WEBVTT - AI and Price Fixing 0:00:04.480 --> 0:00:12.440 Welcome to Tech Stuff, a production from iHeartRadio. Hey thereon 0:00:12.600 --> 0:00:16.080 Welcome to Tech Stuff. I'm your host, job than Strickland. 0:00:16.079 --> 0:00:19.279 I'm an executive producer with iHeart Podcasts. And how the 0:00:19.320 --> 0:00:24.079 tech are you? So imagine that you're a kid, and 0:00:24.560 --> 0:00:27.560 it's a bright summer morning, and you got the whole 0:00:27.720 --> 0:00:30.320 day ahead of you. So you decide you want to 0:00:30.360 --> 0:00:33.040 earn a little scratch. You're going to engage in the old, 0:00:33.440 --> 0:00:36.960 tried and true hustle of elimonade stand. So you got 0:00:37.000 --> 0:00:39.600 your lemons, you got your sugar, you got your water, 0:00:40.000 --> 0:00:42.400 you got your ice, you got your paper cups, your 0:00:42.479 --> 0:00:45.880 sunk around. I don't know ten bucks in your ingredients 0:00:45.960 --> 0:00:49.480 and supplies all told, and your goal is to make 0:00:49.600 --> 0:00:53.600 more money than you spent putting it all together. Well, 0:00:53.680 --> 0:00:56.680 you got some decisions you need to make. Where are 0:00:56.720 --> 0:00:59.080 you going to locate your lemonade stand? How are you 0:00:59.120 --> 0:01:02.080 going to get the attention of people going by? And 0:01:02.200 --> 0:01:06.360 perhaps most importantly, how much are you going to charge? Now? 0:01:06.400 --> 0:01:08.800 If you charge too much, no one's going to buy 0:01:08.840 --> 0:01:11.319 a cup of your lemonade. If you charge too little, 0:01:11.520 --> 0:01:14.520 you'll end up making less than what you spent on materials. 0:01:14.560 --> 0:01:17.520 Even if you sell out. So finding the balance between 0:01:17.560 --> 0:01:19.880 what the market will bear and what you need in 0:01:19.959 --> 0:01:23.000 order to cover your costs is one of those fundamental 0:01:23.080 --> 0:01:27.959 journeys every business takes, whether it's products or services. Now, 0:01:28.280 --> 0:01:32.920 an agile business operator might adjust things on the fly. 0:01:33.480 --> 0:01:38.600 Maybe there are some external factors that are playing an effect. Right, 0:01:38.680 --> 0:01:41.839 Maybe the day is extra hot, you know, maybe there's 0:01:41.840 --> 0:01:45.199 a block party or a festival nearby, so there's more 0:01:45.240 --> 0:01:47.240 foot traffic than normal. That would be a good place 0:01:47.280 --> 0:01:49.680 to put your lemonade stand. These are the things that 0:01:49.720 --> 0:01:53.120 are going to affect demand, and they could mean that 0:01:53.200 --> 0:01:56.000 you could price your eliminade a little higher than you 0:01:56.040 --> 0:02:00.400 would if it were on a quiet and overcast day. Heck, 0:02:00.640 --> 0:02:04.200 maybe you realize early on that you price things too high, 0:02:04.440 --> 0:02:06.760 so you quietly lower the cost of a couple of 0:02:06.840 --> 0:02:09.600 lemonade and sales pick up. As a result, you have 0:02:09.720 --> 0:02:13.280 engaged in what is called dynamic pricing. Now, from a 0:02:13.360 --> 0:02:16.560 high level, this sort of pricing happens all the time, 0:02:16.760 --> 0:02:19.600 though it's not always shifting moment to moment where we 0:02:19.639 --> 0:02:22.880 can see it in action. So you might find that 0:02:22.960 --> 0:02:26.160 a product in the store costs a little more or 0:02:26.160 --> 0:02:29.080 a little less since the last time you went shopping 0:02:29.120 --> 0:02:32.160 at that store. But in your typical store, you're not 0:02:32.320 --> 0:02:35.120 likely to see the price tag change right before your eyes. 0:02:35.480 --> 0:02:39.360 But that is changing, and it's largely thanks to AI. 0:02:39.960 --> 0:02:44.600 AI powered dynamic pricing is intended to maximize profits for 0:02:44.720 --> 0:02:47.880 business owners. That's really all there is to it. When 0:02:47.919 --> 0:02:51.080 you get down to the bottom line. It's meant to 0:02:51.240 --> 0:02:54.560 make people more money. And by people I mean business 0:02:54.560 --> 0:02:57.680 owners customers. It's meant to take more of their money. 0:02:57.840 --> 0:03:03.200 So maximization can get pretty crazy. An aipower dynamic pricing 0:03:03.240 --> 0:03:06.640 tool could in theory adjust prices on products not just 0:03:06.720 --> 0:03:10.359 based on local demand, but also as a result of 0:03:10.720 --> 0:03:13.880 how the perception of the product changes. Here, I'm going 0:03:13.919 --> 0:03:18.280 to give you a totally hypothetical example. Susie the TikTok 0:03:18.360 --> 0:03:22.880 star is brainstorming ideas for content and she decides to 0:03:22.919 --> 0:03:26.800 create a comedic sassy sketch and it centers around a 0:03:26.800 --> 0:03:30.160 particular brand of butter. So she shoots her video, she 0:03:30.360 --> 0:03:34.000 edits it, she uploads it, and the engagement comes pouring in. 0:03:34.320 --> 0:03:38.360 Now among the countless teeny bopper fans watching her content, 0:03:38.480 --> 0:03:42.200 there are also AI bots. One of those identifies the 0:03:42.240 --> 0:03:44.600 brand of butter in the video and takes note of 0:03:44.640 --> 0:03:47.560 the engagement that the video is getting. It's saying, huh, 0:03:47.760 --> 0:03:50.040 lots of people are watching this video. And it does 0:03:50.080 --> 0:03:53.400 some quick checking on available stats to figure out how 0:03:53.600 --> 0:03:58.040 popular Susie the TikTok Star is in say a specific 0:03:58.160 --> 0:04:01.280 local region. It figures out there's actually a huge fan 0:04:01.360 --> 0:04:04.680 base in this area, and it concludes that at least 0:04:04.720 --> 0:04:06.840 some of those fans are likely to rush out and 0:04:06.920 --> 0:04:10.520 try to recreate Susie the TikTok Stars video and to 0:04:10.560 --> 0:04:13.080 do that, they're going to want the genuine article. They're 0:04:13.120 --> 0:04:16.279 going to want the same brand of butter that Susie used. 0:04:16.560 --> 0:04:20.480 And so this AI, which happens to be a price 0:04:20.640 --> 0:04:25.159 strategy AI for a local grocery store chain, makes the 0:04:25.200 --> 0:04:28.160 call to bump the price on that particular brand of 0:04:28.200 --> 0:04:31.279 butter because there's about to be a surge in demand. 0:04:31.480 --> 0:04:34.640 The AI is not waiting to detect the demand is 0:04:34.680 --> 0:04:38.480 surging and then respond to it. It's actually being proactive. 0:04:38.640 --> 0:04:42.719 It is predicting the increase in demand and acting on 0:04:42.800 --> 0:04:47.000 it now instead of later. Now, we've seen dynamic surge 0:04:47.000 --> 0:04:52.920 pricing for years already, though not necessarily in the grocery market. 0:04:53.600 --> 0:04:57.000 Ride haling companies have been making use of surge pricing 0:04:57.040 --> 0:05:02.080 to respond to demand for years since they launched. You've 0:05:02.200 --> 0:05:06.280 probably been in a situation in which the cost of hailing, 0:05:06.440 --> 0:05:09.119 you know, an uber or lyft or whatever is way 0:05:09.240 --> 0:05:12.320 higher than it normally would be for that part of 0:05:12.360 --> 0:05:15.000 town in that time of day. This often happens when 0:05:15.040 --> 0:05:18.000 there's a big event going on, like a huge music 0:05:18.080 --> 0:05:22.080 concert or sporting event or conference or whatever, and during 0:05:22.120 --> 0:05:25.040 that window of time you may be paying twice as 0:05:25.120 --> 0:05:28.400 much or more for a ride as you would any 0:05:28.440 --> 0:05:31.400 other day. Now, there are a couple of reasons for this. 0:05:31.839 --> 0:05:36.000 One is that demand is exceeding supply. There are more 0:05:36.040 --> 0:05:38.320 people who are looking for a ride than you have 0:05:38.440 --> 0:05:42.080 drivers available to give people a ride, So raising prices 0:05:42.400 --> 0:05:45.520 can discourage folks who don't need a ride as badly 0:05:45.800 --> 0:05:48.560 as the ones who do. So the folks who really 0:05:48.600 --> 0:05:51.360 need a ride, presuming they can still afford to pay 0:05:51.400 --> 0:05:54.240 for it, will stick it out, and yeah, they'll have 0:05:54.279 --> 0:05:56.480 to pay more than usual, but the point is they'll 0:05:56.520 --> 0:05:58.320 get a car and they'll be able to get to 0:05:58.360 --> 0:06:03.680 where they need to go. The folks who say WHOA, Okay, well, y'all, 0:06:03.760 --> 0:06:06.159 maybe we shouldn't go to shake Shack. Maybe we should 0:06:06.160 --> 0:06:09.000 just find a place to eat around here. They'll back 0:06:09.080 --> 0:06:13.120 off and that helps improve availability. It decreases demand and 0:06:13.200 --> 0:06:15.880 allows the supply to get to where it's needed. But 0:06:15.960 --> 0:06:19.920 another reason is that surge pricing alerts drivers to areas 0:06:19.960 --> 0:06:23.000 of high demand. So cities can be pretty darn big. 0:06:23.040 --> 0:06:25.359 If you don't live in a city, you might not 0:06:25.440 --> 0:06:28.159 be used to it. But if there's a large demand 0:06:28.160 --> 0:06:31.600 that arises in one particular part of the city, drivers 0:06:31.640 --> 0:06:34.320 will see it on their apps and they can actually 0:06:34.440 --> 0:06:37.480 choose to reroute toward that part of town in order 0:06:37.520 --> 0:06:40.000 to take advantage of the surge. So that means that 0:06:40.120 --> 0:06:43.800 you get more drivers coming into the area, and that 0:06:43.880 --> 0:06:48.640 means the supply for drivers increases. This in turn affects 0:06:48.640 --> 0:06:52.880 demands and eventually the surge subsides. So this is one 0:06:52.920 --> 0:06:56.600 way to dynamically respond to demand spikes. It's giving an 0:06:56.640 --> 0:06:59.920 incentive to drivers to say, yes, there are probably people 0:07:00.400 --> 0:07:03.240 around my area right now who are looking for a ride, 0:07:03.480 --> 0:07:06.679 but way more people are looking for a ride across town, 0:07:07.240 --> 0:07:10.520 and I will benefit from the fact that surge pricing 0:07:10.680 --> 0:07:12.480 is in effect, So if I can get there in 0:07:12.600 --> 0:07:15.640 time to pick up a ride, I can end up 0:07:15.640 --> 0:07:19.240 getting more money for that ride, so it actually makes 0:07:19.280 --> 0:07:21.360 sense for me to leave the area I'm in right 0:07:21.400 --> 0:07:24.360 now and head across town now. Of course, the other 0:07:24.440 --> 0:07:29.400 reason that surge pricing exists is that it creates opportunities 0:07:29.400 --> 0:07:32.840 for the company to make that cheddar right because the 0:07:32.840 --> 0:07:36.520 companies that are employing oh wait, I'm sorry, I'm sorry, 0:07:36.680 --> 0:07:41.040 the companies that are contracting with the drivers, they certainly 0:07:41.120 --> 0:07:43.920 want their cut of the pie as well. So surge 0:07:43.920 --> 0:07:47.840 pricing means making more money from the same trips as 0:07:47.880 --> 0:07:51.600 you would any other day. Though this doesn't last forever 0:07:51.680 --> 0:07:55.720 because once supply and demand will reach parity, then surge 0:07:55.720 --> 0:07:59.440 pricing will decrease. Otherwise you run the risk of alienating 0:07:59.440 --> 0:08:03.800 your customers. So surge pricing is something that can address 0:08:03.920 --> 0:08:07.480 a sudden increase in demand. And I don't think it's 0:08:07.520 --> 0:08:10.640 a perfect solution, but at the same time like it. 0:08:10.640 --> 0:08:13.760 It is a way to solve that problem. It kind 0:08:13.760 --> 0:08:17.679 of stinks if you're one of the people who really 0:08:17.720 --> 0:08:19.680 needs a ride. That means that you're going to be 0:08:19.720 --> 0:08:23.840 paying extra. If you're able to wait, then eventually that 0:08:24.000 --> 0:08:28.600 serge is going to drive enough drivers there to bring 0:08:28.680 --> 0:08:30.840 the pricing down. But then on the flip side, if 0:08:30.840 --> 0:08:34.559 you're a driver and you get to an area just 0:08:34.600 --> 0:08:37.120 as surge pricing is on the decline, kind of stinks 0:08:37.160 --> 0:08:39.520 for you because you missed out on an opportunity. So like, 0:08:39.600 --> 0:08:42.400 it's not a perfect system by any stretch of the imagination. 0:08:42.480 --> 0:08:44.640 But let me do you one better. What if the 0:08:44.679 --> 0:08:49.600 AI knew how much money the customers were making, and 0:08:49.679 --> 0:08:53.240 therefore the AI could adjust the price on products and 0:08:53.360 --> 0:08:56.080 services in order to kind of give it that little 0:08:56.200 --> 0:08:59.880 personal touch. And by personal touch, I mean adjust ca 0:09:00.640 --> 0:09:03.800 so that they're as much, but not more than the 0:09:03.840 --> 0:09:06.800 customer would be willing to pay, perhaps more than what 0:09:06.960 --> 0:09:10.600 the person ahead of that customer or behind that customer 0:09:10.640 --> 0:09:14.240 is paying, but just the right amount to get the 0:09:14.280 --> 0:09:18.960 maximum money from that customer. Does that seem fair? Imagine 0:09:19.000 --> 0:09:21.360 walking into a grocery store and finding out that the 0:09:21.400 --> 0:09:23.760 carton of oat milk you've picked up it's actually going 0:09:23.840 --> 0:09:26.600 to cost you forty cents more than it costs the 0:09:26.640 --> 0:09:29.360 person who is behind you. That probably wouldn't feel really 0:09:29.400 --> 0:09:32.439 good that. Keep in mind, this dynamic pricing is meant 0:09:32.480 --> 0:09:36.880 to cater to income levels, so being charged less means 0:09:36.880 --> 0:09:40.000 that you would be in a lower earning household, at 0:09:40.080 --> 0:09:43.040 least assuming that the system is working as intended. So 0:09:43.080 --> 0:09:45.040 in some ways you can look at this as being 0:09:45.280 --> 0:09:48.640 kind right, bringing the cost of products to within the 0:09:48.679 --> 0:09:53.000 budget of lower earning customers. But that's not really what 0:09:53.040 --> 0:09:55.920 we're talking about here. We're talking about finding the price 0:09:56.360 --> 0:10:00.440 that you can set as your maximum before going to 0:10:00.520 --> 0:10:02.920 a point where people will just not buy anything at all. 0:10:03.280 --> 0:10:06.280 You want to find that spot where someone is going 0:10:06.320 --> 0:10:09.360 to hand over money. It might hurt, but they're gonna 0:10:09.360 --> 0:10:12.480 do it because they need the thing, And it's essentially 0:10:12.520 --> 0:10:15.199 price gouging, right. And when you flip it over, when 0:10:15.240 --> 0:10:18.640 you say this is charging more for the same stuff 0:10:18.679 --> 0:10:21.320 because the person who's doing the shopping is a big earner, 0:10:21.720 --> 0:10:24.600 that feels wrong too. And yet this is something that 0:10:24.760 --> 0:10:28.640 dynamic pricing could end up doing. In fact, it's more 0:10:28.679 --> 0:10:32.080 than could end up doing. I'll explain more, but first 0:10:32.160 --> 0:10:44.120 let's take a quick break. So before the break, I 0:10:44.200 --> 0:10:48.080 was talking about this sort of situation in which a 0:10:48.280 --> 0:10:54.120 store could use dynamic pricing to potentially personalize the shopping 0:10:54.200 --> 0:10:58.480 experience so that certain individuals would end up having to 0:10:58.480 --> 0:11:02.840 pay more for specific items versus others. Like you could 0:11:02.880 --> 0:11:05.559 have all the people in the store at the same time, 0:11:05.679 --> 0:11:08.880 and they'd each have an individual experience of how much 0:11:09.480 --> 0:11:12.840 each particular item would cost. Here in the United States, 0:11:12.920 --> 0:11:16.439 the grocery store chain Kroger is facing some scrutiny from 0:11:16.520 --> 0:11:20.600 legislators like Senators Elizabeth Warren and Bob Casey over the 0:11:20.640 --> 0:11:25.760 implementation of dynamic pricing that is at least in part 0:11:26.120 --> 0:11:29.760 driven by AI. So the chain makes use of digital 0:11:29.800 --> 0:11:33.199 price labels, and around eighteen percent of its stores I 0:11:33.240 --> 0:11:37.040 think it's like five hundred locations across the United States. 0:11:37.080 --> 0:11:40.400 They own and operate like twenty seven hundred and fifty, 0:11:40.800 --> 0:11:43.840 so five hundred is around a little less than twenty 0:11:43.840 --> 0:11:47.440 percent of their stores. But they have these digital price tags, 0:11:47.840 --> 0:11:51.320 and of course with digital price tags, you could change 0:11:51.320 --> 0:11:54.719 the price of things instantly. When the store makes use 0:11:54.760 --> 0:11:58.360 of proper labels like paper labels, I guess I shouldn't 0:11:58.360 --> 0:12:02.840 say proper. That's being really biased. Paper labels, well, there's 0:12:02.880 --> 0:12:05.680 not really the opportunity to switch things up so quickly. 0:12:05.720 --> 0:12:09.840 You're not going to have a shelf stocker just walking 0:12:10.280 --> 0:12:13.320 directly ahead of a shopper, paying attention to what the 0:12:13.320 --> 0:12:15.920 shoppers looking at and then slapping a new label on 0:12:16.520 --> 0:12:19.000 in order to change the price. That's not going to happen. 0:12:19.240 --> 0:12:22.680 But in these Kroger stores with the digital labels, it's 0:12:22.840 --> 0:12:26.120 entirely possible to change the prices for specific items at 0:12:26.160 --> 0:12:29.679 a moment's notice. Now, let's just say that you have 0:12:29.760 --> 0:12:33.040 these digital labels. You pair this with cameras that have 0:12:33.160 --> 0:12:38.199 things like facial recognition technology, and you gather information about customers. 0:12:38.240 --> 0:12:40.640 Maybe you know a lot about this customer already because 0:12:40.640 --> 0:12:43.840 they're in your loyalty program. Maybe they're using your app, 0:12:44.160 --> 0:12:49.280 and the app is communicating in real time what you're doing, 0:12:49.559 --> 0:12:52.640 Like if you're using it to scan products, it's sending 0:12:52.640 --> 0:12:55.959 that information to the home base. Well, Kroger could dynamically 0:12:56.120 --> 0:12:59.240 change prices as you were actually moving down the aisles, 0:12:59.320 --> 0:13:01.679 and before you can actually look at the digital price, 0:13:01.720 --> 0:13:04.440 the amount has changed from the person ahead of you. 0:13:04.760 --> 0:13:07.920 That amount will be reflected ultimately when you check out 0:13:08.000 --> 0:13:11.439 of the store. So what's more, Kroger has been gathering 0:13:11.440 --> 0:13:15.000 customer information for years because those customer loyalty tags that 0:13:15.040 --> 0:13:17.880 you can get at stores like grocery stores. Kroger has one, 0:13:18.200 --> 0:13:21.760 These entitle you to stuff like discounts, right well, it's 0:13:21.800 --> 0:13:24.679 also a tag that lets the store keep an ongoing 0:13:24.760 --> 0:13:29.320 tallly of everything you've ever purchased there. Through years of 0:13:29.440 --> 0:13:33.679 data gathering, Kroger knows the brands that specific customers gravitate toward, 0:13:34.000 --> 0:13:36.480 So the store knows if a specific customer is more 0:13:36.640 --> 0:13:38.880 likely to go for, you know, whatever brand is on 0:13:39.040 --> 0:13:42.760 sale versus a premium brand. Right, some people have brand 0:13:42.800 --> 0:13:45.760 loyalty and they'll buy that brand no matter if the 0:13:45.760 --> 0:13:50.040 price is up or not. Others are more pragmatic and 0:13:50.080 --> 0:13:54.040 they will go with whichever brand is the cheapest at 0:13:54.080 --> 0:13:57.079 the time, at least cheapest by volume or whatever. And 0:13:57.400 --> 0:13:59.840 Kroger knows this. It also knows like if you prefer 0:13:59.920 --> 0:14:02.920 you're salsa, spicy or mild. You know, it knows what 0:14:03.000 --> 0:14:05.040 kind of so does you drink, and the snacks you 0:14:05.120 --> 0:14:09.160 crave and what impulse buys you're inclined to indulge in 0:14:09.200 --> 0:14:12.480 when you get up there to the cashier. And sure, 0:14:13.080 --> 0:14:15.400 there are ways to use that information that can be 0:14:15.440 --> 0:14:18.680 of benefit both to the store and to the customer. 0:14:19.080 --> 0:14:22.040 But the concern is that through this system Kroger could 0:14:22.040 --> 0:14:26.960 engage in profiling and price gouging. That Kroger could dynamically 0:14:27.080 --> 0:14:31.120 change prices based on factors like race, age or sex. 0:14:31.360 --> 0:14:33.320 Now to be clear, the company has said that the 0:14:33.400 --> 0:14:37.200 idea is to quote lower prices more for customers where 0:14:37.200 --> 0:14:42.160 it matters most end quote, but skeptics remain well skeptical. 0:14:42.520 --> 0:14:46.800 So in an open letter to Kroger's CEO, Senators Warren 0:14:46.880 --> 0:14:50.400 and Casey ask for more information about the digital price 0:14:50.440 --> 0:14:53.920 tags and Kroger's plans, and expressed concerns that the store 0:14:53.960 --> 0:14:58.440 could use customer and world information combined to quote calibrate 0:14:58.560 --> 0:15:02.280 price increases to a distract maximum profits at a time 0:15:02.360 --> 0:15:05.240 when the amount of American's income spent on food is 0:15:05.280 --> 0:15:09.000 at a thirty year high. End quote. The senators pointed 0:15:09.000 --> 0:15:11.840 out that Kroger system could let the store take advantage 0:15:11.880 --> 0:15:15.040 of local conditions. So, for example, let's say that there 0:15:15.160 --> 0:15:18.160 is a big storm that's forecast to hit the area. 0:15:18.520 --> 0:15:20.560 Or let's say you live in a place like my 0:15:20.760 --> 0:15:25.040 hometown of Atlanta, Georgia, where the summer we have had 0:15:25.440 --> 0:15:29.280 numerous water main breaks happening all over the place. Well, 0:15:29.320 --> 0:15:31.800 you might find that a trip to the store means 0:15:31.840 --> 0:15:33.960 that you'll have to pay a premium if you want 0:15:33.960 --> 0:15:36.400 to get that bottled water you're gonna need while there's 0:15:36.440 --> 0:15:39.120 a boil water advisory in place and you have no 0:15:39.280 --> 0:15:43.440 power at home. It's like being kicked when you're down. Now, 0:15:43.800 --> 0:15:47.760 am I saying Kroger is currently doing this, that it 0:15:47.800 --> 0:15:53.280 is proactively raising prices when there's a prediction that demands 0:15:53.280 --> 0:15:56.240 going to surge. I am not saying that. I'm saying 0:15:56.280 --> 0:15:59.320 that the system allows for it to happen and to 0:15:59.360 --> 0:16:02.200 do it quickly. And that's before you get to the 0:16:02.240 --> 0:16:06.600 potential issue of personalized pricing. I mean, the whole purpose 0:16:06.640 --> 0:16:11.080 of the system ultimately is to maximize profit. That might 0:16:11.600 --> 0:16:14.080 mean that you're doing it in service of the customer, 0:16:14.280 --> 0:16:17.960 but it's not through an altruistic desire to help the customer. 0:16:18.000 --> 0:16:20.600 It's because that's the way you're going to end up 0:16:20.600 --> 0:16:23.200 making more money. And if it turns out that that's 0:16:23.320 --> 0:16:25.720 not the way you're going to make more money, then 0:16:25.760 --> 0:16:28.520 it's not going to happen. The letter is a really 0:16:28.560 --> 0:16:31.960 good read, and it isn't just a letter. The senators 0:16:32.000 --> 0:16:34.960 cite specific reference materials. In fact, there are certain pages 0:16:34.960 --> 0:16:39.040 where the references take up more space on the page 0:16:39.360 --> 0:16:42.040 than the letter does, but they really show that they've 0:16:42.080 --> 0:16:46.320 done their homework like this isn't just them pontificating and 0:16:46.480 --> 0:16:50.400 theorizing that there's an issue. They're citing sources. They point 0:16:50.440 --> 0:16:52.560 out that it's not just Kroger that's rolling out a 0:16:52.560 --> 0:16:57.160 system that's like this. Retailers like Walmart are doing it too. Meanwhile, 0:16:57.440 --> 0:16:59.960 several companies are really eager to find ways to let 0:17:00.200 --> 0:17:02.640 JI to figure out just how much money they can 0:17:02.720 --> 0:17:05.760 charge for their goods and services without taking off the 0:17:05.800 --> 0:17:09.479 customer base and driving them to competitors. Forbes magazine has 0:17:09.520 --> 0:17:14.040 an article titled Harnessing AI for Dynamic Pricing for Your Business, 0:17:14.240 --> 0:17:16.560 and it talks about this, and it was written by 0:17:16.640 --> 0:17:20.520 a writer and business advisor named Neil Sahota, who explains 0:17:20.520 --> 0:17:24.480 that AI boosts the concept of dynamic pricing quote by 0:17:24.560 --> 0:17:30.240 analyzing vast data sets to predict demand fluctuations, understand customer 0:17:30.400 --> 0:17:34.960 price sensitivity, and identify the optimal price point that maximizes 0:17:35.040 --> 0:17:39.080 revenue or market share end quote. Now, if we examine 0:17:39.119 --> 0:17:43.840 that statement closely, it can start to get sinister is 0:17:43.880 --> 0:17:47.919 probably not the right word, but certainly predatory could be 0:17:48.240 --> 0:17:51.960 a word I could use. Because you're talking about understanding 0:17:52.000 --> 0:17:56.600 customer price sensitivity. That phrase just means knowing how much 0:17:56.640 --> 0:18:01.520 you can charge before your customers decide they've had enough. Right, Like, 0:18:01.720 --> 0:18:05.760 the wording kind of falls into that. Let's couch this 0:18:05.880 --> 0:18:10.360 in a way so it doesn't sounds as predatory as 0:18:10.400 --> 0:18:12.760 it is, but it's really all about how much can 0:18:12.800 --> 0:18:16.800 I charge you before you walk away from me? So yeah, 0:18:16.359 --> 0:18:19.480 it's a little scary. But in other words, the AI 0:18:19.640 --> 0:18:22.000 might also be taking into account, you know, like I said, 0:18:22.040 --> 0:18:24.320 how much people are willing to pay before they just 0:18:24.359 --> 0:18:26.199 get fed up with the company and move on. And 0:18:26.240 --> 0:18:29.000 on one hand, all I'm really talking about here is 0:18:29.000 --> 0:18:31.639 a more real time approach to something that businesses have 0:18:31.720 --> 0:18:34.840 been doing forever. It's not like there's some sort of 0:18:34.960 --> 0:18:38.560 oracle out there that dictates what the price should be 0:18:38.640 --> 0:18:42.080 for a gallon of milk or a vacuum cleaner or 0:18:42.119 --> 0:18:45.560 a new car. These prices are determined by the companies 0:18:45.560 --> 0:18:48.400 that sell the things, and the companies are taking into 0:18:48.400 --> 0:18:52.080 account lots of factors that contribute to the cost that 0:18:52.119 --> 0:18:55.240 they incur in providing those goods and services in the 0:18:55.240 --> 0:18:58.880 first place. Right, Like, nothing is free, a company has 0:18:59.000 --> 0:19:01.840 to take that into accoun out or else the cost 0:19:01.920 --> 0:19:04.399 of doing business will be greater than what revenue it 0:19:04.480 --> 0:19:08.000 pulls in, and ultimately the business will fail. So you 0:19:08.080 --> 0:19:11.560 can't put all the blame on companies. You can't just 0:19:11.680 --> 0:19:16.160 you know, reduce them to greedy capitalists or something along 0:19:16.200 --> 0:19:19.840 those lines, although that can if they could turn into that, certainly, 0:19:19.880 --> 0:19:23.760 but that's the way business works. You gotta make money 0:19:24.200 --> 0:19:26.440 or else there's no reason to do the thing anymore. 0:19:26.720 --> 0:19:29.720 But I think it's the immediacy of AI power dynamic 0:19:29.760 --> 0:19:32.560 pricing that really gives me the willies, as well as 0:19:32.600 --> 0:19:35.960 the ability to respond to or even predict conditions that 0:19:36.040 --> 0:19:39.760 will lead to increased demand, allowing for price gouging opportunities. 0:19:40.080 --> 0:19:44.879 But don't worry, it gets worse. Okay, I'll explain how, 0:19:45.119 --> 0:19:56.800 but first let's take another quick break. We're back, and 0:19:56.840 --> 0:20:00.679 one other element that really comes into play when we 0:20:00.720 --> 0:20:04.240 are talking about AI and price strategies and dynamic pricing 0:20:04.520 --> 0:20:07.800 is the concept of competition. Now, if I own a 0:20:07.800 --> 0:20:10.760 grocery store and I decide I really want to squeeze 0:20:10.800 --> 0:20:14.520 every penny I possibly can from every shopper that I 0:20:14.680 --> 0:20:17.520 possibly can, and if I'm the only game in town, 0:20:17.760 --> 0:20:19.800 well I guess I'll get away with it, or I'll 0:20:19.840 --> 0:20:21.760 get run out of town one of the two, but 0:20:21.920 --> 0:20:24.960 presuming there are at least a few other grocers in 0:20:25.000 --> 0:20:28.000 the area, my customers will have alternatives to my store, 0:20:28.200 --> 0:20:31.320 and if they feel that my prices are unfair, they 0:20:31.320 --> 0:20:34.160 can move to the competition, So that creates an incentive 0:20:34.200 --> 0:20:38.600 for me to lower my prices. However, let's imagine we 0:20:38.880 --> 0:20:42.000 have a group of business owners and all of them 0:20:42.119 --> 0:20:47.560 are employing aipower dynamic pricing. They're not talking with each other, 0:20:47.640 --> 0:20:51.520 but they're all using the same or similar tools in 0:20:51.600 --> 0:20:54.439 order to figure out how much to price stuff. Now, 0:20:54.520 --> 0:20:56.880 all the competition is on a similar level, and it's 0:20:56.920 --> 0:21:01.080 possible that one business, by calculating thee at the competitors 0:21:01.080 --> 0:21:04.800 have increased prices, might actually lower prices on their items 0:21:04.920 --> 0:21:07.760 or to lure people away from the competition. But it's 0:21:07.880 --> 0:21:11.680 more likely that the AI will effectively set prices across 0:21:11.720 --> 0:21:14.399 the board so that no matter where a customer goes, 0:21:14.440 --> 0:21:17.480 they're going to face higher prices than they would like, 0:21:17.880 --> 0:21:20.840 as high as the market will allow, and you end 0:21:20.960 --> 0:21:25.000 up with an AI powered cartel. In other words, this 0:21:25.119 --> 0:21:29.000 is not hypothetical. We have seen this play out in 0:21:29.040 --> 0:21:32.200 certain businesses in certain parts of the world. For instance, 0:21:33.040 --> 0:21:35.959 there are people who allege that this is happening in 0:21:36.080 --> 0:21:39.600 rental housing. We turn to the tail of a software 0:21:39.640 --> 0:21:43.800 package called real Page from the company with the same name. 0:21:44.320 --> 0:21:48.399 So the company Real Page formed out of various acquisitions 0:21:48.440 --> 0:21:53.200 over several years, but its main product is property management 0:21:53.240 --> 0:21:57.000 systems for landlords, and one thing the software does is 0:21:57.040 --> 0:22:01.480 suggest rental fees for the landlord's properties. Now, the landlord 0:22:01.560 --> 0:22:05.080 first has to feed a lot of data into the system, 0:22:05.119 --> 0:22:09.080 including data that would not be publicly available, at least 0:22:09.080 --> 0:22:12.159 not to the general public. So that would include stuff 0:22:12.200 --> 0:22:15.639 like how many units the landlord oversees, how many of 0:22:15.680 --> 0:22:19.080 those are vacant, how much is being charged in rent 0:22:19.160 --> 0:22:22.959 at the moment, etc. Now, Real Page's power comes from 0:22:23.000 --> 0:22:26.399 the fact that it's an incredibly popular piece of software 0:22:26.760 --> 0:22:30.200 and lots of landlords depend upon it, which means real 0:22:30.240 --> 0:22:34.040 Page has access to a ton of data. So not 0:22:34.160 --> 0:22:37.520 only is real Page suggesting a rental price based off 0:22:37.920 --> 0:22:42.719 one landlord's specific situation, it's taking into account other rental 0:22:42.760 --> 0:22:46.920 properties managed by other landlords in the area and what 0:22:47.000 --> 0:22:49.840 those rental fees amount to. And this can mean that 0:22:49.920 --> 0:22:54.080 collectively Real Page starts to nudge markets into a sort 0:22:54.119 --> 0:22:57.760 of price fixed cartel. See. The whole goal of the 0:22:57.840 --> 0:23:01.159 software is to find that sweet spot in which landlords 0:23:01.160 --> 0:23:04.159 will maximize profits. So how much can you charge for 0:23:04.200 --> 0:23:07.639 your rental property before folks just pass it over? You 0:23:07.640 --> 0:23:10.000 don't want to underprice your property. You don't want to 0:23:10.080 --> 0:23:12.479 leave money on the table, But you also don't want 0:23:12.520 --> 0:23:14.520 to price it so high that no one's going to 0:23:14.560 --> 0:23:17.360 rent it and it just remains vacant. So real Page's 0:23:17.400 --> 0:23:20.480 purpose is to arrive at the magic number that's going 0:23:20.520 --> 0:23:24.760 to result in the most profit for its customers. The 0:23:24.880 --> 0:23:29.440 landlords now applied to a single location, like a single 0:23:29.520 --> 0:23:34.119 apartment complex. Arguably, this might not be so bad as 0:23:34.200 --> 0:23:36.760 long as people living in the rental property feel they're 0:23:36.760 --> 0:23:39.600 getting value for their money, it's not a huge issue. 0:23:39.680 --> 0:23:42.160 But when you start to apply this across a region, 0:23:42.480 --> 0:23:44.800