#instacart

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. 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. 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:

https://pluralistic.net/2023/03/27/walmarts-jackals/#cheater-sizes

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:

https://www.consumeraffairs.com/news/do-all-those-low-dollar-store-prices-really-add-up-120325.html

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):

https://www.reuters.com/article/world/insight-amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK0AG/

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 have⹋.

⹋ 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):

https://pluralistic.net/2025/09/09/germanium-valley/#i-cant-quit-you

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:

https://pluralistic.net/2022/08/04/its-a-trap/#a-little-on-the-nose

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:

https://pluralistic.net/2024/12/18/loose-flapping-ends/#luigi-has-a-point

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:

https://pluralistic.net/2025/08/20/billionaireism/#surveillance-infantalism

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 – ending surveillance itself, in this case – is always far more effective than redistribution:

https://pluralistic.net/2025/10/31/losing-the-crypto-wars/#surveillance-monopolism

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:

https://pluralistic.net/2023/12/06/privacy-first/#but-not-just-privacy

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:

https://pluralistic.net/2025/04/19/gotcha/#known-to-the-state-of-california-to-cause-cancer

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:

https://pluralistic.net/2025/11/15/unconscionability/#standalone-authority

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:

https://pluralistic.net/2023/08/16/the-second-best-time-is-now/#the-point-of-a-system-is-what-it-does

And of course, Trump's CFPB (neutered by Musk and his broccoli-haired brownshirts at DOGE) killed that effort:

https://pluralistic.net/2025/05/15/asshole-to-appetite/#ssn-for-sale

But the CFPB staffer who ran that effort has gone to work on an effort to leverage a New Jersey state privacy law to crush the data-broker industry:

https://www.wired.com/story/daniels-law-new-jersey-online-privacy-matt-adkisson-atlas-lawsuits/

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 – 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: