Nor are we supposed to notice the "price consultancies" that let the dominant firms in many sectors - from potatoes to meat to rental housing - 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":

pluralistic.net/2025/01/25/pot…

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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:

pluralistic.net/2024/06/05/you…

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Surveillance pricing - 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 - 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:

pluralistic.net/2025/06/24/pri…

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It's a form of cod-Marxism: "from each according to their desperation":

pluralistic.net/2025/01/11/soc…

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.

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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 two items from 15 stores, and found evidence of surveillance pricing at Albertsons, Costco, Kroger, and Sprouts Farmers Market:

groundworkcollaborative.org/wo…

The price-swings are *wild*. Some test subjects are being charged *23%* more than others.

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Same Cart, Different Price: Instacart’s Price Experiments Cost Families at Checkout - Groundwork Collaborative

^{Groundwork Collaborative}

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.

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Instacart - who acquired Eversight, a surveillance pricing company, in 2022 - 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.

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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."

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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.

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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.

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Then you generate more variations based on these conclusions - different ways of mentioning kittens - 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).

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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.

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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 - all you're doing is adding or subtracting from the price you show to shoppers.

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You don't need to get the writing team together to come up with new ways of mentioning kittens in a headline - 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.

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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.

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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:

pluralistic.net/2023/03/27/wal…

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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:

consumeraffairs.com/news/do-al…

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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.

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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."

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This is so well-understood that anyone who sets this system 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):

reuters.com/article/world/insi…

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.

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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 have⹋.

⹋ Unless, of course, you are an academic publisher, in which case this is just how you do business.

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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):

pluralistic.net/2025/09/09/ger…

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Or they can lock their workers in with "training repayment agreement provisions" (TRAPs) - contractual clauses that force workers to pay their bosses thousands of dollars if they quit or get fired:

pluralistic.net/2022/08/04/its…

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.

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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:

pluralistic.net/2024/12/18/loo…

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."

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Forget "becoming ungovernable": to defeat these evil fuckers, we have to *become unoptimizable*:

pluralistic.net/2025/08/20/bil…

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.

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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.

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We're trying to make the companies share some of the excess they extract from our surveillance data. But *predistribution* - ending surveillance itself, in this case - is *always* far more effective than redistribution:

pluralistic.net/2025/10/31/los…

How do we do that? Well, we need to build a coalition.

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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:

pluralistic.net/2023/12/06/pri…

But of course, "privacy first," doesn't mean "just privacy." We *also* need tools that target algorithmic pricing *per se*.

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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."

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