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Speaker 1: Welcome to Tech Stuff, a production from iHeartRadio. Hey thereon

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Speaker 1: Welcome to Tech Stuff. I'm your host, job than Strickland.

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Speaker 1: I'm an executive producer with iHeart Podcasts. And how the

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Speaker 1: tech are you? So imagine that you're a kid, and

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Speaker 1: it's a bright summer morning, and you got the whole

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Speaker 1: day ahead of you. So you decide you want to

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Speaker 1: earn a little scratch. You're going to engage in the old,

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Speaker 1: tried and true hustle of elimonade stand. So you got

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Speaker 1: your lemons, you got your sugar, you got your water,

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Speaker 1: you got your ice, you got your paper cups, your

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Speaker 1: sunk around. I don't know ten bucks in your ingredients

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Speaker 1: and supplies all told, and your goal is to make

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Speaker 1: more money than you spent putting it all together. Well,

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Speaker 1: you got some decisions you need to make. Where are

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Speaker 1: you going to locate your lemonade stand? How are you

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Speaker 1: going to get the attention of people going by? And

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Speaker 1: perhaps most importantly, how much are you going to charge? Now?

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Speaker 1: If you charge too much, no one's going to buy

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Speaker 1: a cup of your lemonade. If you charge too little,

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Speaker 1: you'll end up making less than what you spent on materials.

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Speaker 1: Even if you sell out. So finding the balance between

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Speaker 1: what the market will bear and what you need in

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Speaker 1: order to cover your costs is one of those fundamental

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Speaker 1: journeys every business takes, whether it's products or services. Now,

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Speaker 1: an agile business operator might adjust things on the fly.

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Speaker 1: Maybe there are some external factors that are playing an effect. Right,

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Speaker 1: Maybe the day is extra hot, you know, maybe there's

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Speaker 1: a block party or a festival nearby, so there's more

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Speaker 1: foot traffic than normal. That would be a good place

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Speaker 1: to put your lemonade stand. These are the things that

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Speaker 1: are going to affect demand, and they could mean that

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Speaker 1: you could price your eliminade a little higher than you

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Speaker 1: would if it were on a quiet and overcast day. Heck,

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Speaker 1: maybe you realize early on that you price things too high,

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Speaker 1: so you quietly lower the cost of a couple of

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Speaker 1: lemonade and sales pick up. As a result, you have

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Speaker 1: engaged in what is called dynamic pricing. Now, from a

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Speaker 1: high level, this sort of pricing happens all the time,

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Speaker 1: though it's not always shifting moment to moment where we

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Speaker 1: can see it in action. So you might find that

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Speaker 1: a product in the store costs a little more or

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Speaker 1: a little less since the last time you went shopping

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Speaker 1: at that store. But in your typical store, you're not

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Speaker 1: likely to see the price tag change right before your eyes.

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Speaker 1: But that is changing, and it's largely thanks to AI.

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Speaker 1: AI powered dynamic pricing is intended to maximize profits for

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Speaker 1: business owners. That's really all there is to it. When

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Speaker 1: you get down to the bottom line. It's meant to

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Speaker 1: make people more money. And by people I mean business

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Speaker 1: owners customers. It's meant to take more of their money.

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Speaker 1: So maximization can get pretty crazy. An aipower dynamic pricing

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Speaker 1: tool could in theory adjust prices on products not just

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Speaker 1: based on local demand, but also as a result of

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Speaker 1: how the perception of the product changes. Here, I'm going

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Speaker 1: to give you a totally hypothetical example. Susie the TikTok

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Speaker 1: star is brainstorming ideas for content and she decides to

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Speaker 1: create a comedic sassy sketch and it centers around a

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Speaker 1: particular brand of butter. So she shoots her video, she

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Speaker 1: edits it, she uploads it, and the engagement comes pouring in.

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Speaker 1: Now among the countless teeny bopper fans watching her content,

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Speaker 1: there are also AI bots. One of those identifies the

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Speaker 1: brand of butter in the video and takes note of

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Speaker 1: the engagement that the video is getting. It's saying, huh,

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Speaker 1: lots of people are watching this video. And it does

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Speaker 1: some quick checking on available stats to figure out how

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Speaker 1: popular Susie the TikTok Star is in say a specific

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Speaker 1: local region. It figures out there's actually a huge fan

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Speaker 1: base in this area, and it concludes that at least

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Speaker 1: some of those fans are likely to rush out and

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Speaker 1: try to recreate Susie the TikTok Stars video and to

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Speaker 1: do that, they're going to want the genuine article. They're

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Speaker 1: going to want the same brand of butter that Susie used.

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Speaker 1: And so this AI, which happens to be a price

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Speaker 1: strategy AI for a local grocery store chain, makes the

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Speaker 1: call to bump the price on that particular brand of

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Speaker 1: butter because there's about to be a surge in demand.

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Speaker 1: The AI is not waiting to detect the demand is

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Speaker 1: surging and then respond to it. It's actually being proactive.

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Speaker 1: It is predicting the increase in demand and acting on

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Speaker 1: it now instead of later. Now, we've seen dynamic surge

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Speaker 1: pricing for years already, though not necessarily in the grocery market.

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Speaker 1: Ride haling companies have been making use of surge pricing

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Speaker 1: to respond to demand for years since they launched. You've

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Speaker 1: probably been in a situation in which the cost of hailing,

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Speaker 1: you know, an uber or lyft or whatever is way

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Speaker 1: higher than it normally would be for that part of

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Speaker 1: town in that time of day. This often happens when

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Speaker 1: there's a big event going on, like a huge music

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Speaker 1: concert or sporting event or conference or whatever, and during

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Speaker 1: that window of time you may be paying twice as

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Speaker 1: much or more for a ride as you would any

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Speaker 1: other day. Now, there are a couple of reasons for this.

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Speaker 1: One is that demand is exceeding supply. There are more

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Speaker 1: people who are looking for a ride than you have

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Speaker 1: drivers available to give people a ride, So raising prices

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Speaker 1: can discourage folks who don't need a ride as badly

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Speaker 1: as the ones who do. So the folks who really

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Speaker 1: need a ride, presuming they can still afford to pay

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Speaker 1: for it, will stick it out, and yeah, they'll have

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Speaker 1: to pay more than usual, but the point is they'll

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Speaker 1: get a car and they'll be able to get to

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Speaker 1: where they need to go. The folks who say WHOA, Okay, well, y'all,

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Speaker 1: maybe we shouldn't go to shake Shack. Maybe we should

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Speaker 1: just find a place to eat around here. They'll back

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Speaker 1: off and that helps improve availability. It decreases demand and

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Speaker 1: allows the supply to get to where it's needed. But

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Speaker 1: another reason is that surge pricing alerts drivers to areas

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Speaker 1: of high demand. So cities can be pretty darn big.

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Speaker 1: If you don't live in a city, you might not

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Speaker 1: be used to it. But if there's a large demand

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Speaker 1: that arises in one particular part of the city, drivers

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Speaker 1: will see it on their apps and they can actually

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Speaker 1: choose to reroute toward that part of town in order

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Speaker 1: to take advantage of the surge. So that means that

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Speaker 1: you get more drivers coming into the area, and that

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Speaker 1: means the supply for drivers increases. This in turn affects

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Speaker 1: demands and eventually the surge subsides. So this is one

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Speaker 1: way to dynamically respond to demand spikes. It's giving an

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Speaker 1: incentive to drivers to say, yes, there are probably people

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Speaker 1: around my area right now who are looking for a ride,

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Speaker 1: but way more people are looking for a ride across town,

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Speaker 1: and I will benefit from the fact that surge pricing

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Speaker 1: is in effect, So if I can get there in

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Speaker 1: time to pick up a ride, I can end up

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Speaker 1: getting more money for that ride, so it actually makes

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Speaker 1: sense for me to leave the area I'm in right

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Speaker 1: now and head across town now. Of course, the other

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Speaker 1: reason that surge pricing exists is that it creates opportunities

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Speaker 1: for the company to make that cheddar right because the

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Speaker 1: companies that are employing oh wait, I'm sorry, I'm sorry,

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Speaker 1: the companies that are contracting with the drivers, they certainly

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Speaker 1: want their cut of the pie as well. So surge

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Speaker 1: pricing means making more money from the same trips as

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Speaker 1: you would any other day. Though this doesn't last forever

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Speaker 1: because once supply and demand will reach parity, then surge

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Speaker 1: pricing will decrease. Otherwise you run the risk of alienating

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Speaker 1: your customers. So surge pricing is something that can address

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Speaker 1: a sudden increase in demand. And I don't think it's

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Speaker 1: a perfect solution, but at the same time like it.

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Speaker 1: It is a way to solve that problem. It kind

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Speaker 1: of stinks if you're one of the people who really

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Speaker 1: needs a ride. That means that you're going to be

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Speaker 1: paying extra. If you're able to wait, then eventually that

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Speaker 1: serge is going to drive enough drivers there to bring

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Speaker 1: the pricing down. But then on the flip side, if

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Speaker 1: you're a driver and you get to an area just

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Speaker 1: as surge pricing is on the decline, kind of stinks

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Speaker 1: for you because you missed out on an opportunity. So like,

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Speaker 1: it's not a perfect system by any stretch of the imagination.

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Speaker 1: But let me do you one better. What if the

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Speaker 1: AI knew how much money the customers were making, and

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Speaker 1: therefore the AI could adjust the price on products and

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Speaker 1: services in order to kind of give it that little

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Speaker 1: personal touch. And by personal touch, I mean adjust ca

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Speaker 1: so that they're as much, but not more than the

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Speaker 1: customer would be willing to pay, perhaps more than what

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Speaker 1: the person ahead of that customer or behind that customer

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Speaker 1: is paying, but just the right amount to get the

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Speaker 1: maximum money from that customer. Does that seem fair? Imagine

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Speaker 1: walking into a grocery store and finding out that the

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Speaker 1: carton of oat milk you've picked up it's actually going

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Speaker 1: to cost you forty cents more than it costs the

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Speaker 1: person who is behind you. That probably wouldn't feel really

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Speaker 1: good that. Keep in mind, this dynamic pricing is meant

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Speaker 1: to cater to income levels, so being charged less means

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Speaker 1: that you would be in a lower earning household, at

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Speaker 1: least assuming that the system is working as intended. So

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Speaker 1: in some ways you can look at this as being

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Speaker 1: kind right, bringing the cost of products to within the

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Speaker 1: budget of lower earning customers. But that's not really what

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Speaker 1: we're talking about here. We're talking about finding the price

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Speaker 1: that you can set as your maximum before going to

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Speaker 1: a point where people will just not buy anything at all.

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Speaker 1: You want to find that spot where someone is going

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Speaker 1: to hand over money. It might hurt, but they're gonna

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Speaker 1: do it because they need the thing, And it's essentially

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Speaker 1: price gouging, right. And when you flip it over, when

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Speaker 1: you say this is charging more for the same stuff

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Speaker 1: because the person who's doing the shopping is a big earner,

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Speaker 1: that feels wrong too. And yet this is something that

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Speaker 1: dynamic pricing could end up doing. In fact, it's more

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Speaker 1: than could end up doing. I'll explain more, but first

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Speaker 1: let's take a quick break. So before the break, I

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Speaker 1: was talking about this sort of situation in which a

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Speaker 1: store could use dynamic pricing to potentially personalize the shopping

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Speaker 1: experience so that certain individuals would end up having to

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Speaker 1: pay more for specific items versus others. Like you could

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Speaker 1: have all the people in the store at the same time,

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Speaker 1: and they'd each have an individual experience of how much

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Speaker 1: each particular item would cost. Here in the United States,

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Speaker 1: the grocery store chain Kroger is facing some scrutiny from

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Speaker 1: legislators like Senators Elizabeth Warren and Bob Casey over the

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Speaker 1: implementation of dynamic pricing that is at least in part

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Speaker 1: driven by AI. So the chain makes use of digital

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Speaker 1: price labels, and around eighteen percent of its stores I

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Speaker 1: think it's like five hundred locations across the United States.

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Speaker 1: They own and operate like twenty seven hundred and fifty,

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Speaker 1: so five hundred is around a little less than twenty

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Speaker 1: percent of their stores. But they have these digital price tags,

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Speaker 1: and of course with digital price tags, you could change

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Speaker 1: the price of things instantly. When the store makes use

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Speaker 1: of proper labels like paper labels, I guess I shouldn't

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Speaker 1: say proper. That's being really biased. Paper labels, well, there's

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Speaker 1: not really the opportunity to switch things up so quickly.

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Speaker 1: You're not going to have a shelf stocker just walking

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Speaker 1: directly ahead of a shopper, paying attention to what the

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Speaker 1: shoppers looking at and then slapping a new label on

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Speaker 1: in order to change the price. That's not going to happen.

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Speaker 1: But in these Kroger stores with the digital labels, it's

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Speaker 1: entirely possible to change the prices for specific items at

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Speaker 1: a moment's notice. Now, let's just say that you have

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Speaker 1: these digital labels. You pair this with cameras that have

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Speaker 1: things like facial recognition technology, and you gather information about customers.

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Speaker 1: Maybe you know a lot about this customer already because

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Speaker 1: they're in your loyalty program. Maybe they're using your app,

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Speaker 1: and the app is communicating in real time what you're doing,

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Speaker 1: Like if you're using it to scan products, it's sending

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Speaker 1: that information to the home base. Well, Kroger could dynamically

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Speaker 1: change prices as you were actually moving down the aisles,

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Speaker 1: and before you can actually look at the digital price,

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Speaker 1: the amount has changed from the person ahead of you.

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Speaker 1: That amount will be reflected ultimately when you check out

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Speaker 1: of the store. So what's more, Kroger has been gathering

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Speaker 1: customer information for years because those customer loyalty tags that

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Speaker 1: you can get at stores like grocery stores. Kroger has one,

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Speaker 1: These entitle you to stuff like discounts, right well, it's

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Speaker 1: also a tag that lets the store keep an ongoing

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Speaker 1: tallly of everything you've ever purchased there. Through years of

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Speaker 1: data gathering, Kroger knows the brands that specific customers gravitate toward,

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Speaker 1: So the store knows if a specific customer is more

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Speaker 1: likely to go for, you know, whatever brand is on

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Speaker 1: sale versus a premium brand. Right, some people have brand

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Speaker 1: loyalty and they'll buy that brand no matter if the

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Speaker 1: price is up or not. Others are more pragmatic and

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Speaker 1: they will go with whichever brand is the cheapest at

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Speaker 1: the time, at least cheapest by volume or whatever. And

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Speaker 1: Kroger knows this. It also knows like if you prefer

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Speaker 1: you're salsa, spicy or mild. You know, it knows what

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Speaker 1: kind of so does you drink, and the snacks you

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Speaker 1: crave and what impulse buys you're inclined to indulge in

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Speaker 1: when you get up there to the cashier. And sure,

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Speaker 1: there are ways to use that information that can be

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Speaker 1: of benefit both to the store and to the customer.

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Speaker 1: But the concern is that through this system Kroger could

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Speaker 1: engage in profiling and price gouging. That Kroger could dynamically

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Speaker 1: change prices based on factors like race, age or sex.

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Speaker 1: Now to be clear, the company has said that the

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Speaker 1: idea is to quote lower prices more for customers where

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Speaker 1: it matters most end quote, but skeptics remain well skeptical.

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Speaker 1: So in an open letter to Kroger's CEO, Senators Warren

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Speaker 1: and Casey ask for more information about the digital price

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Speaker 1: tags and Kroger's plans, and expressed concerns that the store

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Speaker 1: could use customer and world information combined to quote calibrate

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Speaker 1: price increases to a distract maximum profits at a time

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Speaker 1: when the amount of American's income spent on food is

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Speaker 1: at a thirty year high. End quote. The senators pointed

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Speaker 1: out that Kroger system could let the store take advantage

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Speaker 1: of local conditions. So, for example, let's say that there

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Speaker 1: is a big storm that's forecast to hit the area.

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Speaker 1: Or let's say you live in a place like my

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Speaker 1: hometown of Atlanta, Georgia, where the summer we have had

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Speaker 1: numerous water main breaks happening all over the place. Well,

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Speaker 1: you might find that a trip to the store means

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Speaker 1: that you'll have to pay a premium if you want

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Speaker 1: to get that bottled water you're gonna need while there's

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Speaker 1: a boil water advisory in place and you have no

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Speaker 1: power at home. It's like being kicked when you're down. Now,

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Speaker 1: am I saying Kroger is currently doing this, that it

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Speaker 1: is proactively raising prices when there's a prediction that demands

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Speaker 1: going to surge. I am not saying that. I'm saying

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Speaker 1: that the system allows for it to happen and to

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Speaker 1: do it quickly. And that's before you get to the

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Speaker 1: potential issue of personalized pricing. I mean, the whole purpose

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Speaker 1: of the system ultimately is to maximize profit. That might

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Speaker 1: mean that you're doing it in service of the customer,

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Speaker 1: but it's not through an altruistic desire to help the customer.

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Speaker 1: It's because that's the way you're going to end up

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Speaker 1: making more money. And if it turns out that that's

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Speaker 1: not the way you're going to make more money, then

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Speaker 1: it's not going to happen. The letter is a really

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Speaker 1: good read, and it isn't just a letter. The senators

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Speaker 1: cite specific reference materials. In fact, there are certain pages

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Speaker 1: where the references take up more space on the page

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Speaker 1: than the letter does, but they really show that they've

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Speaker 1: done their homework like this isn't just them pontificating and

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Speaker 1: theorizing that there's an issue. They're citing sources. They point

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Speaker 1: out that it's not just Kroger that's rolling out a

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Speaker 1: system that's like this. Retailers like Walmart are doing it too. Meanwhile,

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Speaker 1: several companies are really eager to find ways to let

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Speaker 1: JI to figure out just how much money they can

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Speaker 1: charge for their goods and services without taking off the

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Speaker 1: customer base and driving them to competitors. Forbes magazine has

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Speaker 1: an article titled Harnessing AI for Dynamic Pricing for Your Business,

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Speaker 1: and it talks about this, and it was written by

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Speaker 1: a writer and business advisor named Neil Sahota, who explains

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Speaker 1: that AI boosts the concept of dynamic pricing quote by

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Speaker 1: analyzing vast data sets to predict demand fluctuations, understand customer

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Speaker 1: price sensitivity, and identify the optimal price point that maximizes

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Speaker 1: revenue or market share end quote. Now, if we examine

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Speaker 1: that statement closely, it can start to get sinister is

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Speaker 1: probably not the right word, but certainly predatory could be

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Speaker 1: a word I could use. Because you're talking about understanding

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Speaker 1: customer price sensitivity. That phrase just means knowing how much

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Speaker 1: you can charge before your customers decide they've had enough. Right, Like,

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Speaker 1: the wording kind of falls into that. Let's couch this

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Speaker 1: in a way so it doesn't sounds as predatory as

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Speaker 1: it is, but it's really all about how much can

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Speaker 1: I charge you before you walk away from me? So yeah,

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Speaker 1: it's a little scary. But in other words, the AI

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Speaker 1: might also be taking into account, you know, like I said,

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Speaker 1: how much people are willing to pay before they just

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Speaker 1: get fed up with the company and move on. And

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Speaker 1: on one hand, all I'm really talking about here is

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Speaker 1: a more real time approach to something that businesses have

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Speaker 1: been doing forever. It's not like there's some sort of

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Speaker 1: oracle out there that dictates what the price should be

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Speaker 1: for a gallon of milk or a vacuum cleaner or

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Speaker 1: a new car. These prices are determined by the companies

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Speaker 1: that sell the things, and the companies are taking into

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Speaker 1: account lots of factors that contribute to the cost that

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Speaker 1: they incur in providing those goods and services in the

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Speaker 1: first place. Right, Like, nothing is free, a company has

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Speaker 1: to take that into accoun out or else the cost

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Speaker 1: of doing business will be greater than what revenue it

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Speaker 1: pulls in, and ultimately the business will fail. So you

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Speaker 1: can't put all the blame on companies. You can't just

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Speaker 1: you know, reduce them to greedy capitalists or something along

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Speaker 1: those lines, although that can if they could turn into that, certainly,

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Speaker 1: but that's the way business works. You gotta make money

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Speaker 1: or else there's no reason to do the thing anymore.

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Speaker 1: But I think it's the immediacy of AI power dynamic

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Speaker 1: pricing that really gives me the willies, as well as

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Speaker 1: the ability to respond to or even predict conditions that

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Speaker 1: will lead to increased demand, allowing for price gouging opportunities.

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Speaker 1: But don't worry, it gets worse. Okay, I'll explain how,

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Speaker 1: but first let's take another quick break. We're back, and

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Speaker 1: one other element that really comes into play when we

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Speaker 1: are talking about AI and price strategies and dynamic pricing

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Speaker 1: is the concept of competition. Now, if I own a

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Speaker 1: grocery store and I decide I really want to squeeze

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Speaker 1: every penny I possibly can from every shopper that I

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Speaker 1: possibly can, and if I'm the only game in town,

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Speaker 1: well I guess I'll get away with it, or I'll

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Speaker 1: get run out of town one of the two, but

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Speaker 1: presuming there are at least a few other grocers in

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Speaker 1: the area, my customers will have alternatives to my store,

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Speaker 1: and if they feel that my prices are unfair, they

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Speaker 1: can move to the competition, So that creates an incentive

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Speaker 1: for me to lower my prices. However, let's imagine we

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Speaker 1: have a group of business owners and all of them

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Speaker 1: are employing aipower dynamic pricing. They're not talking with each other,

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Speaker 1: but they're all using the same or similar tools in

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Speaker 1: order to figure out how much to price stuff. Now,

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Speaker 1: all the competition is on a similar level, and it's

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Speaker 1: possible that one business, by calculating thee at the competitors

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Speaker 1: have increased prices, might actually lower prices on their items

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Speaker 1: or to lure people away from the competition. But it's

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Speaker 1: more likely that the AI will effectively set prices across

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Speaker 1: the board so that no matter where a customer goes,

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Speaker 1: they're going to face higher prices than they would like,

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Speaker 1: as high as the market will allow, and you end

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Speaker 1: up with an AI powered cartel. In other words, this

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Speaker 1: is not hypothetical. We have seen this play out in

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Speaker 1: certain businesses in certain parts of the world. For instance,

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Speaker 1: there are people who allege that this is happening in

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Speaker 1: rental housing. We turn to the tail of a software

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Speaker 1: package called real Page from the company with the same name.

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Speaker 1: So the company Real Page formed out of various acquisitions

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Speaker 1: over several years, but its main product is property management

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Speaker 1: systems for landlords, and one thing the software does is

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Speaker 1: suggest rental fees for the landlord's properties. Now, the landlord

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Speaker 1: first has to feed a lot of data into the system,

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Speaker 1: including data that would not be publicly available, at least

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Speaker 1: not to the general public. So that would include stuff

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Speaker 1: like how many units the landlord oversees, how many of

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Speaker 1: those are vacant, how much is being charged in rent

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Speaker 1: at the moment, etc. Now, Real Page's power comes from

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Speaker 1: the fact that it's an incredibly popular piece of software

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Speaker 1: and lots of landlords depend upon it, which means real

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Speaker 1: Page has access to a ton of data. So not

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Speaker 1: only is real Page suggesting a rental price based off

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Speaker 1: one landlord's specific situation, it's taking into account other rental

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Speaker 1: properties managed by other landlords in the area and what

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Speaker 1: those rental fees amount to. And this can mean that

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Speaker 1: collectively Real Page starts to nudge markets into a sort

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Speaker 1: of price fixed cartel. See. The whole goal of the

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Speaker 1: software is to find that sweet spot in which landlords

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Speaker 1: will maximize profits. So how much can you charge for

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Speaker 1: your rental property before folks just pass it over? You

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Speaker 1: don't want to underprice your property. You don't want to

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Speaker 1: leave money on the table, But you also don't want

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Speaker 1: to price it so high that no one's going to

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Speaker 1: rent it and it just remains vacant. So real Page's

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Speaker 1: purpose is to arrive at the magic number that's going

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Speaker 1: to result in the most profit for its customers. The

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Speaker 1: landlords now applied to a single location, like a single

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Speaker 1: apartment complex. Arguably, this might not be so bad as

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Speaker 1: long as people living in the rental property feel they're

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Speaker 1: getting value for their money, it's not a huge issue.

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Speaker 1: But when you start to apply this across a region,

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Speaker 1: you reach a point where the rental market has little

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Speaker 1: to no competition and people will struggle to afford a

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Speaker 1: place to live. There will be no alternatives they can

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Speaker 1: turn to. They'll just be looking at kind of a

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Speaker 1: landscape of high prices across the board. Critics of real

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Speaker 1: Page have argued that the third party software has led

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Speaker 1: to an anti competitive marketplace and that it turns landlords

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Speaker 1: into a monopoly, even if the landlords themselves are unaware

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Speaker 1: of this, and some critics are from really important positions

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Speaker 1: and authority. For example, back in twenty seventeen, a member

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Speaker 1: of the Federal Trade Commission named Maureen Ohlhausen argued that

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Speaker 1: algorithmically driven price strategies applied across multiple landlords is really

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Speaker 1: no different from a human being collecting all this information

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Speaker 1: and then telling all the competitors how to fix their

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Speaker 1: prices so that they can maximize profits and not worry

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Speaker 1: about competition. Now, that would be against the law if

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Speaker 1: a human being did it, and she was saying, maybe

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Speaker 1: we should consider something similar for algorithmically driven strategies, because

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Speaker 1: technically that's not accounted for in the laws. Real Page

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Speaker 1: is a defendant named in several lawsuits that have accused

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Speaker 1: the company of anti competitive practices. J Karma has a

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Speaker 1: great piece on this titled We're entering an AI price

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Speaker 1: fixing dystopia in the Atlantic, and that explains the Atlantic

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Speaker 1: is where the article's from. We're not entering an AI

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Speaker 1: price fixing dystopia in the Atlantic anyway. This article explains

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Speaker 1: how plaintifs I've argued that Real Page is essentially the

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Speaker 1: lynch pen that's holding together a monopoly among landlords, at

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Speaker 1: least in certain regions. And among the bits that Karma

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Speaker 1: reveals is that Real Page allegedly strong arms its clients,

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Speaker 1: so the landlords, in other words, to adopt the price

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Speaker 1: suggestions that Real Page it submits, or else those landlords

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Speaker 1: risk getting the boot from the company. Karma has quoted

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Speaker 1: an antitrust lawyer for the American Economic Liberties Project named

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00:25:45,280 --> 00:25:50,040
Speaker 1: Lee Hepner, who says, quote enforced compliance is the hallmark

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00:25:50,080 --> 00:25:53,320
Speaker 1: feature of any cartel end quote. The company I feel

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00:25:53,359 --> 00:25:56,200
Speaker 1: I should add disputes these claims and says that they

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00:25:56,280 --> 00:26:00,160
Speaker 1: are baseless. Karma also points out that in the United States,

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00:26:00,200 --> 00:26:04,959
Speaker 1: winning a case against a company that is allegedly responsible

439
00:26:04,960 --> 00:26:09,280
Speaker 1: for anti competitive collusion is really tough because antitrust laws

440
00:26:09,359 --> 00:26:12,720
Speaker 1: do not take into account this particular situation. In your

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00:26:12,800 --> 00:26:16,119
Speaker 1: good old fashioned price fixing arrangement, you've got two or

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00:26:16,119 --> 00:26:20,000
Speaker 1: more entities that meet and they agree upon pricing, they

443
00:26:20,040 --> 00:26:23,200
Speaker 1: remove competition from a region, and they milk the consumer

444
00:26:23,280 --> 00:26:25,240
Speaker 1: for all their worth. But you have to prove that

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00:26:25,240 --> 00:26:28,639
Speaker 1: there's collusion between these two or more parties. Right. However,

446
00:26:28,680 --> 00:26:31,280
Speaker 1: in this case, we're not talking about two entities that

447
00:26:31,320 --> 00:26:34,439
Speaker 1: are in direct communication with one another. It's possible they

448
00:26:34,720 --> 00:26:37,000
Speaker 1: never met at all, they don't even know each other.

449
00:26:37,119 --> 00:26:40,200
Speaker 1: They just both happen to use the same software package.

450
00:26:40,359 --> 00:26:43,200
Speaker 1: So it's the software doing all the work of collusion,

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00:26:43,440 --> 00:26:47,080
Speaker 1: and you don't have any actual, traditional direct collusion between

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00:26:47,119 --> 00:26:50,040
Speaker 1: the parties themselves. And since the law doesn't account for

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00:26:50,080 --> 00:26:54,000
Speaker 1: this in most places, there's really no recourse unless laws

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00:26:54,000 --> 00:26:56,760
Speaker 1: are amended or new laws proposed to deal with the situation.

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00:26:57,160 --> 00:26:59,800
Speaker 1: We're kind of in a bit of a fix. But wait,

456
00:27:00,400 --> 00:27:03,560
Speaker 1: it gets worse. We don't even need a situation in

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00:27:03,560 --> 00:27:07,200
Speaker 1: which everyone is using the same software package. With Real Page.

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00:27:07,240 --> 00:27:10,080
Speaker 1: There's a dispute between how many landlords actually use it.

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00:27:10,320 --> 00:27:14,480
Speaker 1: University of Tennessee law professor Maurice Stuck says that more

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00:27:14,520 --> 00:27:18,040
Speaker 1: than forty markets in the US sees thirty to sixty

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00:27:18,080 --> 00:27:23,879
Speaker 1: percent of landlords of multifamily apartment units relying upon real

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00:27:23,920 --> 00:27:29,080
Speaker 1: Page for their pricing strategies. Now, the company released a

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00:27:29,119 --> 00:27:32,639
Speaker 1: statement arguing that it's only used in seven percent of

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00:27:33,280 --> 00:27:37,840
Speaker 1: rental units. Karma, however, points out that this is largely

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00:27:37,880 --> 00:27:41,680
Speaker 1: because Real Page is talking about all rental properties, every

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00:27:41,680 --> 00:27:45,920
Speaker 1: single one, whereas what Stuck was saying was specifically multifamily

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00:27:45,960 --> 00:27:48,840
Speaker 1: apartment buildings. But even if we're talking at the low

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00:27:48,960 --> 00:27:52,080
Speaker 1: end of usage, if other landlords are using other property

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00:27:52,080 --> 00:27:56,240
Speaker 1: management software packages with price strategy driven by AI, you

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00:27:56,280 --> 00:27:59,920
Speaker 1: can arrive at the same outcome. So Maurice Stuck also

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00:28:00,000 --> 00:28:02,879
Speaker 1: so studied the case in Germany in which two different

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00:28:02,920 --> 00:28:07,480
Speaker 1: gas stations, each using a different pricing algorithm, saw their

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00:28:07,520 --> 00:28:11,440
Speaker 1: margins increase by thirty eight percent once both of them

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00:28:11,520 --> 00:28:15,600
Speaker 1: were using these algorithms. Now earlier, when just one of

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00:28:15,640 --> 00:28:19,679
Speaker 1: them had employed a pricing algorithm, its price margin didn't

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00:28:19,760 --> 00:28:22,520
Speaker 1: change at all. It was only when each of them

477
00:28:22,800 --> 00:28:26,840
Speaker 1: were on their own pricing algorithms that the situation changed.

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00:28:27,160 --> 00:28:30,240
Speaker 1: So clearly, if you're a business owner, the lesson is

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00:28:30,280 --> 00:28:35,040
Speaker 1: that AI driven pricing strategies make you more money. They

480
00:28:35,119 --> 00:28:39,280
Speaker 1: are more profitable, so the incentive is clear it is

481
00:28:39,400 --> 00:28:43,160
Speaker 1: best to get on board. For customers, it's much worse

482
00:28:43,200 --> 00:28:45,960
Speaker 1: news because it means you're being hit by the effects

483
00:28:46,080 --> 00:28:50,440
Speaker 1: of collusion, even if what's actually going on isn't collusion.

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00:28:50,560 --> 00:28:54,160
Speaker 1: From the traditional legal perspective, the legal thing is likely

485
00:28:54,200 --> 00:28:56,240
Speaker 1: to be an issue for a while because it takes

486
00:28:56,280 --> 00:28:59,200
Speaker 1: time for laws to change, especially at a big scale.

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00:29:00,080 --> 00:29:03,280
Speaker 1: All our regions have already taken steps. However, San Francisco

488
00:29:03,480 --> 00:29:06,960
Speaker 1: recently passed an ordinance that forbids companies from using price

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00:29:06,960 --> 00:29:10,440
Speaker 1: strategy software that makes use of non public data among

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00:29:10,480 --> 00:29:13,520
Speaker 1: competitors in an effort to arrive at prices. So that's

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00:29:13,560 --> 00:29:16,280
Speaker 1: good news for the folks of San Francisco, a region

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00:29:16,320 --> 00:29:20,880
Speaker 1: where it is monstrously expensive, like the cost of living

493
00:29:20,920 --> 00:29:25,280
Speaker 1: in San Francisco is truly out of this world. Now,

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00:29:25,320 --> 00:29:28,479
Speaker 1: I'm sure we're going to see regulations and laws follow

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00:29:28,520 --> 00:29:31,040
Speaker 1: at some point to protect the free market, if not

496
00:29:31,240 --> 00:29:35,000
Speaker 1: the customer, unless, of course, the company's profiting off these

497
00:29:35,040 --> 00:29:39,520
Speaker 1: practices lobby hard enough to prevent any opposition to form

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00:29:39,560 --> 00:29:43,280
Speaker 1: within the government. But come on, how likely is that? Hi,

499
00:29:43,360 --> 00:29:48,200
Speaker 1: for one, welcome our robot overgrossers. Okay, that's it about

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00:29:48,240 --> 00:29:52,400
Speaker 1: how AI can help business owners, but in the same

501
00:29:52,760 --> 00:29:56,640
Speaker 1: breath create an environment of collusion that hurts the consumer.

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00:29:56,960 --> 00:29:59,280
Speaker 1: And I don't think this is always going to be

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00:29:59,320 --> 00:30:01,240
Speaker 1: the case, but I think I think if we are

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00:30:01,320 --> 00:30:05,600
Speaker 1: depending upon AI for pricing strategy, it's more likely to

505
00:30:05,600 --> 00:30:10,200
Speaker 1: happen than not, because if the mandate is find the

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00:30:10,240 --> 00:30:13,800
Speaker 1: way where I'm going to make the most money. Ultimately,

507
00:30:13,840 --> 00:30:16,920
Speaker 1: this is where we arrive, right, There's no there's no

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00:30:17,000 --> 00:30:22,560
Speaker 1: other destination but the effects of collusion. That's the only

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00:30:22,720 --> 00:30:25,600
Speaker 1: place you can go to. And the only alternative would

510
00:30:25,640 --> 00:30:28,880
Speaker 1: be businesses that don't get on the AI train, that

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00:30:29,040 --> 00:30:33,120
Speaker 1: do try to compete by offering lower prices. And whether

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00:30:33,240 --> 00:30:36,000
Speaker 1: or not that works, I can't say. I suppose in

513
00:30:36,040 --> 00:30:40,080
Speaker 1: the short term it could force the AI driven strategies

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00:30:40,160 --> 00:30:45,160
Speaker 1: to adjust so that they're not being completely drained of

515
00:30:45,240 --> 00:30:48,640
Speaker 1: customers by the competition, But I imagine there'd be a

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00:30:48,800 --> 00:30:52,080
Speaker 1: great deal of pressure to get that other business to

517
00:30:52,520 --> 00:30:55,720
Speaker 1: get on board, you know, be a team player, even

518
00:30:55,760 --> 00:30:58,400
Speaker 1: though it's legal to be in a team like that.

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00:30:59,120 --> 00:31:01,720
Speaker 1: I hope all of you out there are doing well,

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00:31:02,320 --> 00:31:11,760
Speaker 1: and I will talk to you again really soon. Tech

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00:31:11,840 --> 00:31:16,240
Speaker 1: Stuff is an iHeartRadio production. For more podcasts from iHeartRadio,

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00:31:16,560 --> 00:31:20,280
Speaker 1: visit the iHeartRadio app, Apple Podcasts, or wherever you listen

523
00:31:20,320 --> 00:31:24,840
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