I'm coming to COLORADO! Catch me in DENVER on Thu (Jan 22) at The Tattered Cover, and in COLORADO SPRINGS this weekend (Jan 23β25) where I'm the Guest of Honor at COSine. Then I'll be in OTTAWA on Jan 28 at Perfect Books and in TORONTO with Tim Wu on Jan 30.
Google is spending a lot on AI, but what's not clear is how Google will make a lot from AI. Or, you know, even break even. Given, you know, that businesses are seeing zero return from AI:
But maybe they've figured it out. In a recent edition of his BIG newsletter, Matt Stoller pulls on several of the strings that Google's top execs have dangled recently:
The first string: Google's going to spy on you a lot more, for the same reason Microsoft is spying on all of its users: because they want to supply their AI "agents" with your personal data:
https://www.youtube.com/watch?v=0ANECpNdt-4
Google's announced that it's going to feed its AI your Gmail messages, as well as the whole deep surveillance dossier the company has assembled based on your use of all the company's products: Youtube, Maps, Photos, and, of course, Search:
Apple already charges Google more than $20b/year not to enter the search market; now they're going to be charging Google billions not to stay out of the AI market, too. Meanwhile, Google will get to spy on Apple customers, just like they spy on their own users. Anyone who says that Apple is ideologically committed to your privacy because they're real capitalists is a sucker (or a cultist):
But the big revelation is how Google is going to make money with AI: they're going to sell AI-based "personalized pricing" to "partners," including "Walmart, Visa, Mastercard, Shopify, Gap, Kroger, Macyβs, Stripe, Home Depot, Lowe's, American Express, etc":
Personalized pricing, of course, is the polite euphemism for surveillance pricing, which is when a company spies on you in order to figure out how much they can get away with charging you (or how little they can get away with paying you):
It's a weird form of cod-Marxism, whose tenet is "From each according to their desperation; to each according to their vulnerability." Surveillance pricing advocates say that this is "efficient" because they can use surveillance data to offer you discounts, too β like, say you rock up to an airline ticket counter 45 minutes before takeoff and they can use surveillance data to know that you won't take their last empty seat for $200, but you would fly in it for $100, you could get that seat for cheap.
This is, of course, nonsense. Airlines don't sell off cheap seats like bakeries discounting their day-olds β they jack up the price of a last-minute journey to farcical heights.
Google also claims that it will only use its surveillance pricing facility to offer discounts, and not to extract premiums. As Stoller points out, there's a well-developed playbook for making premiums look like discounts, which is easy to see in the health industry. As Stoller says, the list price for an MRI is $8,000, but your insurer gets a $6000 "discount" and actually pays $1970, sticking you with a $30 co-pay. The $8000 is a fake number, and so is the $6000 β the only real price is the $30 you're paying.
The whole economy is filled with versions of this transparent ruse, from "department stores who routinely mark everything as 80% off" to pharmacy benefit managers:
Google, meanwhile, is touting its new "universal commerce protocol" (UCP), a way for AI "agents" to retrieve prices and product descriptions and make purchases:
Right now, a major hurdle to "agentic AI" is the complexity of navigating websites designed for humans. AI agents just aren't very reliable when it comes to figuring out which product is which, choosing the correct options, and putting it in a shopping cart, and then paying for it.
Some of that is merely because websites have inconsistent "semantics" β literally things like the "buy" button being called something other than "buy button" in the HTML code. But there's a far more profound problem with agentic shopping, which is that companies deliberately obfuscate their prices.
This is how junk fees work, and why they're so destructive. Say you're a hotel providing your rate-card to an online travel website. You know that travelers are going to search for hotels by city and amenities, and then sort the resulting list by price. If you hide your final price β by surprising the user with a bunch of junk fees at checkout, or, better yet, after they arrive and put their credit-card down at reception β you are going to be at the top of that list. Your hotel will seem like the cheapest, best option.
But of course, it's not. From Ticketmaster to car rentals, hotels to discount airlines, rental apartments to cellular plans, the real price is withheld until the very last instant, whereupon it shoots up to levels that are absolutely uncompetitive. But because these companies are able to engage in deceptive advertising, they look cheaper.
And of course, crooked offers drive out honest ones. The honest hotel that provides a true rate card, reflecting the all-in price, ends up at the bottom of the price-sorted list, rents no rooms, and goes out of business (or pivots to lying about its prices, too).
Online sellers do not want to expose their true prices to comparison shopping services. They benefit from lying to those services. For decades, technologists have dreamed of building a "semantic web" in which everyone exposes true and accurate machine-readable manifests of their content to facilitate indexing, search and data-mining:
This has failed. It's failed because lying is often more profitable than telling the truth, and because lying to computers is easier than lying to people, and because once a market is dominated by liars, everyone has to lie, or be pushed out of the market.
Of course, it would be really cool if everyone diligently marked up everything they put into the public sphere with accurate metadata. But there are lots of really cool things you could do if you could get everyone else to change how they do things and arrange their affairs to your convenience. Imagine how great it would be if you could just get everyone to board an airplane from back to front, or to stand right and walk left on escalators, or to put on headphones when using their phones in public.
Wanting it badly is not enough. People have lots of reasons for doing things in suboptimal ways. Often the reason is that it's suboptimal for you, but just peachy for them.
Google says that it's going to get every website in the world to expose accurate rate cards to its chatbots to facilitate agentic AI. Google is also incapable of preventing "search engine optimization" companies from tricking it into showing bullshit at the top of the results for common queries:
Google somehow thinks that the companies that spend millions of dollars trying to trick its crawler won't also spend millions of dollars trying to trick its chatbot β and they're providing the internet with a tool to inject lies straight into the chatbot's input hopper.
But UCP isn't just a way for companies to tell Google what their prices are. As Stoller points out, UCP will also sell merchants the ability to have Gemini set prices on their products, using Google's surveillance data, through "dynamic pricing" (another euphemism for "surveillance pricing").
This decade has seen the rise and rise of price "clearinghouses" β companies that offer price "consulting" to direct competitors in a market. Nominally, this is just a case of two competitors shopping with the same supplier β like Procter and Gamble and Unilever buying their high-fructose corn-syrup from the same company.
But it's actually far more sinister. "Clearinghouses" like Realpage β a company that "advises" landlords on rental rates β allow all the major competitors in a market to collude to raise prices in lockstep. A Realpage landlord that ignores the service's "advice" and gives a tenant a break on the rent will be excluded from Realpage's service. The rental markets that Realpage dominates have seen major increases in rental rates:
Google's "direct pricing" offering will allow all comers to have Google set their prices for them, based on Google's surveillance data. That includes direct competitors. As Stoller points out, both Nike and Reebok are Google advertisers. If they let Google price their sneakers, Google can raise prices across the market in lockstep.
Despite how much everyone hates this garbage, neoclassical economists and their apologists in the legal profession continue to insist that surveillance pricing is "efficient." Stoller points to a law review article called "Antitrust After the Coming Wave," written by antitrust law prof and Google lawyer Daniel Crane:
Crane argues that AI will kill antitrust law because AI favors monopolies, and argues "that we should forget about promoting competition or costs, and instead enact a new Soviet-style regime, one in which the government would merely direct a monopolistβs 'AI to maximize social welfare and allocate the surplus created among different stakeholders of the firm.'"
This is a planned economy, but it's one in which the planning is done by monopolists who are β somehow, implausibly β so biddable that governments can delegate the power to decide what we can buy and sell, what we can afford and who can afford it, and rein them in if they get it wrong.
In 1890, Senator John Sherman was stumping for the Sherman Act, America's first antitrust law. On the Senate floor, he declared:
If we will not endure a King as a political power we should not endure a King over the production, transportation, and sale of the necessaries of life. If we would not submit to an emperor we should not submit to an autocrat of trade with power to prevent competition and to fix the price of any commodity.
Google thinks that it has finally found a profitable use for AI. It thinks that it will be the first company to make money on AI, by harnessing that AI to a market-rigging, price-gouging monopoly that turns Google's software into Sherman's "autocrat of trade."
It's funny when you think of all those "AI safety" bros who claimed that AI's greatest danger was that it would become sentient and devour us. It turns out that the real "AI safety" risk is that AI will automate price gouging at scale, allowing Google to crown itself a "King over the necessaries of life":
If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:
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Instacart reaches into your pocket and lops a third off your dollars
I'm at the end of my tour for my new book, the international bestseller Enshittification. My last two stops are CCC in Hamburg, Dec 27-30 and the Tattered Cover in Denver (Jan 22). Hope to see you!
There's a whole greedflation-denial cottage industry that insists that rising prices are either the result of unknowable, untameable and mysterious economic forces, or they're the result of workers having too much money and too many jobs.
The one thing we're absolutely not allowed to talk about is the fact that CEOs keep going on earnings calls to announce that they are hiking prices way ahead of any increase in their costs, and blaming inflation:
Nor are we supposed to notice the "price consultancies" that let the dominant firms in many sectors β from potatoes to meat to rental housing β fix prices in illegal collusive arrangements that are figleafed by the tissue-thin excuse that "if you use an app to fix prices, it's not a crime":
And we're especially not supposed to notice the proliferation of "personalized pricing" businesses that use surveillance data to figure out how desperate you are and charge you a premium based on that desperation:
Surveillance pricing β when you are charged more for the same goods than someone else, based on surveillance data about the urgency of your need and the cash in your bank account β is a way for companies to reach into your pocket and devalue the dollars in your wallet. After all, if you pay $2 for something that I pay $1 for, that's just the company saying that your dollars are only worth half as much as mine:
The economy is riddled with surveillance pricing gouging. You are almost certainly paying more than your neighbors for various items, based on algorithmic price-setting, every day. Case in point: More Perfect Union and Groundwork Collaborative teamed up with Consumer Reports to recruit 437 volunteers from across America to login to Instacart at the same time and buy the same items from 15 stores, and found evidence of surveillance pricing at Albertsons, Costco, Kroger, and Sprouts Farmers Market:
The price-swings are wild. Some test subjects are being charged 23% more than others. The average variance for "the exact same items, from the exact same locations, at the exact same time" comes out to 7%, or "$1,200 per year for groceries" for a family of four.
The process by which your greedflation premium is assigned is opaque. The researchers found that Instacart shoppers ordering from Target clustered into seven groups, but it's not clear how Instacart decides how much extra to charge any given shopper.
Instacart β who acquired Eversight, a surveillance pricing company, in 2022 β blamed the merchants (who, in turn, blamed Instacart). Instacart also claimed that they didn't use surveillance data to price goods, but hedged, admitting that the consumer packaged goods duopoly of Unilever and Procter & Gamble do use surveillance data in connection with their pricing strategies.
Finally, Instacart claimed that this was all an "experiment" to "learn what matters most to consumers and how to keep essential items affordable." In other words, they were secretly charging you more (for things like eggs and bread) because somehow that lets them "keep essential items affordable."
Instacart said their goal was to help "retail partners understand consumer preferences and identify categories where they should invest in lower prices."
Anyone who's done online analytics can easily pierce this obfuscation, but for those of you who haven't had the misfortune of directing an iterated, A/B tested optimization effort, I'll unpack this statement.
Say you have a pool of users and a bunch of variations on a headline. You randomly assign different variants to different users and measure clickthroughs. Then you check to see which variants performed best, and dig into the data you have on those users to see if there are any correlations that tie together users who liked a given approach.
This might let you discover that, say, women over 40 click more often on headlines that mention kittens. Then you generate more variations based on these conclusions β different ways of mentioning kittens β and see which of these variations perform best, and whether the targeted group of users split into smaller subgroups (women over 40 in the midwest prefer "tabby kitten" while their southern sisters prefer "kitten" without a mention of breed).
By repeatedly iterating over these steps, you can come up with many highly refined variants, and you can use surveillance data to target them to ever narrower, more optimized slices of your user-base.
Obviously, this is very labor intensive. You have to do a lot of tedious analysis, and generate a lot of variants. This is one of the reasons that slopvertising is so exciting to the worst people on earth: they imagine that they can use AI to create a self-licking ice-cream cone, performing the analysis and generating endless new variations, all untouched by human hands.
But when it comes to prices, it's much easier to produce variants β all you're doing is adding or subtracting from the price you show to shoppers. You don't need to get the writing team together to come up with new ways of mentioning kittens in a headline β you can just raise the price from $6.23 to $6.45 and see if midwestern women over 40 balk or add the item to their shopping baskets.
And here's the kicker: you don't need to select by gender, racial or economic criteria to end up with a super-racist and exploitative arrangement. That's because race, gender and socioeconomic status have broad correlates that are easily discoverable through automated means.
For example, thanks to generations of redlining, discriminatory housing policy, wage discrimination and environmental racism, the poorest, sickest neighborhoods in the country are also the most racialized and are also most likely to be "food deserts" where you can't just go to the grocery store and shop for your family.
What's more, the private equity-backed dollar store duopoly have waged a decades-long war on community grocery stores, enveloping them with dollar stores that use their access to preferential discounts (from companies like Unilever and Procter & Gamble, another duopoly) to force grocers out of business:
Then these dollar stores run a greedflation scam that is so primitive, it's almost laughable: they just charge customers much higher amounts than the prices shown on the shelves and price-tags:
When you live in a food desert where your only store is a Dollar General that defrauds you at the cash-register, you are more likely to accept a higher price from Instacart, because you have fewer choices than someone in a middle-class neighborhood with two or three competing grocers. And the people who live in those food deserts are more likely to be poor, which, in America, is an excellent predictor of whether they are Black or brown.
Which is to say, without ever saying, "Charge Black people more for groceries," Instacart can easily A/B split its way into a system where they predictably and reliably charges Black people more for groceries. That's the old cod-Marxism at work: "from each according to their desperation."
This is so well-understood that anyone who sets one of these systems in motion should be understood to be deliberately seeking to do racist profiteering under cover of an algorithm. It's empiricism-washing: "I'm not racist, I just did some math" (that produced a predictably racist outcome):
This is the dark side and true meaning of "business optimization." The optimal business pays its suppliers and workers nothing, and charges its customers everything it can. Obviously, businesses need to settle for suboptimal outcomes, because workers won't show up if they don't get paid, and customers won't buy things that cost everything they haveβΉ.
βΉ Unless, of course, you are an academic publisher, in which case this is just how you do business.
A business "optimizes" its workforce by finding ways to get them to accept lower wages. For example, they can bind their workers with noncompete "agreements" that ban Wendy's cashiers from quitting their job and making $0.25 more per hour at the McDonald's next door (one in 18 American workers have been locked into one of these contracts):
Or they can lock their workers in with "training repayment agreement provisions" (TRAPs) β contractual clauses that force workers to pay their bosses thousands of dollars if they quit or get fired:
But the most insidious form of worker optimization is "algorithmic wage discrimination." That's when a company uses surveillance data to lower the wages of workers. For example, contract nurses are paid less if the app that hires them discovers (through the unregulated data-broker sector) that they have a lot of credit-card debt. After all, nurses who are heavily indebted can't afford to be choosy and turn down lowball offers:
This is the other form of surveillance pricing: pricing labor based on surveillance data. It's more cod-Marxism: "From each according to their desperation."
Forget "becoming ungovernable": to defeat these evil fuckers, we have to become unoptimizable:
How do we do that? Well, nearly every form of "optimization" begins with surveillance. They can't figure out whether they can charge you more if they can't spy on you. They can't figure out whether they can pay you less if they can't spy on you, either.
And the reason they can spy on you is because we let them. The last consumer privacy law to pass out of Congress was a 1988 bill that bans video-store clerks from disclosing your VHS rental history. Every other form of consumer surveillance is permitted under US federal law.
So step one of this process is to ban commercial surveillance. Banning algorithmic price discrimination is all well and good, but it is, ultimately, a form of redistribution. We're trying to make the companies share some of the excess they extract from our surveillance data. But predistribution β ending surveillance itself, in this case β is always far more effective than redistribution:
How do we do that? Well, we need to build a coalition. At the Electronic Frontier Foundation, we call this "privacy first": you can't solve all the internet's problems by fixing privacy, but you won't fix most of them unless we get privacy right, and so the (potential) coalition for a strong privacy regime is large and powerful:
But of course, "privacy first," doesn't mean "just privacy." We also need tools that target algorithmic pricing per se. In New York State, there's a new law that requires disclosure of algorithmic pricing, in the form of a prominent notification reading, "THIS PRICE WAS SET BY AN ALGORITHM USING YOUR PERSONAL DATA."
This is extremely weaksauce, and might even be worse than nothing. In California we have Prop 65, a rule that requires businesses to post signs and add labels any time they expose you to chemicals "known to the state of California to cause cancer." This caveat emptor approach (warn people, let them vote with their wallets) has led to every corner of California's built environment to be festooned with these warnings. Today, Californians just ignore these warnings, the same way that web users ignore the "privacy policy" disclosures on the sites they visit:
The right approach isn't to (merely) warn people about carcinogens (or privacy risks). The right approach is regulating harmful business practices, whether those practices give you a tumor or pick your pocket.
Under Biden, former FTC chair Lina Khan undertook proceedings to ban algorithmic pricing altogether. Trump's FTC killed that, along with all the other quality-of-life enhancing measures the FTC had in train (Trump's FTC chair replaced these with a program to root out "wokeness" in the agency).
Today, Khan is co-chair of Zohran Mamdani's transition team, and she will use the mayor's authority (under the New York City Consumer Protection Law of 1969, which addresses "unconscionable" commercial practices) to ban algorithmic pricing in NYC:
Khan wasn't Biden's only de-optimizer. Under chair Rohit Chopra, Biden's Consumer Finance Protection Bureau actually banned the data-brokers who power surveillance pricing:
These are efforts to optimize corporations for human thriving, by making them charge us less and pay us more. For while we are best off when we are unoptimizable, we are also best off when corporations are totally optimized β for our benefit.
If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:
Surveillance pricing lets corporations decide what your dollar is worth
I'm in the home stretch of my 24-city book tour for my new novel PICKS AND SHOVELS. Catch me in LONDON (July 1) with TRASHFUTURE'S RILEY QUINN and then a big finish in MANCHESTER on July 2.
Economists praise "price discrimination" as "efficient." That's when a company charges different customers different amounts based on inferences about their willingness to pay. But when a company sells you something for $2 that someone else can buy for $1, they're revaluing the dollars in your pocket at half the rate of the other guy's.
That's not how economists see it, of course. When a hotel sells you a room for $50 that someone else might get charged $500 for, that's efficient, provided that the hotelier is sure no $500 customers are likely to show up after you check in. The empty room makes them nothing, and $50 is more than nothing. There's a kind of metaphysics at work here, in which the room that is for sale at $500 is "a hotel room you book two weeks in advance and are sure will be waiting for you when you check in" while the $50 room is "a hotel room you can only get at the last minute, and if it's not available, you're sleeping in a chair at the Greyhound station."
But what if you show up at the hotel at 9pm and the hotelier can ask a credit bureau how much you can afford to pay for the room? What if they can find out that you're in chemotherapy, so you don't have the stamina to shop around for a cheaper room? What if they can tell that you have a 5AM flight and need to get to bed right now? What if they charge you more because they can see that your kids are exhausted and cranky and the hotel infers that you'll pay more to get the kids tucked into bed? What if they charge you more because there's a wildfire and there are plenty of other people who want the room?
The metaphysics of "room you booked two weeks ago" as a different product from "room you're trying to book right now" break down pretty quickly once you factor in the ability of sellers to figure out how desperate you are β or merely how distracted you are β and charge accordingly. "Surveillance pricing" is the practice of spying on you to figure out how much you're willing to spend β because you're wealthy, because you're desperate, because you're distracted, because it's payday β and charging you more:
For example, a McDonald's ventures portfolio company called Plexure offers drive-through restaurants the ability to raise the price of your regular order based on whether you've recently received your paycheck. They're just one of many "personalized pricing" companies that have attracted investor capital to figure out how to charge you more for the things you need, or merely for the small pleasures of life.
Personalized pricing (that is, "surveillance pricing") is part of the "pricing revolution" that is underway in the US and the world today. Another major element of this revolution are the "price clearinghouses" that charge firms within a sector to submit their prices to them, then "offer advice" on the optimum pricing. This advice β given to all the suppliers of a good or service β inevitably boils down to "everyone should raise their prices in unison." So long as everyone follows that advice, we poor suckers have nowhere else to go to get a better deal.
This is a pretty thin pretext. Price-fixing is illegal, after all. These companies pretend that when all the meat-packers in America send their pricing data to a "neutral" body like Agri-Stats, which then tells them all to jack up the price of meat, that this isn't a price-fixing conspiracy, since the actual conspiracy takes the form of strongly worded suggestions from an entity that isn't formally part of the industry:
Same goes for when all the landlords in town send their rental data to a company like Realpage, which then offers "advice" about the optimum price, along with stern warnings not to rent below that price: apparently that's not price-fixing either:
It's not just sellers who engage in this kind of price-fixing β it's also buyers. Specifically buyers of labor, AKA "bosses." Take contract nursing, where a cartel of three staffing apps have displaced the many small regional staffing agencies that historically served the sector. These companies buy nurses' credit history from the unregulated, Wild West data-brokerage sector. They're checking to see whether a nurse who's looking for a shift has a lot of credit-card debt, especially delinquent debt, because these nurses are facing economic hardship and will accept a lower wage than their better-off compatriots:
This is surveillance pricing for buyers, and as with the sell-side pricing revolution, buyers also make use of a third party as an accountability sink (a term coined by Dan Davies): the apps that they use to buy nursing labor are a convenient way for hospitals to pretend that they're not engaged in price-fixing for labor.
Veena Dubal calls this "algorithmic wage discrimination." Algorithmic wage discrimination doesn't need to use third-party surveillance data: Uber, who invented the tactic, use their own in-house data as a way to make inferences about drivers' desperation and thus their willingness to accept a lower wage. Drivers who are less picky about which rides they accept are treated as more desperate, and offered lower wages than their pickier colleagues:
But this gets much creepier and more powerful when combined with aggregated surveillance data. This is one of the real labor consequences of AI: not the hypothetical millions of people who will become technologically unemployed, numbers that AI bosses pull out of their asses and hand to dutiful stenographers in the tech press who help them extol the power of their products; but rather the millions of people whose wages are suppressed by algorithms that continuously recalculate how desperate a worker is apt to be and lower their wages accordingly.
This is as good a candidate for AI regulation as any, but it's also a very good reason to regulate data brokers, who operate with total impunity. Thankfully, Biden's Consumer Finance Protection Bureau passed a rule that made data brokers effectively illegal:
But then Trump got elected and his despicable minions killed that rule, giving data brokers carte blanche to spy on you and sell your data, effectively without restriction:
Also, Biden's FTC was in the middle of an antitrust investigation into surveillance pricing on the eve of the election, a prelude to banning the practice in America:
But then Trump got elected and his despicable minions killed that investigation and instead created a snitch line where FTC employees could complain about colleagues who were "woke":
Naomi Klein's Doppelganger proposes a "mirror world" that the fever-swamp right lives in β a world where concern for children takes the form of Pizzagate conspiracies, while ignoring the starving babies in Gaza and the kids whose parents are being kidnapped by ICE:
The pricing revolution is a kind of mirror-world Marxism, grounded in "From each according to their ability to pay; to each according to their economic desperation":
A recent episode of the excellent Organized Money podcast featured an interview with Lee Hepner, an antitrust lawyer who is on the front lines of the pricing revolution (on the side of workers and buyers) (not bosses):
Hepner is the one who proposed the formulation that personalized pricing is a way for corporations to decide that your dollars are worth less than your neighbors' dollars β a form of economic discrimination that treats the poorest, most desperate, and most precarious among us as the people who should pay the most, because we are the people whose dollars are worth the least.
Now, this isn't always true. Earlier this month, Delta, United and American were caught charging more for single travelers than they charged pairs of groups:
That's a way to charge business travelers extra β for valuing their dollars less than the dollars of families, not because business travelers are desperate, but because they are, on average, richer than holidaymakers (because their bosses are presumed to be buying their tickets). Sometimes, price discrimination really does charge richer people more to subsidize everyone else.
But here's the difference: when the news about the business-traveler's premium broke, its victims β powerful people with social capital and also regular capital β rose up in outrage, and the airlines reversed the policy:
If the airlines are still pursuing this kind of price discrimination, they'll do something sneakier, like buying our credit histories before showing us a price. This is something British Airways is already teeing up, by offering essentially zero reward miles to frequent travelers for partner airline tickets unless they're purchased from BA's own website:
But BA operates in the UK, where most of the pre-Brexit, EU-based privacy regime is still intact, despite the best efforts of Keir Starmer to destroy it, something that neither Boris Johnson, nor Theresa May,nor Rishi Sunak, nor Liz Truss could manage:
So for now, BA travelers might be safe from surveillance pricing, at least in the UK and EU. And that's the thing, America is pretty much cooked. It might be generations β centuries β before the USA emerges from its Trumpian decline and becomes a civilized democracy again. Americans have little hope of a future in which their government protects them from corporate predators, rather than serving them up on a toothpick, along with a little cocktail napkin.
The future of the fight against corporate power and oligarchy is something for the rest of the world to carry on, as the American hermit kingdom sinks into ever-deeper collapse:
And as it happens, Canada's Competition Bureau, newly equipped with muscular enforcement powers thanks to a 2024 law, is seeking public comment on surveillance pricing and whether Canada should do something about it:
I'm writing comments for this one. If you're in Canada, or a Canadian abroad (like me), perhaps you could, too. If you're looking for an excellent Canadian perspective to crib from, check out this episode of The Globe and Mail's Lately podcast on the subject:
Just because America jumped off the Empire State Building, that's no reason for Canada to jump off the CN Tower, after all.
(Eh?)
If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:
Picks and Shovels is a new, standalone technothriller starring Marty Hench, my two-fisted, hard-fighting, tech-scam-busting forensic accountant. You can pre-order it on my latest Kickstarter, which features a brilliant audiobook read by Wil Wheaton.
The social function of the economics profession is to explain, over and over again, that your boss is actually right and that you don't really want the things you want, and you're secretly happy to be abused by the system. If that wasn't true, why would your "choose" commercial surveillance, abusive workplaces and other depredations?
In other words, economics is the "look what you made me do" stick that capitalism uses to beat us with. We wouldn't spy on you, rip you off or steal your wages if you didn't choose to use the internet, shop with monopolists, or work for a shitty giant company. The technical name for this ideology is "public choice theory":
Of all the terrible things that economists say we all secretly love, one of the worst is "price discrimination." This is the idea that different customers get charged different amounts based on the merchant's estimation of their ability to pay. Economists insist that this is "efficient" and makes us all better off. After all, the marginal cost of filling the last empty seat on the plane is negligible, so why not sell that seat for peanuts to a flier who doesn't mind the uncertainty of knowing whether they'll get a seat at all? That way, the airline gets extra profits, and they split those profits with their customers by lowering prices for everyone. What's not to like?
Plenty, as it turns out. With only four giant airlines who've carved up the country so they rarely compete on most routes, why would an airline use their extra profits to lower prices, rather than, say, increasing their dividends and executive bonuses?
For decades, the airline industry was the standard-bearer for price discrimination. It was basically impossible to know how much a plane ticket would cost before booking it. But even so, airlines were stuck with comparatively crude heuristics to adjust their prices, like raising the price of a ticket that didn't include a Saturday stay, on the assumption that this was a business flyer whose employer was footing the bill:
With digitization and mass commercial surveillance, we've gone from pricing based on context (e.g. are you buying your ticket well in advance, or at the last minute?) to pricing based on spying. Digital back-ends allow vendors to ingest massive troves of commercial surveillance data from the unregulated data-broker industry to calculate how desperate you are, and how much money you have. Then, digital front-ends β like websites and apps β allow vendors to adjust prices in realtime based on that data, repricing goods for every buyer.
As digital front-ends move into the real world (say, with digital e-ink shelf-tags in grocery stores), vendors can use surveillance data to reprice goods for ever-larger groups of customers and types of merchandise. Grocers with e-ink shelf tags reprice their goods thousands of times, every day:
Here's where an economist will tell you that actually, your boss is right. Many groceries are perishable, after all, and e-ink shelf tags allow grocers to reprice their goods every minute or two, so yesterday's lettuce can be discounted every fifteen minutes through the day. Some customers will happily accept a lettuce that's a little gross and liztruss if it means a discount. Those customers get a discount, the lettuce isn't thrown out at the end of the day, and everyone wins, right?
Well, sure, if. If the grocer isn't part of a heavily consolidated industry where competition is a distant memory and where grocers routinely collude to fix prices. If the grocer doesn't have to worry about competitors, why would they use e-ink tags to lower prices, rather than to gouge on prices when demand surges, or based on time of day (e.g. making frozen pizzas 10% more expensive from 6-8PM)?
And unfortunately, groceries are one of the most consolidated sectors in the modern world. What's more, grocers keep getting busted for colluding to fix prices and rip off shoppers:
Surveillance pricing is especially pernicious when it comes to apps, which allow vendors to reprice goods based not just on commercially available data, but also on data collected by your pocket distraction rectangle, which you carry everywhere, do everything with, and make privy to all your secrets. Worse, since apps are a closed platform, app makers can invoke IP law to criminalize anyone who reverse-engineers them to figure out how they're ripping you off. Removing the encryption from an app is a potential felony punishable by a five-year prison sentence and a $500k fine (an app is just a web-page skinned in enough IP to make it a crime to install a privacy blocker on it):
Large vendors love to sell you shit via their apps. With an app, a merchant can undetectably change its prices every few seconds, based on its estimation of your desperation. Uber pioneered this when they tweaked the app to raise the price of a taxi journey for customers whose batteries were almost dead. Today, everyone's getting in on the act. McDonald's has invested in a company called Plexure that pitches merchants on the use case of raising the cost of your normal breakfast burrito by a dollar on the day you get paid:
Surveillance pricing isn't just a matter of ripping off customers, it's also a way to rip off workers. Gig work platforms use surveillance pricing to titrate their wage offers based on data they buy from data brokers and scoop up with their apps. Veena Dubal calls this "algorithmic wage discrimination":
Take nurses: increasingly, American hospitals are firing their waged nurses and replacing them with gig nurses who are booked in via an app. There's plenty of ways that these apps abuse nurses, but the most ghastly is in how they price nurses' wages. These apps buy nurses' financial data from data-brokers so they can offer lower wages to nurses with lots of credit card debt, on the grounds that crushing debt makes nurses desperate enough to accept a lower wage:
This week, the excellent Lately podcast has an episode on price discrimination, in which cohost Vass Bednar valiantly tries to give economists their due by presenting the strongest possible case for charging different prices to different customers:
Bednar really tries, but β as she later agrees β this just isn't a very good argument. In fact, the only way charging different prices to different customers β or offering different wages to different workers β makes sense is if you're living in a socialist utopia.
After all, a core tenet of Marxism is "from each according to his ability, to each according to his needs." In a just society, people who need more get more, and people who have less, pay less:
Price discrimination, then, is a Bizarro-world flavor of cod-Marxism. Rather than having a democratically accountable state that sets wages and prices based on need and ability, price discrimination gives this authority to large firms with pricing power, no regulatory constraints, and unlimited access to surveillance data. You couldn't ask for a neater example of the maxim that "What matters isn't what technology does. What matters is who it does it for; and who it does it to."
Neoclassical economists say that all of this can be taken care of by the self-correcting nature of markets. Just give consumers and workers "perfect information" about all the offers being made for their labor or their business, and things will sort themselves out. In the idealized models of perfectly spherical cows of uniform density moving about on a frictionless surface, this does work out very well:
But while large companies can buy the most intimate information imaginable about your life and finances, IP law lets them capture the state and use it to shut down any attempts you make to discover how they operate. When an app called Para offered Doordash workers the ability to preview the total wage offered for a job before they accepted it, Doordash threatened them with eye-watering legal penalties, then threw dozens of full-time engineers at them, changing the app several times per day to shut out Para:
And when an Austrian hacker called Mario Zechner built a tool to scrape online grocery store prices β discovering clear evidence of price-fixing conspiracies in the process β he was attacked by the grocery cartel for violating their "IP rights":
Conservatism consists of exactly one proposition, to wit: There must be in-groups whom the law protects but does not bind, alongside out-groups whom the law binds but does not protect.
Of course, there wouldn't be any surveillance pricing without surveillance. When it comes to consumer privacy, America is a no-man's land. The last time Congress passed a new consumer privacy law was in 1988, when they enacted the Video Privacy Protection Act, which bans video-store clerks from revealing which VHS cassettes you take home. Congress has not addressed a single consumer privacy threat since Die Hard was still playing in theaters.
Corporate bullies adore a regulatory vacuum. The sleazy data-broker industry that has festered and thrived in the absence of a modern federal consumer privacy law is absolutely shameless. For example, every time an app shows you an ad, your location is revealed to dozens of data-brokers who pretend to be bidding for the right to show you an ad. They store these location data-points and combine them with other data about you, which they sell to anyone with a credit card, including stalkers, corporate spies, foreign governments, and anyone hoping to reprice their offerings on the basis of your desperation:
Under Biden, the outgoing FTC did incredible work to fill this gap, using its authority under Section 5 of the Federal Trade Commission Act (which outlaws "unfair and deceptive" practices) to plug some of the worst gaps in consumer privacy law:
But now the burden of enforcing these rules falls to Trump's FTC, whose new chairman has vowed to end the former FTC's "war on business." What America desperately needs is a new privacy law, one that has a private right of action (so that individuals and activist groups can sue without waiting for a public enforcer to take up their causes) and no "pre-emption" (so that states can pass even stronger privacy laws):
How will we get that law? Through a coalition. After all, surveillance pricing is just one of the many horrors that Americans have to put up with thanks to America's privacy law gap. The "privacy first" theory goes like this: if you're worried about social media's impact on teens, or women, or old people, you should start by demanding a privacy law. If you're worried about deepfake porn, you should start by demanding a privacy law. If you're worried about algorithmic discrimination in hiring, lending, or housing, you should start by demanding a privacy law. If you're worried about surveillance pricing, you should start by demanding a privacy law. Privacy law won't entirely solve all these problems, but none of them would be nearly as bad if Congress would just get off its ass and catch up with the privacy threats of the 21st century. What's more, the coalition of everyone who's worried about all the harms that arise from commercial surveillance is so large and powerful that we can get Congress to act:
Economists, meanwhile, will line up to say that this is all unnecessary. After all, you "sold" your privacy when you clicked "I agree" or walked under a sign warning you that facial recognition was in use in this store. The market has figured out what you value privacy at, and it turns out, that value is nothing. Any kind of privacy law is just a paternalistic incursion on your "freedom to contract" and decide to sell your personal information. It is "market distorting."
In other words, your boss is right.
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If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:
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Correction, 7 June 2024: The initial version of this article erroneously described Jeffrey Roper as the founder of ATPCO. He benefited from ATPCO, but did not co-found it. The initial version of this article called ATPCO "an illegal airline price-fixing service"; while ATPCO provides information that the airlines use to set prices, it does not set prices itself, and while the DOJ investigated the company, they did not pursue a judgment declaring the service to be illegal. I regret the error.
Noted anti-capitalist agitator Adam Smith had it right: "People of the same trade seldom meet together, even for merriment and diversion, but the conversation ends in a conspiracy against the public, or in some contrivance to raise prices."
Despite being a raving commie loon, Smith's observation was so undeniably true that regulators, policymakers, and economists couldn't help but acknowledge that it was true. The trustbusting era was defined by this idea: if we let the number of companies in a sector get too small, or if we let one or a few companies get too big, they'll eventually start to rig prices.
What's more, once an industry contracts corporate gigantism, it will become too big to jail, able to outspend and overpower the regulators charged with reining in its cheating. Anyone who believes Smith's self-evident maxim had to accept its conclusion: that companies had to be kept smaller than the state that regulated them. This wasn't about "punishing bigness" β it was the necessary precondition for a functioning market economy.
We kept companies small for the same reason that we limited the height of skyscrapers: not because we opposed height, or failed to appreciate the value of a really good penthouse view β rather, to keep the building from falling over and wrecking all the adjacent buildings and the lives of the people inside them.
Starting in the neoliberal era β Carter, then Reagan β we changed our tune. We liked big business. A business that got big was doing something right. It was perverse to shut down our best companies. Instead, we'd simply ban big companies from rigging prices. This was called the "consumer welfare" theory of antitrust. It was a total failure.
40 years later, nearly every industry is dominated by a handful of companies, and these companies price-gouge us with abandon. Worse, they use their gigantic ripoff winnings to fill war-chests that fund the corruption of democracy, capturing regulators so that they can rip us off even more, while ignoring labor, privacy and environmental law and ducking taxes.
It turns out that keeping gigantic, opaque, complex corporations honest is really hard. They have so many ways to shuffle money around that it's nearly impossible to figure out what they're doing. Digitalization makes things a million times worse, because computers allow businesses to alter their processes so they operate differently for every customer, and even for every interaction.
This is Dieselgate times a billion: VW rigged its cars to detect when they were undergoing emissions testing and switch to a less polluting, more compliant mode. But when they were on the open road, they spewed lethal quantities of toxic gas, killing people by the thousands. Computers don't make corporate leaders more evil, but they let evil corporate leaders execute far more complex and nefarious plans. Digitalization is a corporate moral hazard, making it just too easy and tempting to rig the game.
That's why Toyota, the largest car-maker in the world, just did Dieselgate again, more than a decade later. Digitalization is a temptation no giant company can resist:
https://www.bbc.com/news/articles/c1wwj1p2wdyo
For forty years, pro-monopoly cheerleaders insisted that we could allow companies to grow to unimaginable scale and still prevent cheating. They passed rules banning companies from explicitly forming agreements to rig prices. About ten seconds later, new middlemen popped up offering "information brokerages" that helped companies rig prices without talking to one another.
Take Agri Stats: the country's hyperconcentrated meatpacking industry pays Agri Stats to "consult on prices." They provide Agri Stats with a list of their prices, and then Agri Stats suggests changes based on its analysis. What does that analysis consist of? Comparing the company's prices to its competitors, who are also Agri Stats customers:
In other words, Agri Stats finds the highest price for each product in the sector, then "advises" all the companies with lower prices to raise their prices to the "competitive" level, creating a one-way ratchet that sends the price of food higher and higher.
More and more sectors have an Agri Stats, and digitalization has made this price-gouging system faster, more efficient, and accessible to sectors with less concentration. Landlords, for example, have tapped into Realpage, a "data broker" that the same thing to your rent that Agri Stats does to meat prices. Realpage requires the landlords who sign up for its service to accept its "recommendations" on minimum rents, ensuring that prices only go up:
Writing for The American Prospect, Luke Goldstein lays out the many ways in which these digital intermediaries have supercharged the business of price-rigging:
Goldstein identifies a kind of patient zero for this ripoff epidemic: Jeffrey Roper, a former Alaska Air exec who benefited from a service that helps airlines set prices. ATPCO was investigated by the DOJ in the 1990s, but the enforcers lost their nerve and settled with the company, which agreed to apply some ornamental fig-leafs to its collusion-machine. Even those cosmetic changes were seemingly a bridge too far Roper, who left the US.
But he came back to serve as Realpage's "principal scientist" β the architect of a nationwide scheme to make rental housing vastly more expensive. For Roper, the barrier to low rents was empathy: landlords felt stirrings of shame when they made shelter unaffordable to working people. Roper called these people "idiots" who sentimentality "costs the whole system."
Sticking a rent-gouging computer between landlords and the people whose lives they ruin is a classic "accountability sink," as described in Dan Davies' new book "The Unaccountability Machine: Why Big Systems Make Terrible Decisions β and How The World Lost its Mind":
It's a form of "empiricism washing": if computers are working in the abstract realm of pure numbers, they're just moving the objective facts of the quantitative realm into the squishy, imperfect qualitative world. Davies' interview on Trashfuture is excellent:
To rig prices, an industry has to solve three problems: the problem of coming to an agreement to fix prices (economists call this "the collective action problem"); the problem of coming up with a price; and the problem of actually changing prices from moment to moment. This is the ripoff triangle, and like a triangle, it has many stable configurations.
The more concentrated an industry is, the easier it is to decide to rig prices. But if the industry has the benefit of digitalization, it can swap the flexibility and speed of computers for the low collective action costs from concentration. For example, grocers that switch to e-ink shelf tags can make instantaneous price-changes, meaning that every price change is less consequential β if sales fall off after a price-hike, the company can lower them again at the press of a button. That means they can collude less explicitly but still raise prices:
My name for this digital flexibility is "twiddling." Businesses with digital back-ends can alter their "business logic" from second to second, and present different prices, payouts, rankings and other key parts of the deal to every supplier or customer they interact with:
https://pluralistic.net/2023/02/19/twiddler/
Not only does twiddling make it easier to rip off suppliers, workers and customers, it also makes these crimes harder to detect. Twiddling made Dieselgate possible, and it also underpinned "Greyball," Uber's secret strategy of refusing to send cars to pick up transportation regulators who would then be able to see firsthand how many laws the company was violating:
Twiddling is so easy that it has brought price-fixing to smaller companies and less concentrated sectors, though the biggest companies still commit crimes on a scale that put these bit-players to shame. In The Prospect, David Dayen investigates the "personalized pricing" ripoff that has turned every transaction into a potential crime-scene:
"Personalized pricing" is the idea that everything you buy should be priced based on analysis of commercial surveillance data that predicts the maximum amount you are willing to pay.
Proponents of this idea β like Harvard's Pricing Lab with its "Billion Prices Project" β insist that this isn't a way to rip you off. Instead, it lets companies lower prices for people who have less ability to pay:
https://thebillionpricesproject.com/
This kind of weaponized credulity is totally on-brand for the pro-monopoly revolution. It's the same wishful thinking that led regulators to encourage monopolies while insisting that it would be possible to prevent "bad" monopolies from raising prices. And, as with monopolies, "personalized pricing" leads to an overall increase in prices. In econspeak, it is a "transfer of wealth from consumer to the seller."
"Personalized pricing" is one of those cuddly euphemisms that should make the hair on the back of your neck stand up. A more apt name for this practice is surveillance pricing, because the "personalization" depends on the vast underground empire of nonconsensual data-harvesting, a gnarly hairball of ad-tech companies, data-brokers, and digital devices with built-in surveillance, from smart speakers to cars:
Much of this surveillance would be impractical, because no one wants their car, printer, speaker, watch, phone, or insulin-pump to spy on them. The flexibility of digital computers means that users always have the technical ability to change how these gadgets work, so they no longer spy on their users. But an explosion of IP law has made this kind of modification illegal:
https://locusmag.com/2020/09/cory-doctorow-ip/
This is why apps are ground zero for surveillance pricing. The web is an open platform, and web-browsers are legal to modify. The majority of web users have installed ad-blockers that interfere with the surveillance that makes surveillance pricing possible:
But apps are a closed platform, and reverse-engineering and modifying an app is a literal felony β several felonies, in fact. An app is just a web-page skinned with enough IP to make it a felony to modify it to protect your consumer, privacy or labor rights:
(Google is leading a charge to turn the web into the kind of enshittifier's paradise that apps represent, blocking the use of privacy plugins and proposing changes to browser architecture that would allow them to felonize modifying a browser without permission:)
Apps are a twiddler's playground. Not only can they "customize" every interaction you have with them, but they can block you (or researchers seeking to help you) from recording and analyzing the app's activities. Worse: digital transactions are intimate, contained to the palm of your hand. The grocer whose e-ink shelf-tags flicker and reprice their offerings every few seconds can be collectively observed by people who are in the same place and can start a conversation about, say, whether to come back that night a throw a brick through the store's window to express their displeasure. A digital transaction is a lonely thing, atomized and intrinsically shielded from a public response.
That shielding is hugely important. The public hates surveillance pricing. Time and again, through all of American history, there have been massive and consequential revolts against the idea that every price should be different for every buyer. The Interstate Commerce Commission was founded after Grangers rose up against the rail companies' use of "personalized pricing" to gouge farmers.
Companies know this, which is why surveillance pricing happens in secret. Over and over, every day, you are being gouged through surveillance pricing. The sellers you interact with won't tell you about it, so to root out this practice, we have to look at the B2B sales-pitches from the companies that sell twiddling tools.
One of these companies is Plexure, partly owned by McDonald's, which provides the surveillance-pricing back-ends for McD's, Ikea, 7-Eleven, White Castle and others β basically, any time a company gives you a hard-sell to order via its apps rather than its storefronts or its website, you should assume you're getting twiddled, hard.
These companies use the enshittification playbook to trap you into using their apps. First, they offer discounts to customers who order through their apps β then, once the customers are fully committed to shopping via app, they introduce surveillance pricing and start to jack up the prices.
For example, Plexure boasts that it can predict what day a given customer is getting paid on and use that information to raise prices on all the goods the customer shops for on that day, on the assumption that you're willing to pay more when you've got a healthy bank balance.
The surveillance pricing industry represents another reason for everything you use to spy on you β any data your "smart" TV or Nest thermostat or Ring doorbell can steal from you can be readily monetized β just sell it to a surveillance pricing company, which will use it to figure out how to charge you more for everything you buy, from rent to Happy Meals.
But the vast market for surveillance data is also a potential weakness for the industry. Put frankly: the commercial surveillance industry has a lot of enemies. The only thing it has going for it is that so many of these enemies don't know that what's they're really upset about is surveillance.
Some people are upset because they think Facebook made Grampy into a Qanon. Others, because they think Insta gave their kid anorexia. Some think Tiktok is brainwashing millennials into quoting Osama bin Laden. Some are upset because the cops use Google location data to round up Black Lives Matter protesters, or Jan 6 insurrectionists. Some are angry about deepfake porn. Some are angry because Black people are targeted with ads for overpriced loans or colleges:
And some people are angry because surveillance feeds surveillance pricing. The thing is, whatever else all these people are angry about, they're all angry about surveillance. Are you angry that ad-tech is stealing a 51% share of news revenue? You're actually angry about surveillance. Are you angry that "AI" is being used to automatically reject resumes on racial, age or gender grounds? You're actually angry about surveillance.
There's a very useful analogy here to the history of the ecology movement. As James Boyle has long said, before the term "ecology" came along, there were people who cared about a lot of issues that seemed unconnected. You care about owls, I care about the ozone layer. What's the connection between charismatic nocturnal avians and the gaseous composition of the upper atmosphere? The term ecology took a thousand issues and welded them together into one movement.
That's what's on the horizon for privacy. The US hasn't had a new federal consumer privacy law since 1988, when Congress acted to ban video-store clerks from telling the newspapers what VHS cassettes you were renting:
We are desperately overdue for a new consumer privacy law, but every time this comes up, the pro-surveillance coalition defeats the effort. but as people who care about conspiratorialism, kids' mental health, spying by foreign adversaries, phishing and fraud, and surveillance pricing all come together, they will be an unbeatable coalition:
Not every federal agency has gotten the message, though. Trump's Fed Chairman, Jerome Powell β whom Biden kept on the job β has been hiking interest rates in a bid to reduce our purchasing power by making millions of Americans poorer and/or unemployed. He's doing this to fight inflation, on the theory that inflation is being cause by us being too well-off, and therefore trying to buy more goods than are for sale.
But of course, interest rates are inflationary: when interest rates go up, it gets more expensive to pay your credit card bills, lease your car, and pay a mortgage. And where we see the price of goods shooting up, there's abundant evidence that this is the result of greedflation β companies jacking up their prices and blaming inflation. Interest rate hawks say that greedflation is impossible: if one company raises its prices, its competitors will swoop in and steal their customers with lower prices.
Maybe they would do that β if they didn't have a toolbox full of algorithmic twiddling options and a deep trove of surveillance data that let them all raise prices together:
Someone needs to read some Adam Smith to Chairman Powell: "People of the same trade seldom meet together, even for merriment and diversion, but the conversation ends in a conspiracy against the public, or in some contrivance to raise prices."
If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:
Real-World Applications of AI Revenue Optimization in Hotels
Artificial intelligence in revenue management has moved from theoretical concept to practical reality, with hotels worldwide deploying AI to solve specific operational challenges and capture measurable revenue gains. These real-world applications demonstrate how AI transforms abstract data into actionable pricing strategies, optimizes inventory allocation, and personalizes rate offerings to maximize both occupancy and ADR across diverse market conditions.
One of the most impactful applications of AI Revenue Optimization involves managing complex group business alongside transient demand. Traditional revenue management often treats group blocks as static allocations, but AI enables dynamic group pricing that adjusts based on pickup pace, remaining inventory, and forecasted transient demand. InterContinental Hotels Group properties using AI for group management report improved displacement analysis, helping revenue managers make informed decisions about accepting group business versus holding rooms for higher-rated transient bookings.
Length-of-Stay and Package Optimization
AI excels at identifying optimal length-of-stay restrictions and package configurations that maximize total revenue. Rather than applying uniform minimum-stay requirements across all rate categories, AI analyzes historical booking patterns to determine when three-night minimums capture additional revenue versus when they simply drive guests to competitors. The technology also evaluates package component pricingβroom rate, F&B credits, spa treatments, parkingβto recommend bundling strategies that increase perceived value while maintaining healthy margins.
Resort properties have found particular success using AI to optimize seasonal pricing transitions. Instead of implementing abrupt rate changes when seasons shift, AI development platforms enable gradual pricing curves that capture maximum revenue as demand patterns evolve. This approach prevents the revenue losses that occur when properties move too quickly to off-season pricing or maintain peak-season rates too long into shoulder periods.
Personalized Rate Recommendations
Advanced AI applications integrate guest data from CRM systems to deliver personalized rate offers based on individual booking history, preferences, and predicted lifetime value. Loyal guests who consistently book direct and generate significant F&B revenue might receive preferential rates that maintain their booking patterns, while price-sensitive guests researching multiple OTA options receive competitive offers designed to capture their business before they book elsewhere. This micro-segmentation approach increases both direct booking conversion and total guest spend across the property.
AI also optimizes last-minute inventory management, a perennial challenge in revenue management. By analyzing real-time booking velocity, competitive rate shopping data, and local event calendars, AI systems recommend aggressive discounting when distressed inventory is unlikely to sell otherwise, while protecting rates when last-minute demand is likely to materialize. This nuanced approach to inventory management prevents both unsold rooms and premature discounting that destroys revenue.
Conclusion
These practical applications demonstrate that AI revenue optimization delivers measurable results across diverse operational scenarios. From group displacement analysis to personalized guest pricing, AI transforms how hotels make revenue decisions in increasingly complex market environments. Properties seeking to implement these capabilities should evaluate comprehensive technology solutions that integrate seamlessly with existing systems. A robust Hospitality AI Platform provides the foundation needed to deploy these advanced revenue optimization techniques at scale.
