S07, E29: AI's impact on the labor market (with Brian Albrecht)

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The problem is that the distinction needs to be drawn [music] between the competence of the economists and the correctness of their analysis.
Welcome to the Mobile Dev Memo podcast. I'm your host, Eric Seufert, and I'm joined today by Brian Albrecht. Brian, welcome back to the podcast.

Yeah, happy to be here again.
So, we we last saw each other in Rome in March. How have you been since then?
Oh, good. A little hectic with the move, but you know, a lot going on in the world, a lot of fun things to talk about.
Well, we we probably have more than one episode's worth of content today. We'll see how how much we can get through. So, the impetus for bringing you back was a blog post that you'd written called you are not a horse, but you also wrote a really fascinating op-ed in the Financial Times was like 2 weeks ago ish.
A little while ago, yeah.
Called eliminate personal pricing and you risk harming consumers. And so, these two topics essentially have nothing to do with each other, but I want to try to talk through both of them. Before we get to that though, could you please reintroduce yourself to the audience?
I'm Brian Albrecht. I'm the chief economist at the International Center for Law and Economics. I'm a wide-ranging economist, hence why we're going to cover a few different things here. If people want to read more from me, I have a newsletter that the first one's from called Economic Forces, it's on Substack. That's weekly with Josh

Hendrickson and and yeah, I work on all sorts of things from we've talked about antitrust before, we'll talk about price discrimination, kind of you know, general economics is my beat.
Forgive me if I'm wrong. I feel like you've gained a lot of notoriety on Twitter recently over AI stuff. Do you perceive that? Is that true or
It's hard to tell how much is kind of everyone is writing about AI, but yeah, I'm leaning into AI I think more than than most even and trying to write a lot on it because I think as we'll get into, I think a lot of the discourse around AI could use some basic economics and kind of that's been my stick for as long as I've been writing. Like we you know, we come to a new thing, we come to a new thing like COVID or tariffs or whatever like the latest thing is in the news and kind of what I always do is try to ground our discussions, ground arguments in more or less basic economics.
And I think that that's been missing in a lot of the AI discourse that is either, you know, tech

heavy and really focus on the technical details or just kind of trying to be grounded in economics but trying to kind of be too clever by half and not and not starting kind of where I think most economists would start with things like supply and demand.
Yeah, and that's actually that's one thing I really appreciate about your writing cuz I I I do and I follow a lot of economists on on Twitter and they sort of immediately jump into like you know, just a a very technical analysis of things at times and and I think it's just it it it is helpful to just kind of start from first principles because you know, my sense is like the more complex and convoluted the argument and the more call it technically sophisticated, usually like the the less sort of like substance there is to it. It just seems like a an easy way of like hand waving away an opponent by just saying, "Oh, well, you didn't study as much economics as me, see? I know this.
Yeah, there is definitely that as a debate strategy and there's also even within, you know, economics, technical

economics, there are people who want to just kind of throw the kitchen sink at a problem and just try to think about all these bells and whistles together. This is an old thing within economics. And way to think about it is like does your model have a million moving parts or does it narrow down to one or two moving parts? And I think both of those serve different purposes. I think in the public discourse, which is the thing that I've I've kind of cut my teeth doing. I I do research as well, but I you know, you and I know each other from public writing stuff. I assume you're not reading my articles that are behind paywalls at academic journals. You know, I I think that there is a real benefit to Again, at least starting these conversations and at least grounding them in in in the basics, especially for something like AI where there's going to be so much we can kind of take it for granted a lot of things are going to change. It's not enough to just say, oh, the world's going to change, therefore, you know, I can spin kind of any theory I

want or I can, you know, I can kind of combine all these things and and and say the world is going to look completely different or it's going to look the same. No, let's try to do something in between where we kind of allow one thing to change. Like we're going to assume that for a simple example, like typesetting is going to go to zero. So, everything you do with typesetting, let's just imagine that goes to zero. Let's that takes no time. You think something and it gets typeset. Okay, that's at least an exercise we can talk through that it's not capturing everything with AI. It's not going to be you know, it's not actually going to even happen. Even typesetting is is still going to have a a cost to it, but you can at least kind of ground you can start building up the analysis of the problem in something, you know, concrete and and and kind of micro in that sense.
Yeah, and and to to be fair, I was I was talking about like your public writing, like on X and on your blog, not not, you know, technical academic writing. But okay, let's start there. Let's start there. So So you wrote a blog post, You Are Not a

Horse, which responds to the familiar argument that tractors replaced horses and AI can replace human labor, and therefore humans will eventually suffer the same economic fate as horses. So So and I you know, I'm not trying to straw man that is, I think an argument that everyone's heard. I don't I don't think I'm straw manning that, but, you know, just calling it out. I'm not trying to if I am. You argue that this analogy skips several important steps between automation and eliminating human labor demand across the economy. So just walk me through the argument and walk us through those steps that get skipped and and and why that argument kind of fails.
Yeah, well, let's let's first start at kind of the way that you would make the argument or that like you would get to that that conclusion very clearly. Like if an AI could do everything that I could do, including interacting the world, so you got robotics and all that stuff, it can look it you know, it can it can it can have the dashing good looks on the on the video podcast, it can do all the stuff, then

sure, it could do everything I could do, it could, you know, write my newsletters, there's nothing to it. I don't think anyone thinks that we're remotely close to that in general, and that's what he kind of comes to call perfect substitutes. If it was true that the that the AI and the robots were like exactly like me, then fine. The question is, in the world where we're a little bit different, in the same way that the car and the horse are a little bit different, right? The car can't do everything the horse can do. I still live by horses out in the suburbs of of the Twin Cities, you know, the the car isn't going on the rough terrain that the horses go along and the people like to ride them for fun. So, the car doesn't do everything. So, in the world where they're a little bit different kind of what are the ways of thinking about how the horse and the car are different or the humans and the AIs are different. And then where can they replace, where can they not? And one thing I think that's important is

that I stressed at the end of the piece, not just like thinking like at a particular job are are humans and AIs similar, but let's take a step back and let's think about the economy as a whole. One thing that's really interesting about humans is the just the huge diversity on the supply side, how much they can do. We can do all sorts of things. And on the demand side, you know, we like all sorts of things. And those forces are a little bit different than if if we're just looking about, you know, ATM tellers as {quote} {unquote}, you know, all workers. And that's not the same thing, right? Thinking about what happens to ATM tellers or what happens to telephone operators is not the same thing as workers overall. And so, what I'm trying to do in that piece is kind of okay, build out from there's one very narrow thing. Let's start Let's stick with the typesetting example. You know, there's typesetting. That's something that I do in my job. That's something that a lot

of us do, just type, you know, words out. Okay, then what happens when that can be replaced or one aspect of that? Well, there's verification. There's And just building up step by step of what happens in this logic. And again, I think I think the the the thing that I kind of grounded that essay in, and maybe this is jumping a little bit to the conclusion, but it's the one thing we need to think about that makes humans different is think of it from the consumer side. I need someone to do my taxes. Okay, I decide that now the AI can do that for cheaper. If that was the end the story, okay, what I've spent less money cuz now the AI can do it cheaper. What happens with that extra money that I've saved as a consumer? Well, I can and this is you know different or this really matters when you start thinking about all jobs. I can spend that money on something else. And now that happens in when I'm thinking about you know, the the telephone operator. Okay, now I I've

spent less on on telephone calls. I can spend that money on something else. But when you add across all sorts of of jobs and tasks and everything the extreme situation which is like the horses, humans are going to have like basically no role in the economy. You need that every dollar that you spend. Okay, I've saved a dollar because I don't have to do my taxes anymore. I'm letting AI do that. I don't have to hire a human to do my taxes anymore. I'm letting AI do that. Okay, what am I going to spend that next dollar on? A massage therapist? Okay, well then that's going to be something that at least for the time being still involves humans. So maybe we'll have you know, over time accountants become massage therapists. Whatever that process is, this is going to happen with all sorts of technological change in order for us to go the way of the horse. Like that would basically have to stop and we just have to Okay, I've saved this dollar at the accountants and I'm going to spend this dollar on more AI related stuff. And

that needs to happen you know, continuously all the way to everything I'm spending is on is on AI related stuff. And so that's where you get this extreme horse condition. It's it's a a edge case. It's an extreme condition. I'm trying to figure out when would it happen. Maybe there ends up being 5% of people who have jobs as massage therapists and you know, is that technically going the same way as the horse? Maybe. But you know, we're trying to make the argument as explicit as possible.
Well, yeah, I mean that's but that's just where that argument necessarily goes to have any real impact, right? Because it's like if it doesn't go to that extreme where like AI has actually done every single job and there's no role for humans anymore, then you get to some middle ground where it's just like, "Okay, well, we just kind of reorganize the economy then." And that's happened a lot. It's like it's just that's just not scary if you say that. That's just what happens in a technological innovation cycle.
And that is the I think the fundamental tension between how the people are

worried about you know, jobs going away and the way let's say most economists think about it. So, if you look at history, again, I'll go back to the the you know, the ATM or the or the telephone operators, there are examples where particular jobs went away for sure. I mean, farming is an is is a probably a good analogy. It used to be a huge part of the economy, now in terms of workers, now it's a very small part of the economy in terms of workers. Okay. Everyone, you know, and through the post I I people talked about this in the '30s, I think I cite Wassily Leontief in the '80s worried about this thinking that, you know, we won't be able to compete with mechanized machines and we'll we'll go the way of the horse. But all the time this has happened in the past, people have found new things to spend that savings on. So, now I don't need to hire a telephone operator or I need to pay less for a telephone operator, I can buy more stuff. I can

buy more health care has been a huge thing in the modern world. I can buy more services overall. I run, that's one of my my hobbies. I I spend extra money on a run coach or something like that, you know, all these things as we get richer, we find new things to spend money on.
[snorts]
And so, this dichotomy between oh, we at the micro level these jobs are going away, but at the macro level, jobs aren't going away. How do you square them? Well, it has to be the case that when I save the dollar at the telephone operator, I'm able to spend that on a new job, you know, that increases demand for this worker. This other occupation, run coaches, therefore now more people become run coaches, and people are still have the same amount of jobs. And that's this fundamental tension that economists, you know, economic historians look at it, it's like No, overall, over centuries, you know, labor share is basically the

same, meaning like how much income goes to workers. There's a few things along the way that are interesting, you know, the rise of of women entering the labor force changes some of the calculations. There's some demographics things, but for all intents and purposes for this conversation and the time frames that we're talking about with AI, if the labor share fluctuated between 65 and 70% for 200 years, maybe it'll go from 65 to 63 in the next 10 years, but that's not catastrophic. That's not the type of thing that people are, you know, the the AI's taking our jobs. That's not what they're pointing to.
Can we trust any of the data yet? Right? So, I mean, you probably seen these charts from the Financial Times, and they say, "Look, you know, AI's not having any meaningful impact whatsoever." And you see some that maybe there's like a more, call it like statistically significant impact at like uh you know, entry-level roles, but you know, you see the the data from Indeed, it shows that software engineering jobs

or job postings have actually increased. And so, what do Can we trust any of this data yet? I mean, is there any indication yet, or is it too early?
I'd say it's quite messy still. I mean, just to piggyback on that, some news outlet yesterday had about IT outsourcing to India, those jobs have gone up, which would seem like something that would be automated. A paper by several economists got updated today. The title is like Canaries in the Coal Mine. It's the one that where this idea that entry-level jobs, and particularly in entry-level jobs that are more exposed to AI are affected, that seems to be holding up. But, you know, I I think it's going to be a while before we know what happened to the like the current jobs. It's just it's such a mess. There's so many things pulling in different directions that's really hard to tell at this point and how much of it is general labor trends versus AI-centric stuff. Like implicitly, if we're going to say this is because of AI, we have to be having some

counterfactual where you'd say, "Imagine it was 2026, everything else was the same except for Sam Altman became a artist or something and, you know, AI was not really a thing yet in the way that we use AI now in as chatbots sort of things, then we would have more 26-year-old software engineers." Maybe. That's tough to tell. We also have, you know, general economic turmoil is always an issue. We have, you know, policies that are changing. We have We have just general change in the labor market and it'll be a while to tell, you know, how much will we attribute to each of those things. For For an analogy, there's a lot of discussion of productivity boom in the '90s. This was related to IT. There Some of it showed up in IT producers, you know, your your Microsofts of the world were getting very productive. Later, it showed up on on heavy users of IT. The accounting firms got much more productive when they could, you know, use computers more more straightforward.

That debate, let's say this let's debate about productivity from, let's say, '95 to '99 was still pretty active a decade later. Like until until the financial crisis kind of stole the thunder and economists started writing about the financial crisis. There's a lot of papers in 2006, 2007 disputing exactly how much of the productivity came from what. And I would expect the same thing with AI. That in 5 years we'll have more of a sense of what hap- what happened in 2026, but it still won't be this crystal clear thing.
Well, even the financial crisis, I mean I would say the jury's still out on a lot of aspects of that. That guy was taking I took a class last semester from an economist Karen Dynan. She was at Treasury and the Fed and and it was on history of financial crisis. And the first whole first half of the semester was on the the the GFC. And you know, that all the easy answers kind of fall apart when under like any scrutiny. Like there's not broad consensus on exactly what caused it, on

like exactly what the root causes were. You can trace a lot of it going back to like programs in the 50s and 60s. It's not just as you can't just point to, you know, reckless behavior alone as the catalyst.
Look, if in 10 years the labor share is 40% instead of 70% or 65% then we say, "Okay, assume there I'm assuming in 10 years there wasn't some major war. Okay, yeah, we can we can tell that." But if it looks more like historical fluctuations, the numbers just aren't that big. For example, there's a big debate on the labor share and how much of it has fallen since let's say around 1980 in the United States. So, how much of of income gets paid to workers compared to, you know, returns to to investors and things like that. How much of There's technical details, but it's maybe dropped a few percentage, but there's a big debate on

is that purely statistical issues? Is that purely changes in the tax code and how we account for different capital depreciation? Is that about a change to IT and intellectual property? You know, in a world with more intellectual property, it's and returns are going to intellectual property, it's kind of hard to disentangle exactly how much of that is labor or something else. These are like active debates that are happening with in economics that aren't settled over a 40-year period because we're really debating, okay, did it go down 3%, 4% versus not down at all? You know, if it turns out that it plummets, then okay, sure, we we have a clear answer.
There would be a natural tendency to slow things down if it did feel like we were headed into that extreme case even by the labs themselves, right? It's like the I I did this series called the Prosperous Society and then and I was

responding to the Satrini doomer post that he wrote whenever that was in April or whatever.
the spring sometime.
And it's so it's like, okay, like let's follow this trajectory where AI absorbs everything and at some point you've got the labs are just, you know, they train some new use case as part of the model that just wipes out some industry, right? So, you know, web development, let's say. Like, okay, I don't think it's like controversial say that like, yeah, there's probably way fewer web development companies starting now, right? Because you could just you could do use Lovable or something, you could use Claude code and you start But let's say that they do that with everything. Like, everything is just getting absorbed into these models. And so like, these models are aggregating these massive valuations just like eliminating every single industry like that that they encounter. At some point, they'd probably have to say, "Wait a second, if we keep down this path, there's no customers for us. It's so we need people to be buying our stuff at some At the end of the day, if we have no if we kill every customer, or every customer use wipe out every sort of possible commercial use case for

our tools cuz our tools they make the end product, then we will have no customers and we'll go out of business. At some point, they'd probably find there's some equilibrium where it's like, "Okay, if we leave these industries alone, they'll represent enough of a customer base for us to like they're maximizing revenue in that in that state." Is it Do you know there's going to be some recognition of that?
I don't know if that's the angle I would go because you could imagine So, imagine there are four big models in the in the big companies with models in the way that we're talking about it now. So, the equivalent of OpenAI, Anthropic, Google, and Grok, you know, it's SpaceX. I forget the exact you know, Twitter X, SpaceX, Cursor, whatever their their model, right? It could be the case you you might have a little bit of a commons problem where, you know, the the overall consumers are kind of spread across all four consume all four labs and therefore, you know, if I drive one company out of the business, that doesn't really affect me

how much they purchase from me because they're actually purchasing from the other guys and and that sort of thing. It could be an issue. I mean, there's there is the usual problem that it's sometimes it's just nice to have consumers versus bringing that all in in-house. I mean, it's it's it looks a little bit different than previous discussions of vertical integration, but I don't think we need to throw out, you know, what we've learned about the downsides and the tradeoffs of vertical integration. I mean, there's lots of things gained by vertically integrating, but we don't have one big vertically integrate firm in the US. We have lots of competition because there's the other force that is things get big. I mean, this is getting in the technical weeds and and definitely not something that's that's in my expertise, but just for my own anecdotal experience, like models that specialize have pulled ahead on some margins. For example, within within economics, there's this product called Refine, which is not just economics, but

academic papers. It reads them.
Yep.
They've really leaned into their niche, which is gradient academic type writing. There's some legal stuff and whatnot. And I think it's a very good product. And I'd argue anything, I think they're able to do stuff that the general models are unable to. Now, that might be short-term and in long enough scaling laws are going to just push everything that the biggest model just can do literally everything better, but usually some differentiation, some specialization makes you a little bit better. Now, why does that matter? You know, so instead of just rolling in this customer that you have into your model and this general huge model that you're training, maybe the the more efficient thing that'll happen is, you know, that customer has some sort of spin-off model, maybe it's grounded in your mega model, you know, and then it's got harnesses on it or whatnot, but like there at some point there tradeoffs and and and that pushes against your incentive to just become one big giant integrated firm that runs all your

consumers out of business and just does that for them.
And then I think, you know, why I mean I I I sort of intuitively know why psychologically, but like, you know, it sounds like I feel like the discourse weighs pretty heavily on the side of the negative possible outcomes and there's not nearly as much emphasis placed on like the potential positive outcomes, which I which I mean, maybe it's cuz it's obvious cuz most people use these tools and and there probably are a lot of people are more productive as a result of them and and there's are obvious it's obvious what benefits they bring, but but like imagine so, you know, we're not heading towards that extreme. There's there's some sort of natural breaking system that keeps us from getting there and we end up somewhere in the middle. What does that scenario look like? What if, you know, what if we get lower labor share, but there's shorter working hours and there's greater wage dispersion, there's some painful transitions, but the economy is is larger and and we're wealthier and and maybe this this is like a catalyst that's needed to for some sort of like congressional work on

I don't know some some programs that that that can be used to address lower working hours or lower labor force participation. Like what what are the potential positive outcomes here? Like what maybe the maybe that middle is actually the best possible place to be.
Yeah, people have written scary stories about the middle. Daron Acemoglu has a paper that shows like oh you get an you know, you get a little productivity boost but not enough to like grow the pie so extremely that you can do everything and there's kind of weird middle grounds that could happen. I I think that doesn't seem super plausible to me. I mean I think the you know, if AI is super productive, the pie grows bigger. That's great. If it's if it's somewhat productive, the pie still grows bigger. That's going to be good overall. I don't think there's kind of this I think there's you know, this this monotonicity that you know, the better the AIs are in the types of things we're talking about in in terms of like doing jobs. There's whole separate conversation about destroying the world

that other people need to think about but I mean I think you just you're able to do more, you're able to free up time to do other stuff in the same way that lots of other technological progress has done that. Things I mean, the dishwasher and the laundry machine freed up a lot of us to do a lot of other stuff and what does that overall look like? Well, looks like lower working hours. I mean, across the world. Let's not just think about the US. I mean, working hours overall go down as you get richer. That's very well established. Some of it shows up as easier jobs. My dad was a farmer. You know, I sit and talk on podcasts with hosts. Like is that Sure, even if we put in the same amount of quote unquote hours under the time use surveys like he worked a lot more than I do. Like that's that's just not comparable. And so, you take those people take those earnings, those you know, productivity improvements in lots of different ways. You know, about 100 years ago, Keynes

wrote an essay on the economic future of our grandchildren. He thought that, you know, you'd take all these gains in just pure fewer working hours that that Keynes's grandchildren would work 2 days a week. And that's not really what we've done. We still kind of have a schedule, but not completely. I mean, I don't want to I don't want to go again too much from my experience, but people spend more time on their phones during the work day than than you ever would have in the past. So, there's a lot of different ways that you can take that. Now, there there's a second part of your question, which is about the policy response. And I think this is one of them that I I I don't quite know where I come down on how to think about it because overall, I think I think if we take the last 100 years, let's say, of the United States, a lot of technological improvement has has generated also riches that then are used to things like expand out social welfare, you know, implement programs like social security

and Medicare, Medicaid. And these are these are are big policy programs that you can do when you're rich. And I Does that mean like every every time there's a crisis, the government is great at responding and and, you know, optimally like redistributing the gains from something? No, but there is some policy response, and overall, if you don't destroy the system, I'm optimistic that, you know, people will find find ways to make it work.
Well, yeah. I mean, I I kind of reluctantly included that at the end. If it first of all takes us in a different direction, but also you could have a policy response that makes things much worse. I mean,
Oh, definitely.
I think one of the place we were talking about the GFC, one of the places that there is more consensus is that the initial the was way too weak. It was way too tepid to actually address the actual risk. And so
So, that would be one thing that which the analog here would be that, you know, a bunch of people are driven out of jobs in the short run at least, and you you have like no, you

know, the unemployment insurance doesn't kick in enough, and so there's that. But then there's also the you could overreact or react in the wrong direction. I mean, most of the policy proposals that I've seen are about like actively preventing the, you know, productivity enhancements. Your things on on data centers, you know, wanting to tax or or ban data centers or tax compute, whatever that's supposed to mean, which isn't about there it being kind of too weak of a response, or it's about like actively discouraging it, and that's a definitely a possibility as well.
Well, yeah, I mean, I would I would just categorically oppose anything like that. I mean, especially anything that's more extreme than that, like like a total cessation of development on AI. I mean, you hear this stuff from like, you know, the kind of the the more fringe camps. But yeah, I mean, like that would seem to attempt something impossible, A, which is to

stop globally stop technological progress on something that is essentially ubiquitous now. I mean,
Yeah.
Well, you maybe you could do that in the US, maybe. Can't do it in China. But also, I mean, like you shouldn't probably shouldn't want to. You should probably just want to harness the power as effectively as possible.
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All right, I do want to move on to personalized pricing. So, we're going to we're going to switch gears pretty

radically here. So, you had an op-ed in the Financial Times recently and I read the Financial Times every day, so it was a real treat to see your name there and especially on a topic that's near and dear to my heart. The title is Eliminate personal pricing and you're risk harming consumers. And so, it it responds to a wave of state legislation intended to prohibit companies from using consumer data to set different prices for different people. The intuitive appeal is that everyone should pay the same price, but you argue that this can eliminate discounts and leave many consumers worse off. Could you walk us through your basic argument in the op-ed?
So, the starting point is why do you want to offer different prices? You know, when there there's lots of markets where you don't see this. My I said my dad was a farmer, when he'd go sell his corn at the local co-op, he got the same price as everyone else. That's kind of the going rate of, you know, whatever corn at whatever time of the year type of thing. The reason in general you want to do it is that most firms most of the time have what economists has called market power.

And that has some sounds kind of negative, but all it means is that if the firm would lower its price a little bit, there would be someone out there who would be willing to buy that didn't buy at the current price. So, if you if you gave a sale on this item, there's someone out there who would do it. Okay, very weak condition, but you don't do that because if you give this you know, if you drop the price on this good, you have to give up the money on, you know, the people who are buying it before. So, you know, got three consumers, consumers one and two who already buying the good. Do you lower the price to try to attract consumer three? Maybe not, because then you'd have to lower it for one and two. So, you So, there's this fundamental tension of all of these businesses want to sell more, basically, but also don't want to lower prices, and so that creates this this What do you do? How do you How do you kind of try to do better without being able to just lower prices overall?

There's a lot of business strategies that that follow and try to exploit this. The one I talk about in the in the piece is you offer coupons. So, what does a coupon do? Well, it does create two different prices, but the way it prevents everyone from taking the lower price is some people just won't fuss around with the coupons. Like, you know, back in the day, you know, I I remember my mom I say this in the in the hot bed I remember my mom at the kitchen table cutting coupons. Not everyone's willing to do that, and so therefore, you effectively induce two different prices. At some level, everyone could buy at full price or everyone could buy with the coupon, but in practice, some people take the coupon, some don't. And so, this shows up all the time. This shows up in in different quality of airline tickets, it shows up in different, you know, sizes of of coffees at the shop. You create different products that then allow people to sort

into the different ones to overcome the fundamental tension that you want to lower prices for the marginal consumers. You want to get it just for the new ones. Personalized pricing gets around this kind of wasteful activity of the coupons. It says, "I'm going to target to you a coupon. You're not going to have to cut it out. You're not going to have to waste time sitting at the kitchen table doing it, but because I'm targeting it to you, I don't have to offer it to everyone else, and therefore I'm able to gain the additional sale. You as the third consumer buy a good you weren't buying before, and overall, it's possible, it depends on the exact specifics, you know, consumers could be better off, prices could go down, it's an empirical question, but at least it's a possibility. And so personalized pricing is a way to kind of let's let tech, let the data you've collected, the systems you put in place for pricing to remove the waste.

You know, the these firms aren't doing it to be benevolent to now, you know, the analog of my mom in the story who's cutting coupons. They're not doing it to be nice to that third consumer, they're doing it cuz it makes more money for them, but in the example, it also makes the consumers better off, and so, it's it it's net beneficial. Again, in practice, it there's specifics and we need to go through it, but at least there's enough of a basic economic argument that like a pure ban to me is is not justified.
Price discrimination exists. It's not a new idea that emerged from the ability to do it in real time on a website or in an app. It it's existed for a very long time. The concept's very old. Walk me through the concept of price discrimination and the various flavors of it.
Yeah, so there's a I kind of went through it implicitly, but there's a few different ways to do it. Economists sometimes talk about first, second, and third degree price discrimination. I think that's a little bit confusing. I would call it like

group-based discounts. You know, I give this group a discount, I give students a discount, I give seniors a discount. It's not their data per se that it's grounded in. There's something else going on. It's it's offered to all seniors, so it's a little bit different. That comes with the cost, it comes with you need to verify these things, but it's another one. There's another self-selection system This self sometimes called second-degree price discrimination but not always. That's my example of the airline tickets. You know, everyone you know, let's put aside issues of capacity and the airline, you know, and the flight selling out but at the beginning everyone could buy first class or buy, you know, the one that they have to pay additional to have a carry-on, the basic sub-basic price, whatever that is. But people, you know, people who have the company paying for it will do one thing, people

who have more money will do another thing, people who are more flexible they'll select into the kind of the bins that they that the the firm wants them to. And then there's what we'll call individualized which is the the newer one which is, you know, I have data particularly on you and that allows me to set it just, you know, I'm going to send you a coupon. Now in practice the difference between you and you as a senior isn't particularly clear. You know, at the end of the day all of these things are going to be like well, what do I know about you, Eric? I know you are four of these six categories or something like that. And so it's just an intersection of all these group ones. That's more of an individualized price. So there are a few different ways to do it. They all are trying to handle the the same fun- fundamental tension.
Right. And and one of the issues here, Otter, do you use Blue Sky?
I I have at various points. I can't say I'm up to date on the latest Blue Sky discourse.

Well, I haven't checked but I would probably recommend you don't look for your op-eds URL on Blue Sky and read the comments. But Blue Sky is very opposed to what they deem to be surveillance pricing. And and so you know, you price discrimination already kind of carries this negative context. You call it price personalization which is how I tend to characterize it but but you know, you you've got surveillance pricing as a as a label for this practice, and that's I think that's what people are are claiming to be opposed to. It's it's really just the fact that you're collecting my data to then give me a price, which may not be the lowest possible price that you'd offer to anybody, and and that seems to be what people oppose here.
Yeah.
But you know, like you said, I mean I there's probably very little opposition to student discounts.
Mhm.
Right? Or senior discounts and and matinee tickets. That's just well, how how badly do you want to see it? Do you want to see the movie so badly that you'll do it in the middle of the day? Or or maybe that's probably correlated

with income, too. But so those those things people there's probably just very little opposition to those ideas. What what actually qualifies as personalized pricing? And what And why do you think the terminology here, the surveillance pricing, is is so critical to the political component of the debate?
Yeah, I mean I think there's there's an issue of fairness that I will admit that I I don't quite have a grasp of what people have in mind. But the basic idea is that you know, if it's transparent and we all kind of know the rules, I understand that the senior is getting a discount and I'm not. And when I become a senior, I'll be able to get those discounts. Or, you know, the military people are getting a discount, I'm not. Fine, I'm not in the military. And there's like this stuff happening in the background, and so I don't think that that's fair. I think there's a general uneasiness about your data overall in when people are at least like asked about it. So people seem very worried about when firms collect data on

them. You know this as well as anyone that you know, what that means in actual behavior is not that much. That people don't like when advertisers are are collecting data on them, but it turns out that they actually really like seeing ads for things that they think they'll like. So there's a little bit of that sort of broader data issues. I think there's also just a dimension of its its circling back to the AI stuff. It's new. We're not used to seeing these sorts of things and and it and it's it scares us a little bit. Yeah, there there's there's a lot of things going on for sure. I What I I I think if if you don't like price differences for because you think fairness is really important, completely understand that. I What I want to do in in the op-ed is kind of push back on two things. One is to acknowledge let's at least acknowledge that if we can't do these sorts of price discrimination, whether it's personalized prices that, you know, the

algorithms do or whether it's, you know, sorting into into, you know, people who take the early show or people go to the the night show, that the the alternative isn't that we all get the lowest price. Like that's just not this case. It's It's not It's not that if we ban No one believes this. If we ban coupons, no one believes that everyone would get the coupon price. That there's some trade-off here. Let's at least talk about that and acknowledge that. And that seems obvious, but when you see a lot of discourse on that, it's not obvious. For example, Consumer Reports and and Ground Collective published a study on Instacart and Instacart changing prices. This wasn't personalized prices at all. They had no evidence of that, even though personalized prices went off. It was basically randomized prices, AB testing on prices. They reported this as if this practice of AB pricing like the alternative is not doing the AB

pricing and everyone gets the lowest price that anyone saw. And that's what they said. This This policy of AB price testing is costing consumers I forget their ridiculous made-up number. But their implicit counterfactual is that everyone will get the lowest price. So, I I I want to say, "Let's not do that. Let's be serious that there are real trade-offs here, and that sure, if you care about price differences, let's admit that the price overall is going to be in the middle. The second thing that I wanted to push back on is that this shouldn't be an argument by definition. Just calling this surveillance pricing and trying to scare people with that, I don't think we want to go that way. I think we need to think about the core economic trade-offs. And what I'm pushing for is that these core economic trade-offs are there in the coupon case. That doesn't come with a scary term. And if the economics is the same, the terminology shouldn't change whether we ban it or not. We should think about

what's going to happen to things like prices and quantities and all the things that economists stress all the time. And so, just because all of a sudden Lina Khan came around and the FTC used the word surveillance pricing, like let's not fall into that. Let's think through this more clearly.
Well, I don't remember if she was also the originator of surveillance advertising, but it's very redolent of that argument. It's like, "Oh, look at they're collecting all this data." Okay. But, do you think like, you know, you think prices are set just at random? I mean, they're always set based on data. It may not have been data that's collected via an API or a cookie or something or a, you know, whatever. But, it's the price set with data. And that's what AB price testing is just creating a price curve, a demand curve.
A system to learn about that, you know, which is which the old way could have been just ask the random guy at the counter, and he Well, it seems like when we set it at $30, we get we're not very busy. And so, we should drop it down to 20.

Yeah, and not to mention, you know, when you optimize prices, firm does better, maybe they hire more people, maybe they invest more money in innovation, maybe they make better products. I mean, you can't just assume that like you're better off and the firm is no worse off if the price is the lowest possible price, all right? And they sell and they sell out and then some people can't get it and even because it sells out. I mean, were you the one that I think we cuz this argument came up in the World Cup, remember that? Or was it the World Cup or was it Taylor Swift tickets or something and and Mumdani was saying, well, people should be able to afford to go see the World Cup in their city. And well, it's like, no, some people are willing to pay a lot of money for these tickets. It like if you said, okay, we're capping the price, then the people that are going to get the tickets are the ones that can afford to stay up till midnight or whatever so when the tickets go on sale and they they have, you know, they they have no obligations the next morning so they can wake up at 9:00 and and and so you're just going to like massively skew who has access in a in a different way. You're not going to make it totally accessible, you're just going to skew who gets the access and who doesn't.
Yeah, it's just a different way of competing for the good that at the end of the day, you have a scarce resource.

Let's stick with the the World Cup tickets. If the price were zero or $10, some trivial amount, the amount of people who want to buy the ticket are going to be greater than the number of tickets available. You have to have some way of squaring that circle. You have to have some way of rationing that. One way is to set prices so that it does most of the work. You know, one is you could do an auction and then you'd you'd actually have, you know, doing kind of all the work in some sense. Most firms don't do that, but you let the price do kind of a lot of it. Or you set it low and rely on other mechanisms. Now, it's not just policy. I mean, there's a lot more going on here because you know, famously tickets for big stars, you also said Taylor Swift, right? Taylor Swift does not choose to take all of the possible profit on ticket sales and sets the price such that people do have to log on at a

particular time to get it, you know, and so part of it there's, you know, Taylor Swift isn't selling the tickets at $10. She's also not selling it at whatever the like resale market would set it at. So, so firms do this themselves. It's not just a, you know, price controls coming in, but you need some way to allocate these these goods and that and what, you know, economists will point to in general is that there are different types of systems. One of the beautiful things about prices, when you can use them, not everything can use prices and firms don't even use prices all the time, but it's if I'm willing to pay more, that gets transferred to the seller. Versus if I'm waiting in line, that's just wasted. The seller of the of the tickets doesn't get any benefit from me waiting in line and that becomes a deadweight loss as economists call it. One of the reasons you want to you want to harness them when you can is that it it doesn't generate all of this waste. So, to the the price discrimination

example, you know, the waste is the coupon. The waste is the waiting in line for the thing that goes on sale. You know, maybe you don't wait in line, but maybe you need to interrupt your day because you need to get there at a certain hour. Whatever these mechanisms are, but the actual, you know, the grocery store isn't benefiting from the time I'm spent, you know, cutting out coupons. And so, that's wasteful and we should recognize that as wasteful. Again, maybe maybe in the sake of fairness, we're willing to deal with some of that waste in the same way that, you know, in the sake of some fairness, Taylor Swift decides that she's going to give the the kids that can stay up all night, but don't have so so much money that they have a shot at getting a ticket, fine. But, let's recognize those tradeoffs.
Well, I mean, I think one thing is Mumford and Sons and that's to turn this into like a pointed attack on Mumford and Sons, but he's pretty good at these sort of like performative gimmicks and I think, you know, what he he did like it was Knicks tickets and I think what did

he reserve some of them for that some price cap?
I think there was some World Cup thing as well, but yeah, yeah, yeah.
Okay, so you set aside 20 tickets. Like, are you really changing anything? So now 20 luckily lucky people basically won the lottery. I mean, there that's not really changing that dynamic. They But okay, but I want to talk about so the digital economy personalizes every I mean, that's what recommendation systems are for. Search results are personalized, podcast recommendations are personalized. You know, firms have generally been more cautious about personalizing the transaction price itself. Why do you think that has remained the kind of the the sensitive frontier of personalization?
Yeah, it's it's a good question. People it seems to be consumer driven that consumers have a different reaction to price differences. For example, there was a a news story that that Wendy's was was thinking about implementing dynamic pricing, which is not the same thing as personalized pricing, but it's it's it's this idea that during the busy time

right around the lunch hour you're going to have a higher price versus a lower at 3:00 when kind of no one shows up, okay? Now, that's not it's not on the person, it's more systematic than that, but people seem to they're a big outrage about it and I'm guessing it's under some guises of fairness and and whatnot. There's also issues of like uncertainty. I mean, these things do come with waste. So I'm not going to act like just because an algorithm does it makes life completely wonderful. I mean, it could be the case that now I I worry that I could get a better instead of just going to Amazon and assuming I have the lower price, now I worry that oh, maybe some other places are going to be lower and I search around. Of course, Amazon has the incentive not to do that and wants, you know, doesn't want me to search around. So they're kind of internalizing that. But there is just a general I think it's really is coming from the consumer side that people just don't they have some sense of fairness and that changes over time. I mean, you know, the amount that people are comfortable with today versus maybe what

they would have been comfortable with a while ago, it be different. But yeah, I think I think it's really it's the firms, the sellers responding to, you know, what consumers are going to get upset about. In the same way that Instacart stopped their AB testing because of this big uproar about it, not because they were actually doing the things that people thought, but they were worried that consumers would think that they were doing these things and so that's the discipline.
Brian, this was great. How can people engage with you online? Where can they find you?
They can yell at me at BlueSky and I will see you there. Or you can follow me on on Twitter at Brian C. Albrecht as I mentioned before, Economic Forces is the newsletter on Substack. So subscribe to that and those are the main things, yeah.
Yeah, I will link to both of these articles in the show notes and I will also link to your X account. Brian, thank you so much for joining me today and sharing your wisdom.
Thanks so much.
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