WEBVTT - Bloomberg Wall Street Week - November 15th, 2024

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<v Speaker 1>This is Wall Street Week. I'm David Weston bringing you

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<v Speaker 1>stories of capitalism this week, the world of coffee and

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<v Speaker 1>what global demand and a changing climate may mean for

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<v Speaker 1>our daily habit and creativity from a machine, what jenn

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<v Speaker 1>of Ai means for the world of art and entertainment.

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<v Speaker 1>But we start with a story about popularity, the sort

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<v Speaker 1>of overnight popularity that comes when market players burst onto

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<v Speaker 1>the scene because of big calls they make or new

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<v Speaker 1>products they create, only some of which stand the test

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<v Speaker 1>of time.

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<v Speaker 2>In a world of stayd predictors of the stock market,

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<v Speaker 2>Lane Garzawelli stands out after a series of orn target

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<v Speaker 2>forecasts made her arguably the most widely followed Wall Street guru.

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<v Speaker 3>She was fired last year by Lehman Bros. Reportedly for

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<v Speaker 3>a combination of being bullish indeed a bit too much

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<v Speaker 3>so when the firm was being bearish way too much so,

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<v Speaker 3>and for being too expensive.

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<v Speaker 1>Wall Street analyst Elaine Garzarelli became a superstar in the

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<v Speaker 1>world of finance in nineteen eighty seven when she correctly

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<v Speaker 1>predicted an imminent collapse of the US stock market, took

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<v Speaker 1>her fund to cash, and was proven right just one

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<v Speaker 1>week later. On Black Monday, when the Dow Jones Industrial

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<v Speaker 1>Average dropped twenty two point six percent in a single

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<v Speaker 1>trading session.

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<v Speaker 4>No question about it. She called it absolutely spot on.

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<v Speaker 4>Her fund actually went up quite considerably in value on

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<v Speaker 4>Black Monday because she put on all these puts as protection.

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<v Speaker 1>Bloomberg Senior editor for Markets and Opinion columnists John Authurs

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<v Speaker 1>has been reporting on the markets and forecasters for four decades.

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<v Speaker 4>Did it work out that well? Not really, because the

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<v Speaker 4>whole point of getting one really big call spectacularly right

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<v Speaker 4>like that is the n equals one. If you get

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<v Speaker 4>what I mean. There are only so many opportunities in

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<v Speaker 4>a lifetime to get to call a freak event like

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<v Speaker 4>Black Monday. Having done it once, there wasn't going to

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<v Speaker 4>be another Black Monday.

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<v Speaker 1>In the decades since Black Monday, other investors and fund

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<v Speaker 1>managers have made spectacular calls that have brought fame and

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<v Speaker 1>yes often fortune, at least for a time. One investor

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<v Speaker 1>stole the spotlight in twenty twenty thanks to the gold

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<v Speaker 1>rush into big tech, once again showing the benefits of

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<v Speaker 1>being in the right place at the right time. We

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<v Speaker 1>have some funds, like for example, our investment Yes, which

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<v Speaker 1>did extremely well for a period of time.

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<v Speaker 4>And made Kathy Wood, its manager, probably the most famous

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<v Speaker 4>investor for a couple of years at least. Yes, I

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<v Speaker 4>think one of the points that you begin to grasp

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<v Speaker 4>more and more of the years covering finance is that

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<v Speaker 4>if you really want to make it big, if you

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<v Speaker 4>really want to make a big return, you have to

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<v Speaker 4>take a big risk. That's basically an inescapable fact. If

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<v Speaker 4>you want to be safe, you can be safe, but

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<v Speaker 4>you're not going to be as rich as somebody else

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<v Speaker 4>is who has taken a chance and it's come off.

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<v Speaker 4>In the case of ARC Investment, it very preciently was

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<v Speaker 4>in early on Tesla. It was heavily into bitcoin, bitcoin

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<v Speaker 4>related stocks, heavily into a number of very small companies

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<v Speaker 4>that grew very fast in the exciting conditions during the pandemic.

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<v Speaker 4>So ARC at one point, I think quadruples during the

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<v Speaker 4>pandemic year. Some such astronomically strong performance.

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<v Speaker 1>In recent years, a broad market rally has been good

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<v Speaker 1>news for most investors, but that can come at the

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<v Speaker 1>cost of specific fund performance.

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<v Speaker 4>It looks like a rocket or trajectory that is zoomed

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<v Speaker 4>up and zoomed back down again. If you stand back

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<v Speaker 4>and think about whether you should treat somebody like Cathywood

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<v Speaker 4>as an expert who can call the market liably every time.

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<v Speaker 4>If you're going to have a return like that, it

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<v Speaker 4>implies that you're very good at what you do, which

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<v Speaker 4>in her case is tech investing. This is a person

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<v Speaker 4>whose talents aligned perfectly with capturing this one moment. That

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<v Speaker 4>doesn't mean that we should assume that they can guide

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<v Speaker 4>us further in the future.

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<v Speaker 1>Big contrarian calls can be very popular because of the

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<v Speaker 1>attention they attract and because of the money they make

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<v Speaker 1>for those who take the leap of faith. But betting

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<v Speaker 1>against the markets also comes at a price, certainly when

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<v Speaker 1>it comes to calls that, unlike those of a Lane

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<v Speaker 1>Garzarelli and Kathy Wood, don't come true, but also in

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<v Speaker 1>trading off drama for slow, more quiet progress.

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<v Speaker 4>The people who do best tend to be contrarians. They

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<v Speaker 4>have to be, and most of the time, by definition,

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<v Speaker 4>contrarians don't do that well, otherwise they wouldn't be contrarian.

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<v Speaker 4>And I think that's another fact that people tend to

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<v Speaker 4>forget that if you're really going to make a lot

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<v Speaker 4>of money, you probably do need to partic a contrarian bet.

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<v Speaker 4>But generally speaking, people aren't stupid, the market isn't wildly

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<v Speaker 4>wrong most of the time. Being very contrarian will often

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<v Speaker 4>lose you money.

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<v Speaker 1>It's not just individual market players who can gain enormous

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<v Speaker 1>popularity relatively quickly. That popularity can also come to new

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<v Speaker 1>ways of approaching investing overall, things like so called smart beta.

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<v Speaker 4>This was a great idea that got people very excited

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<v Speaker 4>for a while and has generally now become It's a

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<v Speaker 4>phrase I almost never hear anymore.

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<v Speaker 1>The term smart beta is said to have been coined

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<v Speaker 1>by the consulting firm Willis Towers Watson in two thousand

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<v Speaker 1>and six to describe funds that merge the benefits of

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<v Speaker 1>active and passive management. Smart beta funds don't have a

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<v Speaker 1>manager rebalancing holdings, but they have rules and factors that

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<v Speaker 1>play the role of a manager.

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<v Speaker 5>Well.

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<v Speaker 6>Beta really just measures how much a strategy or a

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<v Speaker 6>stock moves, and alpha is the performance of the stock

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<v Speaker 6>relative to that sensitivity to market movements.

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<v Speaker 1>Rob Arnett is the founder of Research Affiliates, which launched

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<v Speaker 1>its Fundamental Index in two thousand and four.

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<v Speaker 6>The inspiration for it was Fundamental Index, which was our

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<v Speaker 6>idea back in two thousand and four, Fundamental Index simply says,

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<v Speaker 6>why should we weight stocks according to how popular, beloved

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<v Speaker 6>and expensive they are? Why not weight stocks according to

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<v Speaker 6>how big they are in the economy. Now, what that

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<v Speaker 6>does is several things. Firstly, if a stock is a

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<v Speaker 6>growth stock priced at a premium multiple, it reweights it down.

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<v Speaker 6>If it's a value stock priced at a deep discount,

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<v Speaker 6>it rewaights it up. That's not because these aren't good

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<v Speaker 6>companies and these aren't troubled companies. That's because the good

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<v Speaker 6>news is already in the price. The bad news is

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<v Speaker 6>already in the price, so it's harmless to reweight it.

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<v Speaker 1>Because they folks on value stocks, Smart beta funds may

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<v Speaker 1>have missed the moment for the tech stocks known as

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<v Speaker 1>the Magnificent Seven, which have been driving the S and

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<v Speaker 1>P five hundred higher recently. Of the more than nine

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<v Speaker 1>hundred smart Beta ETFs, about six hundred have no exposure

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<v Speaker 1>to the Magnificent seven stocks. According to Bloomberg Intelligence.

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<v Speaker 6>Rules based strategies have had outflows. The genuinely smart forms

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<v Speaker 6>of smart beta have been doing much better than that.

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<v Speaker 6>It leans heavily towards cheap stocks, well cheap stocks have

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<v Speaker 6>been out of favor for the last fifteen years. If

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<v Speaker 6>you had a growth bias, you beat your value counterparts

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<v Speaker 6>for the last fifteen years off and on. Not every year,

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<v Speaker 6>of course, but over the fifteen years by a very

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<v Speaker 6>wide margin. And so anything with a bias towards value

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<v Speaker 6>will have had that headwind and will have struggled accordingly.

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<v Speaker 1>While some trading strategies or financial instruments might go in

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<v Speaker 1>and out of fashion, sometimes what we think of as

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<v Speaker 1>new products in the market really aren't new at all,

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<v Speaker 1>like SPACs.

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<v Speaker 5>David in the early nineties, for those of us who

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<v Speaker 5>are old enough to remember, the US had somewhat of

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<v Speaker 5>the recession, and so there was a capital shortage, and

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<v Speaker 5>two very smart lawyers who represented an industrial company that

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<v Speaker 5>was short of capital and that needed to raise additional

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<v Speaker 5>capital in the public markets came up with the device

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<v Speaker 5>that we now know as the SPAC.

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<v Speaker 1>Betsy Cohen is a financial technology pioneer who founded Internet

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<v Speaker 1>banking for Bank Corp. In nineteen ninety nine and now

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<v Speaker 1>serves as chairman of the Cohen Circle, which she co

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<v Speaker 1>founded in twenty fifteen to bring more than ten SPACs

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<v Speaker 1>to market.

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<v Speaker 5>In a way, the SPAC format and structure has really

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<v Speaker 5>always remained the same and is meeting the same needs,

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<v Speaker 5>and those needs are a capital shortage within a particular area.

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<v Speaker 1>The number of SPACs coming to market peaked in twenty

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<v Speaker 1>twenty one, with six hundred and thirteen just that year,

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<v Speaker 1>and dropped to just thirty one in twenty twenty three.

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<v Speaker 1>If we go back two years twenty twenty two, it

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<v Speaker 1>seemed that there were an awful lot of SPACs. It

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<v Speaker 1>was really almost dominating the market.

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<v Speaker 5>It seemed like a lot of fun.

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<v Speaker 1>It's come down a fair amount since then. Why has

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<v Speaker 1>it come down?

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<v Speaker 5>I think there are several reasons. One of many of

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<v Speaker 5>the SPACs did not find appropriate targets, and secondly, there

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<v Speaker 5>was an excise tax that was imposed if you did

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<v Speaker 5>close the spec, and so that was a financial liability

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<v Speaker 5>that had not been thought of at the beginning of

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<v Speaker 5>the process. So I think that there were enough cases

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<v Speaker 5>of failure in the market that a company would come

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<v Speaker 5>to market and then miss its first earnings projections that

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<v Speaker 5>it became a much riskier vehicle to use.

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<v Speaker 1>Whether it's the latest star investor or hot strategy. What

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<v Speaker 1>draws us to these overnight success stories is just that

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<v Speaker 1>they make for a good story, one that the financial

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<v Speaker 1>media wants to tell and one that the audience wants

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<v Speaker 1>to hear.

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<v Speaker 4>We as journalists, you make a much better story when

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<v Speaker 4>you really do tell tell it with a hero, with

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<v Speaker 4>a person, with a with a with the beginning in

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<v Speaker 4>the middle and an end. You want you want to

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<v Speaker 4>be able to make it into a narrative. Then people

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<v Speaker 4>will enjoy what you're showing them. Then people will understand

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<v Speaker 4>the point you're making. But you might help people fall

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<v Speaker 4>into a narrative fallacy we we the media maybe part

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<v Speaker 4>of building up people only to then have to knock

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<v Speaker 4>them down.

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<v Speaker 1>Today, ETFs have become popular on Wall Street right now.

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<v Speaker 1>They seem to be everyone's answer to just about everything.

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<v Speaker 1>But how long will the love affair last?

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<v Speaker 4>It would appear that they are in this country now.

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<v Speaker 4>In many ways, the ETFs can be a superior product.

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<v Speaker 7>You could.

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<v Speaker 4>You know that they can be much more nimble, they

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<v Speaker 4>can trade that much more rapidly, and therefore they can

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<v Speaker 4>be of more different uses to people. But certainly my

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<v Speaker 4>fear with exchange traded funds it's There's a line from

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<v Speaker 4>one of David Bowe's albums begins with flees the size

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<v Speaker 4>of rats, eats on rats, the size of cats, that

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<v Speaker 4>that that ultimately they are Everybody is just taking a

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<v Speaker 4>different bite of what's ultimately the same thing. That you're

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<v Speaker 4>just churning these assets around in a different way without

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<v Speaker 4>really little The complaints I was making about smart baits

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<v Speaker 4>earlier that you without really adding anything.

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<v Speaker 1>In a world of growing and rapidly changing markets, it's

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<v Speaker 1>no surprise that new people and new ways of investing

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<v Speaker 1>show up regularly, or that we eagerly welcome the next bright,

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<v Speaker 1>shiny object. But it's one thing to rush to them.

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<v Speaker 1>It's another to stay with them too long, because in

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<v Speaker 1>the long run, we are always reminded that that which

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<v Speaker 1>varies greatly from the mean shall eventually return to it.

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<v Speaker 1>Coming up, the big business of hot bruise, we taught

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<v Speaker 1>coffee supply and demand. Next on Wall Street Week.

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<v Speaker 8>You're listening to Bloomberg Wall Straight Week with David Weston

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<v Speaker 8>from Bloomberg Radio. This is Bloomberg Wall Street Week with

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<v Speaker 8>David Weston from Bloomberg Radio.

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<v Speaker 1>This is a story about our habits, those rituals we

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<v Speaker 1>follow every day and come to take for granted, even

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<v Speaker 1>as we start sharing those rituals with hundreds of millions

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<v Speaker 1>of other people.

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<v Speaker 4>This is a fine cup of coffee, Shannon Ravioli, not Roney.

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<v Speaker 9>If you want the thing you loved, you did it.

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<v Speaker 8>Congratulations, world's best cup of coffee.

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<v Speaker 10>We are taking around the three billion cups every day

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<v Speaker 10>in coffee. These means around one hundred and seventy two

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<v Speaker 10>million bags of sixty kilos each bag.

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<v Speaker 1>Venusian Naguera is executive director of the International Coffee Organization,

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<v Speaker 1>a group created more more than a half a century

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<v Speaker 1>ago to encourage trade between countries. She says, the trend

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<v Speaker 1>in coffee consumption is moving in one direction.

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<v Speaker 10>The demand is growing around the world, mainly in Asia

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<v Speaker 10>and also in what we call to the producing countries,

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<v Speaker 10>the producing countries that use it to drink a lot

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<v Speaker 10>of coffee. In the past, where only Ethiopia and Brazil,

0:14:24.560 --> 0:14:29.040
<v Speaker 10>now you have many other producing countries taking a lot

0:14:29.080 --> 0:14:35.520
<v Speaker 10>of coffee. And additionally to that we have now China, Philippines, Vietnam,

0:14:35.800 --> 0:14:40.680
<v Speaker 10>Indonesia taking a lot of coffee together with South Korea,

0:14:40.960 --> 0:14:42.920
<v Speaker 10>Japan and other countries in Asia.

0:14:43.920 --> 0:14:48.240
<v Speaker 1>Wock you All these habits being cultivated around the world

0:14:48.320 --> 0:14:51.480
<v Speaker 1>add up to big business. According to the analysis from

0:14:51.600 --> 0:14:55.280
<v Speaker 1>SMP Global. The annual revenue of the industry worldwide has

0:14:55.320 --> 0:14:58.160
<v Speaker 1>soared from around two hundred billion dollars in twenty nineteen

0:14:58.480 --> 0:15:01.280
<v Speaker 1>to know you four hundred and six billion dollars today.

0:15:01.920 --> 0:15:05.960
<v Speaker 1>But as coffee demand continues to rise, production has been inconsistent.

0:15:06.400 --> 0:15:10.080
<v Speaker 1>In the last two years. Consumption outstripped supply as bad

0:15:10.120 --> 0:15:13.440
<v Speaker 1>weather took a toll on coffee farms that helped send

0:15:13.520 --> 0:15:17.120
<v Speaker 1>prices of Arabica being futures surging more than eighty percent

0:15:17.400 --> 0:15:19.760
<v Speaker 1>in the span of a year, from one hundred and

0:15:19.800 --> 0:15:22.520
<v Speaker 1>forty six in September of twenty twenty three to a

0:15:22.600 --> 0:15:25.200
<v Speaker 1>high of almost two hundred and seventy four this year.

0:15:29.040 --> 0:15:32.920
<v Speaker 11>Coffee as an industry really starts in the late nineteenth century.

0:15:32.960 --> 0:15:35.000
<v Speaker 11>That's really when we see the start of trading, so

0:15:35.040 --> 0:15:39.120
<v Speaker 11>the New York Coffee Exchange opens in the eighteen eighties.

0:15:39.480 --> 0:15:42.440
<v Speaker 1>Jonathan Morris is an expert in the field, dubbed the

0:15:42.520 --> 0:15:46.640
<v Speaker 1>Coffee Historian. He's the author of Coffee, a Global History

0:15:46.840 --> 0:15:49.240
<v Speaker 1>and a professor at the University of Hertfordshire.

0:15:49.880 --> 0:15:53.920
<v Speaker 11>Now we have a much more expanded global market, so

0:15:54.000 --> 0:15:57.160
<v Speaker 11>coffee consumption has been rising. A lot of emulation in

0:15:57.200 --> 0:16:02.360
<v Speaker 11>that rise so essentially that's what we're seeing, and the

0:16:02.520 --> 0:16:05.520
<v Speaker 11>demand for coffee in that sense is ever growing.

0:16:06.080 --> 0:16:08.560
<v Speaker 1>A good part of their growth comes from the Starbucks

0:16:08.560 --> 0:16:11.200
<v Speaker 1>of this world, a phenomenon that may have taken off

0:16:11.200 --> 0:16:15.160
<v Speaker 1>in Seattle, Washington, but it is turned into an international trend.

0:16:16.480 --> 0:16:19.920
<v Speaker 11>So what's really changed coffee in the last sort of

0:16:19.960 --> 0:16:23.600
<v Speaker 11>thirty forty years is obviously the growth of the if

0:16:23.640 --> 0:16:28.000
<v Speaker 11>you like, the international coffee shop chains, that kind of format.

0:16:28.560 --> 0:16:32.080
<v Speaker 11>And I say it's changed it not because it's accounted

0:16:32.120 --> 0:16:35.640
<v Speaker 11>for a huge proportion of consumption, although it's certainly driven

0:16:35.720 --> 0:16:38.840
<v Speaker 11>that up, but rather because of the amount of emulation

0:16:38.920 --> 0:16:42.280
<v Speaker 11>that that's created across the market. So coffee as a

0:16:42.320 --> 0:16:45.240
<v Speaker 11>beverage has become much more popular across the globe.

0:16:45.320 --> 0:16:47.480
<v Speaker 1>It's not just that more of us are spending more

0:16:47.520 --> 0:16:50.880
<v Speaker 1>of our time in our favorite coffee shops. Increasingly, entire

0:16:50.960 --> 0:16:54.640
<v Speaker 1>countries are getting into the game, quickly developing their own

0:16:54.760 --> 0:16:58.359
<v Speaker 1>habits of turning to coffee instead of their traditional beverages.

0:16:58.720 --> 0:17:01.640
<v Speaker 11>So China as a market alone is now probably something

0:17:01.760 --> 0:17:05.000
<v Speaker 11>like in the seventh or sixth in the world in

0:17:05.119 --> 0:17:09.400
<v Speaker 11>terms of volume, and of course the growth across particularly

0:17:09.440 --> 0:17:12.600
<v Speaker 11>Southeast Asia as a whole has really driven the big

0:17:12.640 --> 0:17:14.679
<v Speaker 11>expansion of coffee in the last few years.

0:17:17.600 --> 0:17:21.040
<v Speaker 1>Steven Sutton's job is to meet that demand while strengthening

0:17:21.119 --> 0:17:25.040
<v Speaker 1>the supply chain his New York coffee shops specializing getting

0:17:25.080 --> 0:17:28.920
<v Speaker 1>beans to customers at breakneck speeds. It can take large

0:17:29.000 --> 0:17:31.439
<v Speaker 1>chains up to a year to get coffee from farm

0:17:31.560 --> 0:17:35.040
<v Speaker 1>to cup. Sutton's de Vosion sells beans that have been

0:17:35.080 --> 0:17:38.600
<v Speaker 1>harvested less than a month before hitting shelves. To do that,

0:17:38.800 --> 0:17:42.200
<v Speaker 1>he uses FedEx to overnight the beans straight from Bogata

0:17:42.240 --> 0:17:45.760
<v Speaker 1>to Brooklyn, where he roasts around ten pounds of them

0:17:45.800 --> 0:17:50.280
<v Speaker 1>every week. Within days, they're in cafes, executive dining rooms,

0:17:50.280 --> 0:17:53.000
<v Speaker 1>and high end restaurants like eleven Madison Park.

0:17:53.600 --> 0:17:57.720
<v Speaker 12>We actually opened doors in two thousand and six, and

0:17:57.800 --> 0:18:01.480
<v Speaker 12>the whole idea was to do some different locally in

0:18:01.560 --> 0:18:06.399
<v Speaker 12>Colombia for the farmers and finding absolutely the best coffees

0:18:06.960 --> 0:18:11.040
<v Speaker 12>that were lost at that time because of the internal

0:18:11.080 --> 0:18:15.160
<v Speaker 12>conflict of Colombia. So we decided very early on that

0:18:15.240 --> 0:18:18.240
<v Speaker 12>we were to concentrate our efforts of understanding how to

0:18:18.359 --> 0:18:23.080
<v Speaker 12>go to the farmer, how to say maneuver the territory

0:18:23.960 --> 0:18:27.960
<v Speaker 12>and its difficulties at that time so we can get

0:18:28.000 --> 0:18:31.320
<v Speaker 12>to the farmer pay them actually fair wages, because one

0:18:31.359 --> 0:18:35.040
<v Speaker 12>thing is fair trade and one thing is fair money wages.

0:18:35.359 --> 0:18:38.840
<v Speaker 1>Sun's direct relationship with farmers has been both good practice

0:18:38.880 --> 0:18:42.879
<v Speaker 1>and good business. Better sourcing can mean better coffee, and

0:18:42.920 --> 0:18:44.840
<v Speaker 1>that's been a staple of his success.

0:18:45.359 --> 0:18:49.720
<v Speaker 12>Actually getting the taste that they got at origin, and

0:18:49.840 --> 0:18:52.440
<v Speaker 12>that until now it's pretty much unheard of. So we

0:18:53.040 --> 0:18:57.639
<v Speaker 12>averaged let's call it around fifteen days old coffees before roasting.

0:18:57.960 --> 0:19:01.280
<v Speaker 1>So that sounds remarkable, but also sounds remark expensive. Potentially,

0:19:01.440 --> 0:19:05.600
<v Speaker 1>what's the difference in the price of the fresh coffee,

0:19:05.640 --> 0:19:08.760
<v Speaker 1>the freshly roasted and ground as opposed to things that

0:19:08.840 --> 0:19:09.879
<v Speaker 1>take six months.

0:19:10.080 --> 0:19:14.119
<v Speaker 12>So there's there is a difference, but it's not a

0:19:14.200 --> 0:19:16.360
<v Speaker 12>huge difference. I mean, you're probably going to be paying

0:19:16.400 --> 0:19:18.720
<v Speaker 12>a dollar more for our coffee. If you take out

0:19:18.800 --> 0:19:22.200
<v Speaker 12>a lot of the middle people and a lot of

0:19:22.240 --> 0:19:25.679
<v Speaker 12>the normal process of coffee, you'll end up finding that

0:19:25.840 --> 0:19:29.080
<v Speaker 12>number one, the money can actually go to the farmer.

0:19:29.520 --> 0:19:32.280
<v Speaker 1>Suton says that making sure everyone gets their fair share

0:19:32.320 --> 0:19:34.520
<v Speaker 1>of the pie is one key piece of the puzzle,

0:19:34.960 --> 0:19:38.159
<v Speaker 1>but another is protecting the supply chain. As the rising

0:19:38.240 --> 0:19:41.800
<v Speaker 1>impact of climate change takes a toll on growing conditions.

0:19:44.320 --> 0:19:51.080
<v Speaker 13>The coffee market has been suffering with supply trucks, especially

0:19:51.119 --> 0:19:56.199
<v Speaker 13>giving weather, because of weather problems we had in Brazil.

0:19:56.560 --> 0:19:59.680
<v Speaker 1>Carlos Costa is the chief commercial officer at Hedge Point

0:19:59.680 --> 0:20:04.200
<v Speaker 1>Globe Markets and its spent years analyzing soft commodities like coffee,

0:20:04.520 --> 0:20:07.240
<v Speaker 1>whose futures point to more confidence in the short term

0:20:07.280 --> 0:20:09.560
<v Speaker 1>trade than in the long term investment.

0:20:10.119 --> 0:20:13.359
<v Speaker 14>Cost of production all days in Brazil it's around seven

0:20:13.440 --> 0:20:16.720
<v Speaker 14>hundred has per bag, seven hundred and fifty for Arabica,

0:20:17.320 --> 0:20:21.159
<v Speaker 14>and the price is trading at fourteen hundred fifteen hundred,

0:20:21.680 --> 0:20:25.440
<v Speaker 14>so they are making a quite good amount of profits

0:20:25.520 --> 0:20:29.040
<v Speaker 14>if you compare cost of production and price of selling.

0:20:29.400 --> 0:20:32.280
<v Speaker 14>So farmers are making good money out of that in

0:20:32.320 --> 0:20:38.280
<v Speaker 14>the supply chain. If you see other participants, traders, the

0:20:38.320 --> 0:20:41.280
<v Speaker 14>margin of the trading is not you know, for commodity

0:20:41.320 --> 0:20:44.480
<v Speaker 14>trading is not that much. We're talking about three five

0:20:44.560 --> 0:20:48.120
<v Speaker 14>percent bottom line. But looking ahead and look into the future,

0:20:48.320 --> 0:20:52.600
<v Speaker 14>our sentiment, as is the volatility my continue to stay

0:20:52.640 --> 0:20:55.920
<v Speaker 14>on the high level because of the supply and straints

0:20:55.960 --> 0:20:59.440
<v Speaker 14>we have nowadays and also the lower level of inventories,

0:20:59.480 --> 0:21:02.199
<v Speaker 14>which bring more concerns in the market as well. But

0:21:02.720 --> 0:21:06.160
<v Speaker 14>the main history here is that the world has been

0:21:06.200 --> 0:21:10.840
<v Speaker 14>consuming more coffee year over year, and supplies has not

0:21:10.960 --> 0:21:15.159
<v Speaker 14>been following that growth in the men both because of

0:21:15.280 --> 0:21:21.000
<v Speaker 14>weather situations that affected supplies, but because you also haven't

0:21:21.080 --> 0:21:25.080
<v Speaker 14>saw so much growth in the majority of the origins

0:21:25.119 --> 0:21:26.439
<v Speaker 14>in terms of area.

0:21:26.480 --> 0:21:30.000
<v Speaker 1>Although production has rebounded this year, climate change means supply

0:21:30.160 --> 0:21:34.840
<v Speaker 1>volatility isn't going away and demand will only continue to rise.

0:21:35.480 --> 0:21:38.320
<v Speaker 1>For people like Steven Sutton, who see the whole process

0:21:38.359 --> 0:21:41.520
<v Speaker 1>from start to finish, meeting that demand in the long

0:21:41.600 --> 0:21:44.119
<v Speaker 1>term means investing in the next generation.

0:21:44.359 --> 0:21:47.359
<v Speaker 12>We are very active in doing things for the community

0:21:47.440 --> 0:21:51.800
<v Speaker 12>and the farmer to protect our stakeholders and where we're

0:21:51.800 --> 0:21:54.440
<v Speaker 12>getting our coffee from us we go to a local

0:21:54.440 --> 0:21:59.840
<v Speaker 12>school in Ghaccetta and we are teaching these kits about

0:22:00.480 --> 0:22:03.240
<v Speaker 12>how to do better coffee, how to get better economics.

0:22:03.359 --> 0:22:06.879
<v Speaker 12>We give them tools from a young age not only

0:22:06.960 --> 0:22:11.680
<v Speaker 12>to help them create a business, but also to help us,

0:22:12.000 --> 0:22:16.600
<v Speaker 12>as pretty much all the stakeholders inside of the coffee process,

0:22:16.760 --> 0:22:19.520
<v Speaker 12>to protect where the source is coming from. We like

0:22:19.600 --> 0:22:23.080
<v Speaker 12>to start building something that's more sustainable, and we think

0:22:23.240 --> 0:22:26.920
<v Speaker 12>that not necessarily is what should be done. I think

0:22:26.920 --> 0:22:29.840
<v Speaker 12>it's part of it, but I think it's also part

0:22:29.840 --> 0:22:33.400
<v Speaker 12>of how we farm and how we teach these farmers

0:22:33.440 --> 0:22:36.439
<v Speaker 12>how to protect their farm not only by having coffee

0:22:36.440 --> 0:22:39.440
<v Speaker 12>but other things and protecting their environment. If we can

0:22:39.520 --> 0:22:43.639
<v Speaker 12>do that in a large scale, then I think we

0:22:43.720 --> 0:22:47.000
<v Speaker 12>will have a good future in the coffee world. If

0:22:47.040 --> 0:22:51.639
<v Speaker 12>we can't, then I foreseeing twenty fifty coffee being a

0:22:51.720 --> 0:22:52.960
<v Speaker 12>complete luxury.

0:22:53.880 --> 0:22:57.280
<v Speaker 1>Up next, How artificial intelligence is changing the world of

0:22:57.440 --> 0:23:02.480
<v Speaker 1>art and entertainment, the view from AI and artists themselves.

0:23:03.080 --> 0:23:04.640
<v Speaker 1>Next on Wall Street Week.

0:23:13.840 --> 0:23:18.000
<v Speaker 8>This is Bloomberg Wall Street Week with David Weston from

0:23:18.200 --> 0:23:19.119
<v Speaker 8>Bloomberg Radio.

0:23:25.359 --> 0:23:29.240
<v Speaker 1>This is a story about creativity, something we normally consider

0:23:29.280 --> 0:23:33.080
<v Speaker 1>to be a quintessentially human trait, but something that machines

0:23:33.119 --> 0:23:39.679
<v Speaker 1>increasingly seem to be encroaching on. You really think you

0:23:39.680 --> 0:23:43.240
<v Speaker 1>can just walk away from all this? There's no other choice.

0:23:43.880 --> 0:23:45.200
<v Speaker 11>There's always a choice.

0:23:45.720 --> 0:23:47.160
<v Speaker 12>You just don't like the alternatives.

0:23:47.840 --> 0:23:48.680
<v Speaker 7>What would you have me do?

0:23:48.800 --> 0:23:52.560
<v Speaker 1>This video was made entirely by AI. It was generated

0:23:52.600 --> 0:23:56.879
<v Speaker 1>by Runway, which builds AI tools from multimedia content. Crista

0:23:56.920 --> 0:23:59.880
<v Speaker 1>Baal Bealezuela is the company's founder and CEE.

0:24:01.280 --> 0:24:05.200
<v Speaker 9>You can think of generated AI as a version of CGI.

0:24:05.280 --> 0:24:06.919
<v Speaker 9>It's a better version of the things we've done in

0:24:06.920 --> 0:24:09.760
<v Speaker 9>the past. Imagine a world where you have a system

0:24:09.880 --> 0:24:13.119
<v Speaker 9>that can create imagery on a real time basis. It

0:24:13.119 --> 0:24:15.040
<v Speaker 9>feels like a video game, feels like a movie, but

0:24:15.080 --> 0:24:17.040
<v Speaker 9>you're generating things that you've never seen.

0:24:16.840 --> 0:24:22.399
<v Speaker 1>Before all over the world. Filmmakers, it's frightening on so

0:24:22.400 --> 0:24:28.960
<v Speaker 1>many levels that so many people who will lose their jobs. Musicians,

0:24:29.240 --> 0:24:33.639
<v Speaker 1>the way they are learning and the company is developing,

0:24:33.840 --> 0:24:37.040
<v Speaker 1>it's going to be a threat visual artists.

0:24:37.720 --> 0:24:41.160
<v Speaker 15>Every single professional artist I know has been affected by this,

0:24:41.680 --> 0:24:43.080
<v Speaker 15>and not a good way.

0:24:43.960 --> 0:24:46.880
<v Speaker 1>All worry about what generative AI could mean for their

0:24:46.960 --> 0:24:49.720
<v Speaker 1>creative work, and it turns out that they have good

0:24:49.760 --> 0:24:53.400
<v Speaker 1>reason to worry. A report from January twenty twenty three

0:24:53.520 --> 0:24:56.479
<v Speaker 1>concludes that more than two hundred thousand people in the

0:24:56.600 --> 0:25:00.480
<v Speaker 1>entertainment industry could be adversely affected by AI in twenty

0:25:00.520 --> 0:25:05.040
<v Speaker 1>twenty six, nearly a third of the entire workforce. But

0:25:05.200 --> 0:25:08.359
<v Speaker 1>for those who can use AI, it's already becoming a

0:25:08.440 --> 0:25:10.160
<v Speaker 1>valuable tool in the industry.

0:25:10.640 --> 0:25:13.640
<v Speaker 9>Screenwriters are using Runway, there's a sort of BFx. Artist

0:25:13.680 --> 0:25:15.680
<v Speaker 9>are using Runway, and so every part of the production

0:25:15.840 --> 0:25:20.320
<v Speaker 9>cycle has avenues and spaces where you can augment and

0:25:20.400 --> 0:25:24.480
<v Speaker 9>incorporate it and improve the process using AI.

0:25:25.480 --> 0:25:29.320
<v Speaker 1>Runway is growing fast, training its models to animate and

0:25:29.440 --> 0:25:33.600
<v Speaker 1>alter already existing images in videos, and its adoption in

0:25:33.640 --> 0:25:37.200
<v Speaker 1>the industry is little wonder given what generative AI can

0:25:37.280 --> 0:25:39.080
<v Speaker 1>mean for a company's bottom line.

0:25:39.280 --> 0:25:41.719
<v Speaker 9>So this is your studio, Yes, this is our studio,

0:25:41.760 --> 0:25:43.560
<v Speaker 9>and so today I'm going to show you a with

0:25:43.800 --> 0:25:46.959
<v Speaker 9>creating video that involves no prompts, there's no text, and

0:25:47.000 --> 0:25:48.960
<v Speaker 9>the idea there is that you can take the emotional,

0:25:49.000 --> 0:25:51.840
<v Speaker 9>the expression, the performance of the actor and apply to

0:25:52.119 --> 0:25:54.800
<v Speaker 9>your animated character or live action kind of like element.

0:25:55.200 --> 0:25:58.800
<v Speaker 9>So to me, when I read a few lines, go

0:25:58.920 --> 0:25:59.159
<v Speaker 9>for it.

0:26:00.640 --> 0:26:04.720
<v Speaker 7>No and you're scared.

0:26:06.880 --> 0:26:10.800
<v Speaker 11>Okay, discussed it so discussed like smells really bad.

0:26:12.640 --> 0:26:14.400
<v Speaker 9>So now now what we're going to do is we're

0:26:14.440 --> 0:26:16.439
<v Speaker 9>going to take that performance that Jamie just did and

0:26:16.480 --> 0:26:18.760
<v Speaker 9>we're going to map the un scanned kind of the

0:26:18.840 --> 0:26:22.720
<v Speaker 9>face to translate that set of expressions and emotions you

0:26:22.840 --> 0:26:25.720
<v Speaker 9>just perform for us into an animated character. And that

0:26:25.760 --> 0:26:29.240
<v Speaker 9>happens almost in a roting basis. It's a couple thousand

0:26:29.280 --> 0:26:31.720
<v Speaker 9>dollars for every second of a triple a movie or

0:26:31.760 --> 0:26:33.480
<v Speaker 9>like a good movie. Right, there's a lot of money

0:26:33.480 --> 0:26:36.840
<v Speaker 9>being put into just making a few seconds. The approach

0:26:36.960 --> 0:26:39.720
<v Speaker 9>it takes is like, you can have systems that can

0:26:39.800 --> 0:26:42.480
<v Speaker 9>aid you in creating all of those things in much

0:26:42.560 --> 0:26:45.960
<v Speaker 9>shorter times for a fraction of the cost. And so

0:26:46.080 --> 0:26:49.800
<v Speaker 9>if rendering might take you traditional methods a couple thousand dollars,

0:26:50.119 --> 0:26:52.399
<v Speaker 9>rendering for us is just like less than a dollar.

0:26:53.440 --> 0:26:56.840
<v Speaker 1>To take advantage of that kind of cost savings, Entertainment

0:26:56.840 --> 0:26:59.680
<v Speaker 1>companies are planning to ramp up their use of generative

0:26:59.680 --> 0:27:03.600
<v Speaker 1>AI a lot. A recent survey showed companies plan to

0:27:03.680 --> 0:27:07.040
<v Speaker 1>use AI extensively in the next three years on everything

0:27:07.040 --> 0:27:11.000
<v Speaker 1>from sound design and cinematography to voice acting and writing,

0:27:11.480 --> 0:27:14.040
<v Speaker 1>which may be good for the companies, but isn't so

0:27:14.160 --> 0:27:15.439
<v Speaker 1>good for the creators.

0:27:16.600 --> 0:27:18.600
<v Speaker 15>You know what sucks is like I like these colors,

0:27:18.640 --> 0:27:22.160
<v Speaker 15>and I like the idea, but I would never paint

0:27:22.200 --> 0:27:25.639
<v Speaker 15>it like that. It's so boring. She has absolutely no

0:27:25.720 --> 0:27:26.760
<v Speaker 15>emotion in her face.

0:27:28.080 --> 0:27:31.680
<v Speaker 1>Kelly mcckernin is a classically trained artist in Nashville who

0:27:31.680 --> 0:27:34.879
<v Speaker 1>built a following with a distinctive style of illustration and

0:27:34.960 --> 0:27:39.600
<v Speaker 1>watercolor portraits. But two years ago Kelly started finding images

0:27:39.640 --> 0:27:42.159
<v Speaker 1>on the Internet that looked eerily familiar.

0:27:43.119 --> 0:27:47.000
<v Speaker 15>Summer twenty twenty two was when I first saw some

0:27:47.200 --> 0:27:52.240
<v Speaker 15>images coming out of Dolly and mid Journey and a

0:27:52.280 --> 0:27:56.880
<v Speaker 15>couple of other AI generators. It was very confusing because

0:27:57.119 --> 0:27:59.960
<v Speaker 15>I wanted to know how they did that. At this point,

0:28:00.640 --> 0:28:04.199
<v Speaker 15>there are more images online using my name as a

0:28:04.280 --> 0:28:09.040
<v Speaker 15>prompt than there are actual artworks I've made. So because

0:28:09.040 --> 0:28:13.560
<v Speaker 15>of this, between twenty twenty two and twenty twenty three,

0:28:13.680 --> 0:28:16.880
<v Speaker 15>I saw my income drop by thirty percent. So after

0:28:16.960 --> 0:28:20.280
<v Speaker 15>ten years as a professional artist, I'm no longer able

0:28:20.320 --> 0:28:22.240
<v Speaker 15>to make a full time living off of my work.

0:28:23.560 --> 0:28:26.680
<v Speaker 15>For twenty years, I've had artwork online, and so all

0:28:26.680 --> 0:28:31.000
<v Speaker 15>that art is hoovered up by these companies who believe

0:28:31.200 --> 0:28:35.520
<v Speaker 15>because it's publicly available it's fair use. They should have

0:28:36.080 --> 0:28:40.000
<v Speaker 15>properly licensed these images. We should have been compensated and

0:28:40.040 --> 0:28:43.080
<v Speaker 15>at the very least given our consent, but that didn't happen.

0:28:44.640 --> 0:28:48.200
<v Speaker 1>McKernan has joined with other artists to sue several generitive

0:28:48.240 --> 0:28:53.160
<v Speaker 1>AI firms, including Runway, for claims of copyright violation stemming

0:28:53.160 --> 0:28:56.200
<v Speaker 1>from the company's use of images of their work taken

0:28:56.200 --> 0:29:00.840
<v Speaker 1>from the Internet and used to train AI models entertainment.

0:29:00.880 --> 0:29:04.760
<v Speaker 1>IP lawyer Jennifer Wallace believes McKernan and other creators have

0:29:04.880 --> 0:29:05.800
<v Speaker 1>a strong case.

0:29:06.920 --> 0:29:10.680
<v Speaker 16>If the AI companies want to enter into licenses with

0:29:11.160 --> 0:29:14.720
<v Speaker 16>the companies that own the copyrighted material, they absolutely can

0:29:14.760 --> 0:29:17.800
<v Speaker 16>do that. But that's not what they're doing. And it's

0:29:18.040 --> 0:29:19.920
<v Speaker 16>kind of the model of the tech industry where I'd

0:29:19.920 --> 0:29:21.680
<v Speaker 16>ask for forgiveness, not permission.

0:29:23.520 --> 0:29:27.480
<v Speaker 15>What I was afraid of happening did happen where instead

0:29:27.520 --> 0:29:30.840
<v Speaker 15>of a company reaching out to me, or perhaps a

0:29:31.480 --> 0:29:36.400
<v Speaker 15>small publisher reaching out and wanting to commission me for

0:29:36.640 --> 0:29:40.640
<v Speaker 15>book cover, for example, they could instead save a lot

0:29:40.680 --> 0:29:43.360
<v Speaker 15>of money by just going into one of these programs

0:29:43.600 --> 0:29:46.720
<v Speaker 15>and typing in the image they want and then saying

0:29:46.840 --> 0:29:50.000
<v Speaker 15>in the style of Kelly mcckernan instead of hiring me,

0:29:50.560 --> 0:29:53.440
<v Speaker 15>And this is exactly what started to happen. There were

0:29:54.000 --> 0:29:58.000
<v Speaker 15>well over ten thousand images through the Journey alone by

0:29:58.040 --> 0:30:01.280
<v Speaker 15>the end of twenty twenty two that I could publicly

0:30:01.360 --> 0:30:04.840
<v Speaker 15>search where my name was used to create images.

0:30:08.280 --> 0:30:13.440
<v Speaker 16>So there's three ways that the industry is going about it. Right.

0:30:13.520 --> 0:30:17.960
<v Speaker 16>There's federal legislation that creates a new IP right for

0:30:18.480 --> 0:30:24.560
<v Speaker 16>artificially generated name, image, and likeness. There's licenses. So sag

0:30:24.680 --> 0:30:28.720
<v Speaker 16>Aftra just entered into an agreement with an AI company

0:30:28.720 --> 0:30:32.320
<v Speaker 16>that generates very realistic name, image, and likeness and voice

0:30:32.400 --> 0:30:35.560
<v Speaker 16>replicas of its actors, and it entered into a contract

0:30:35.600 --> 0:30:38.400
<v Speaker 16>that allows the actors to decide if they want to

0:30:38.440 --> 0:30:41.640
<v Speaker 16>allow this company to be able to replicate their voice

0:30:41.800 --> 0:30:46.440
<v Speaker 16>and to set their own prices for it, and then

0:30:46.800 --> 0:30:50.760
<v Speaker 16>you have the litigation that's going through the court systems.

0:30:51.440 --> 0:30:54.440
<v Speaker 1>Valezuela contends that the copyright claim is based on a

0:30:54.480 --> 0:30:58.480
<v Speaker 1>fundamental confusion about how his generative AI models work, a

0:30:58.520 --> 0:31:02.320
<v Speaker 1>confusion indicated by the very word copy right, because he

0:31:02.360 --> 0:31:05.920
<v Speaker 1>says generative AI is not copying, it's learning.

0:31:06.600 --> 0:31:09.000
<v Speaker 9>It's good to remember that these models don't copy data.

0:31:09.040 --> 0:31:11.440
<v Speaker 9>They're not trying to replicate existing data. Like the goal

0:31:11.880 --> 0:31:13.680
<v Speaker 9>for a good model is not to create a scene

0:31:13.680 --> 0:31:16.560
<v Speaker 9>that already aciss there's no point in it. There's no database,

0:31:16.600 --> 0:31:19.720
<v Speaker 9>so you're not pulling from existing scenes. What you're doing

0:31:19.880 --> 0:31:22.520
<v Speaker 9>is you can think about it as a as a student.

0:31:23.000 --> 0:31:26.360
<v Speaker 9>You're showing that student many many different films, and you're

0:31:26.440 --> 0:31:29.120
<v Speaker 9>learning the principles of how a camera moves, what different

0:31:29.160 --> 0:31:31.640
<v Speaker 9>lenses You can use what a performer looks like, and

0:31:31.680 --> 0:31:35.080
<v Speaker 9>then you're asking that model to create new versions of

0:31:35.120 --> 0:31:37.480
<v Speaker 9>what you want. But the things that are creating have

0:31:37.600 --> 0:31:41.040
<v Speaker 9>never been seen before, have never been generated before.

0:31:41.760 --> 0:31:46.640
<v Speaker 7>I'm very skeptical. I could put the history of William

0:31:46.720 --> 0:31:51.160
<v Speaker 7>Randolph Hirst into a large learning model and I wouldn't

0:31:51.320 --> 0:31:53.080
<v Speaker 7>get Citizen.

0:31:52.760 --> 0:31:58.840
<v Speaker 1>Kain, whatever the legal issues involved. Jennathan Taplan says generative

0:31:58.880 --> 0:32:02.120
<v Speaker 1>AI poses a real threat not to the top of

0:32:02.160 --> 0:32:04.760
<v Speaker 1>the creative food chain, but to the vast majority of

0:32:04.800 --> 0:32:07.560
<v Speaker 1>creative artists and writers who make up the core of

0:32:07.560 --> 0:32:11.120
<v Speaker 1>the entertainment industry, people of whom we may never have heard.

0:32:11.880 --> 0:32:15.120
<v Speaker 7>It's not necessarily a real threat to Bob Dylan, but

0:32:15.200 --> 0:32:18.600
<v Speaker 7>it's a real threat to the what I would call

0:32:18.640 --> 0:32:24.200
<v Speaker 7>the working musician who may get one thousand dollars a

0:32:24.320 --> 0:32:27.600
<v Speaker 7>month or a quarter from Spotify.

0:32:28.840 --> 0:32:32.400
<v Speaker 1>In the end, the issues surrounding generative AI and its

0:32:32.400 --> 0:32:35.600
<v Speaker 1>effect on the entertainment industry likely do not come down

0:32:35.640 --> 0:32:39.760
<v Speaker 1>to the law or even the financial consequences for individual artists.

0:32:40.440 --> 0:32:44.640
<v Speaker 1>It's all about creativity and whether generative AI holds out

0:32:44.640 --> 0:32:48.640
<v Speaker 1>the potential for increasing human creativity or drowning it under

0:32:48.640 --> 0:32:51.200
<v Speaker 1>a seat of duplicative content we don't need.

0:32:51.840 --> 0:32:55.320
<v Speaker 9>We've always seen AI as a baseline for a new

0:32:55.480 --> 0:32:58.280
<v Speaker 9>media format, a new way of storytelling, a new set

0:32:58.320 --> 0:33:00.880
<v Speaker 9>of creative an artist. The gold is not technology. We

0:33:00.920 --> 0:33:03.320
<v Speaker 9>don't build models for the sake of building models. But

0:33:03.360 --> 0:33:06.160
<v Speaker 9>it's important to understand that technology serves a human need.

0:33:06.360 --> 0:33:09.120
<v Speaker 9>And if you don't emphasize the human need, then there's

0:33:09.160 --> 0:33:12.080
<v Speaker 9>no goal. And so we've always on emphasize that aspect

0:33:12.120 --> 0:33:14.960
<v Speaker 9>of FAID, and our aspect is to create great storytelling

0:33:14.960 --> 0:33:16.080
<v Speaker 9>tools more than anything else.

0:33:17.840 --> 0:33:21.920
<v Speaker 1>There's little doubt that AI will make storytelling faster, cheaper,

0:33:22.320 --> 0:33:25.640
<v Speaker 1>less labor intensive. But that means it will also cut

0:33:25.680 --> 0:33:28.400
<v Speaker 1>out the need for people to make thousands of small

0:33:28.440 --> 0:33:31.320
<v Speaker 1>decisions like the color of the sky in a movie,

0:33:31.760 --> 0:33:34.800
<v Speaker 1>or the balance between woodwinds and strings and a symphony,

0:33:35.280 --> 0:33:39.280
<v Speaker 1>creative judgments made in nanoseconds by something that's learned from us,

0:33:39.600 --> 0:33:42.680
<v Speaker 1>but that is not us. Will it be as good

0:33:42.840 --> 0:33:46.080
<v Speaker 1>or better than what we humans would have done? Perhaps

0:33:46.200 --> 0:33:57.400
<v Speaker 1>on certainly the AI models would think. So that does

0:33:57.440 --> 0:33:59.479
<v Speaker 1>it for this edition of Bloomberg Wall Street Week. If

0:33:59.480 --> 0:34:02.080
<v Speaker 1>you missed any part of today's program, you can listen

0:34:02.160 --> 0:34:05.200
<v Speaker 1>on demand with our Wall Street Week podcast. Find that

0:34:05.320 --> 0:34:08.759
<v Speaker 1>on Apple, Spotify, or anywhere else you get your podcasts.

0:34:09.040 --> 0:34:12.040
<v Speaker 1>I'm David Weston. Stay with us. Today's top stories and

0:34:12.200 --> 0:34:14.960
<v Speaker 1>global business headlines are coming up right now.