Cassandra: YOU ARE ALL GOING TO REGRET THIS SO MUCH YOU DON’T EVEN KNOW.
Odysseus: Regret it why?
Cassandra: You won’t believe me if I tell you. If I prophecy, nobody believes me. That is my curse.
Odysseus: … I’m Nobody. Fill me in.
*A couple of months later*
Odysseus: HELLO PENELOPE, I AM HERE PRECISELY ON TIME AND NOT YEARS LATE incidentally I rescued and adopted a Trojan seer while I was away, she’s great, got me home really fast, Cassandra this is your new mother who’s not going to treat you like shit.
Penelope: … I’m going to need more details, but okay, sure.
Okay, I need to add some clarification and correction to this.
This photo is known as The Pale Blue Dot. It was taken by Voyager 1, a space probe meant to explore the outer reaches of the solar system. Far from dying, she's still out there doing her job and is the furthest human made object from Earth.
In the mid 80's, they knew Voyager 1 would soon pass beyond where her cameras would matter and she needed to save power, so the question became: what's the last thing she should take a picture of?
Carl Sagan and Carolyn Porco both independently had the same thought: take a picture of Earth. Us. Yes, it would be essentially just one pixel. It wouldn't be scientifically useful. It might even damage the camera because of how intense the sun is, even forty times as far from Earth as Earth is from the Sun. But they got it sorted because it's NASA.
3.7 million (not billion) miles away, that's Earth. Caught in bands of light, artifacts of the Sun's incredible power even 4 million miles away. We are an island in a sea of radiation and vacuum and it's all we have.
I can't say it better than Sagan did, so I'll let you alone with his words:
From this distant vantage point, the Earth might not seem of any particular interest. But for us, it's different. Consider again that dot. That's here. That's home. That's us. On it everyone you love, everyone you know, everyone you ever heard of, every human being who ever was, lived out their lives. The aggregate of our joy and suffering, thousands of confident religions, ideologies, and economic doctrines, every hunter and forager, every hero and coward, every creator and destroyer of civilization, every king and peasant, every young couple in love, every mother and father, hopeful child, inventor and explorer, every teacher of morals, every corrupt politician, every "superstar", every "supreme leader", every saint and sinner in the history of our species lived there – on a mote of dust suspended in a sunbeam.
The Earth is a very small stage in a vast cosmic arena. Think of the rivers of blood spilled by all those generals and emperors so that, in glory and triumph, they could become the momentary masters of a fraction of a dot. Think of the endless cruelties visited by the inhabitants of one corner of this pixel on the scarcely distinguishable inhabitants of some other corner, how frequent their misunderstandings, how eager they are to kill one another, how fervent their hatreds.
Our posturings, our imagined self-importance, the delusion that we have some privileged position in the Universe, are challenged by this point of pale light. Our planet is a lonely speck in the great enveloping cosmic dark. In our obscurity, in all this vastness, there is no hint that help will come from elsewhere to save us from ourselves.
The Earth is the only world known so far to harbor life. There is nowhere else, at least in the near future, to which our species could migrate. Visit, yes. Settle, not yet. Like it or not, for the moment the Earth is where we make our stand.
It has been said that astronomy is a humbling and character-building experience. There is perhaps no better demonstration of the folly of human conceits than this distant image of our tiny world. To me, it underscores our responsibility to deal more kindly with one another, and to preserve and cherish the pale blue dot, the only home we've ever known.
i could not eat a dozen fried eggs and would never want to do so. but i almost certainly could accidentally eat a dozen deviled eggs if i was at a party or perhaps a picnic and they were there. they are a highly dangerous food item in this way
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 – 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":
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 – 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:
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 – 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.
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 – 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).
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 – 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:
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 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):
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:
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 – ending surveillance itself, in this case – 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 – 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:
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.
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.
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."
…
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.
