI will make storytelling faster, cheaper, 543 00:33:22,320 --> 00:33:25,640 Speaker 1: less labor intensive. But that means it will also cut 544 00:33:25,680 --> 00:33:28,400 Speaker 1: out the need for people to make thousands of small 545 00:33:28,440 --> 00:33:31,320 Speaker 1: decisions like the color of the sky in a movie, 546 00:33:31,760 --> 00:33:34,800 Speaker 1: or the balance between woodwinds and strings and a symphony, 547 00:33:35,280 --> 00:33:39,280 Speaker 1: creative judgments made in nanoseconds by something that's learned from us, 548 00:33:39,600 --> 00:33:42,680 Speaker 1: but that is not us. Will it be as good 549 00:33:42,840 --> 00:33:46,080 Speaker 1: or better than what we humans would have done? Perhaps 550 00:33:46,200 --> 00:33:57,400 Speaker 1: on certainly the AI models would think. So that does 551 00:33:57,440 --> 00:33:59,479 Speaker 1: it for this edition of Bloomberg Wall Street Week. If 552 00:33:59,480 --> 00:34:02,080 Speaker 1: you missed any part of today's program, you can listen 553 00:34:02,160 --> 00:34:05,200 Speaker 1: on demand with our Wall Street Week podcast. Find that 554 00:34:05,320 --> 00:34:08,759 Speaker 1: on Apple, Spotify, or anywhere else you get your podcasts. 555 00:34:09,040 --> 00:34:12,040 Speaker 1: I'm David Weston. Stay with us. Today's top stories and 556 00:34:12,200 --> 00:34:14,960 Speaker 1: global business headlines are coming up right now.