the thing about capitalism is that at a certain point a product reaches its maximum audience and cant really be improved (at least not while remaining profitable), but capitalism requires a product provide infinite growth, and at that point the only way to increase profits is to raise prices, cut corners, and in the case of services start adding advertisements. this is just how the system works.
Rent-seeking is the act of growing one's existing wealth by manipulating the social or political environment without creating new wealth.[1] Rent-seeking activities have negative effects on the rest of society. They result in reduced economic efficiency through misallocation of resources, reduced wealth creation, lost government revenue, heightened income inequality,[2][3] risk of growing political bribery, and potential national decline.
The actual economic term for this parasitic behavior is "Rent Seeking", as in "charging you rent for things that didn't used to cost money just because we can."
"The classic example of rent-seeking, according to Robert Shiller, is that of a property owner who installs a chain across a river that flows through their land and then hires a collector to charge passing boats a fee to lower the chain. There is nothing productive about the chain or the collector, nor do passing boats get anything in return. The owner has made no improvements to the river and is not adding value in any way, directly or indirectly, except for themselves. All they are doing is finding a way to obtain money from something that used to be free."
obtain money links to the wikipedia article for Parasitism which might be the most brutal diss I've ever seen on wikipedia ever
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:
One of my favorite rhetorical and analytical moves is joining things together (showing that two different, seemingly unrelated ideas are aspects of the same phenomenon) and taking them apart (resolving a paradox by demonstrating that what appears to be one, contradictory thing is actually two different things that have been lumped together).
"Taking things apart" is a very useful framework for understanding AI. How do we resolve the (seeming) paradox that some skilled workers report wonderful results from their work with AI, while others are full of dire warnings about the lurking defects in their AI-assisted outputs? Simple: the first group are "centaurs" (humans who are assisted by machines) and the second are "reverse centaurs" (humans who have been pressed into service as peripherals for machines):
What are we to make of the people who've been fired by bosses who replaced them with AI, in light of the fact that AI is demonstrably not able to do their (former) jobs? Again, it's simple if you separate out two distinct phenomena: "AI can do your job" is the first. The second is: "Your boss is a credulous dolt who is infinitely horny for replacing lippy workers with pliable machines, which made him an easy mark for an AI salesman who convinced him to fire you and replace you with an AI that can't do your job":
This is also a useful move for understanding the AI investment bubble. It's not just billionaires who don't think other people are as real as they are and consequently their jobs can be done by chatbots. It's also billionaires who believe that bosses can be sold AI and don't care if the AI is defective, because that's your boss's problem after he buys the AI and fires you. They don't have to believe in AI in order to think it's a good investment: like an investor betting that Joe Rogan can sell millions of dollars' worth of peptides to desperate young men, they are assessing the sales potential, not the merits of the thing for sale:
https://pluralistic.net/2026/08/03/andor/#either
As useful as "taking things apart" is, "putting things together" is also a very important technique for assessing, critiquing and improving AI. In a stellar essay entitled "Temperature Zero for Culture: Why Everything Is Starting to Look the Same" by the data scientist Lauren Leek, we get a top-notch example of "putting things together":
Leek's essay is one of those fabulous, wide-ranging, cross-disciplinary pieces, touching on urban design, music trends, synthetic LLM crowds, Netflix recommendation algorithms, and several other subjects, all seeking to resolve a(nother) (seeming) paradox: how is it that we have so much potential variety, but everything is so manifestly the same?
The answer is complicated and nuanced, but Leek's foundational point is that in a data-driven society, "predictions" are self-fulfilling prophecies. As Leek puts it: "Once prediction shapes the choices in front of us, we lose the ability to tell the difference between what people wanted and what the system made easy to want."
This is a pervasive issue across many domains. Leek says that economists call it "performativity," while machine learning researchers call it "model collapse" and urbanists call it "placelessness."
"Performativity" describes how, once a market has been modeled by economists, that model becomes the foundation for economic policy, which pushes the market to conform to the model:
"Model collapse" describes how machine learning models that are trained on their own predictions become incredibly bland, with all variety disappearing from the system's predictions:
This is hugely consequential: it's why bias proliferates through predictive policing algorithms: train a model with data from racist stop-and-frisks and it will predict that all the weapons and drugs in a city are to be found in Black and brown peoples' pockets. Turn those predictions into recommendations telling cops where to go look for weapons and drugs and they will double down on racist stops, producing even more biased training data, which turns into still more bias in the predictions:
"Placelessness" is the urbanist's name for "when everywhere optimises toward the same template." I think of it as Flinstones Syndrome, where the same background is looped behind Fred and Barney as they drive through Bedrock. In New York City, it's Citibank-bodega-Chipotle-Walgreens; in the Chicago suburbs, it's the strip malls with a Chili's, a gas station, and a big box store.
Leek proposes that these are all expressions of the same underlying phenomenon, a failure mode of data science that takes a world of "granular personal data" and arrives at a world where "personalisation produc[es] more sameness."
To these excellent examples, I'd add another one, from the world of monetary policy: Goodhart's Law, which holds that "When a measure becomes a target, it ceases to be a good measure":
https://en.wikipedia.org/wiki/Goodhart%27s_law
Goodhart's Law captures a wide variety of phenomena. When Google first deployed Pagerank, they showed that by counting the inbound links to all the pages on the web, you could extract a signal about which pages were most important (because there was no reason to link to a page unless you found it noteworthy).
But once Pagerank became the dominant means by which web users found pages, counting links stopped being useful: first, because people used Pagerank to find the best pages and link to them, making it impossible for new pages to get the inbound links needed to supersede incumbent pages; and second, because it's easy for fraudsters to create inbound links for low-quality pages in bulk, once there's a reason to do so.
Counting inbound links was a world-beating retrospective way of predicting which page would best match a searcher's query, but once it shaped the world it sought to analyze, it ceased to be a good prospective way to predict which page would best match your queries.
Leek is a brilliant data scientist and an even better science communicator, with a knack for crisp, readily understood explanations. How can a world of granular, highly varied data turn into a world of homogeneous choices? Simple: start with a set of items ("cuisines, genres, shop types") and a standard algorithm for sorting them. Let users choose from those recommendations. The mode (average) of those choices "gets shown more, so it gets picked more, so the model grows more confident the mode is what people want, and the tails starve." Run this for a few rounds and the evenly distributed catalog of choices "collapses onto one dominant option."
This is intrinsic in the choices we make in designing recommendation algorithms, tilting them towards the likelihood of a successful recommendation. A recommender that wants to succeed every time will make the safest possible recommendations, "so an algorithm that is uncertain about you, and it is always at least a little uncertain, hedges toward the average."
Then she busts out a beautiful, perfect little statistics aphorism: "Personalisation under a standard loss function is regression to the collective mean with extra steps." That is to say, "regression to the mean" (the tendency of varied things to become more standardized) cannot be avoided with the standard personalization algorithm. That algorithm is going to play it safe, showing you things that are broadly palatable, and because your choices are constrained to the average, you will choose average things.
This is how recommendation systems – and other analytical tools that produce predictions that are then turned into action – force so many diverse phenomena (streets, markets, media recommendations) into sameness. The fact that these recommenders are self-fulfilling prophecies means that "they don't have to be right," only "listened to."
This explains the sameness of so many of London's high streets. Leek examines 640 shopping streets, characterizing 18,000 food places spread out across them, flagging all the chain restaurants. Her analysis shows that any two London streets will, on average, share about half of their "food profile."
Obviously, this is most pronounced on streets with chain outlets, and it doesn't take that many chain outlets before a street's sameness shoots up: "A relatively small number of repeated names is enough to make otherwise different streets resemble one another more." So why do streets with chains resemble one another so much? Because the chains use an algorithm (weighting footfall, proximity to train stations, demographics, and competitors) to decide where to put their restaurants. If a street with a Gail's Bakery on it feels like every other street with a Gail's Bakery, that's because Gail's only puts its restaurants in places that have highly similar characteristics, measured to a high degree of accuracy and controlled by a narrow set of tolerances.
In other words, every street that feels like it should have a Gail's will eventually get a Gail's, whereupon that street will feel even more like all the other streets that have a Gail's, because it will share one more common factor with those other streets (a Gail's).
Leek points here to her earlier work on pub closures in the UK. The UK has experienced an epidemic of pub closures, with thousands of pubs disappearing since 2016:
Her research found that the biggest predictor of a pub surviving was its similarity to the median pub; which is to say that the more distinctive a pub was, the more "character" it had, the more likely it was to close. Pubs that are different from the average pub are harder to categorize, which means they're harder for a bank manager to assess for creditworthiness or for a landlord to justify extending a long-term lease to. The algorithms used to allocate capital and real estate are also recommenders, and they also drive variety out of the system.
This same phenomenon acts on culture. In an age of music recommendation algorithms, hit songs are changing; today's songs use a smaller vocabulary of unique words and repeat those words more often:
Vocabulary richness, distinct words relative to length, has fallen by more than a quarter since the early 1960s, while the share of repeated lines has climbed by nearly a third. The modern hit says less and says it more often, because the hook that works gets repeated.
But that's not the whole story! While each song resembles itself more ("saying less more often"), within that constraint, there's far more variety today than before: a given song's (constrained) vocabulary has grown more distinct when compared to all the other songs' vocabularies. Songs repeat the words they use, but the words repeated in songs are getting more different.
For Leek, this is the key to understanding the whole phenomenon and (more importantly) doing something about it. Music recommendation systems optimized for a singable hook, but did not optimize on any of the other variables in songs, so those dimensions acquired a broader range, even as the optmized variable got flatter and narrower.
This means that the tendency of recommenders to "flatten the world" isn't a single blunt outcome: it depends on which dimension we choose to flatten through recommendation, and who chooses to flatten that dimension.
A media recommender optimizes for consumption, showing you a tractable set of things it believes you'll watch, read or listen to. When you choose from among this limited set, the recommender takes note of that fact and shows you more of the same, pushing everything to a greige median. All the movies, books and songs you might have liked that were omitted from that initial set are excluded from being recommended in the future. The features of that media that you might have appreciated "decay out of consideration." They are never tested for desirability. The model collapses.
How badly does it collapse? Leek cites Movietweetings' data on which movies people watch: out of a million public movie ratings, half relate to the top 2% of movies in the set. There's 38,000 films in the set, but just 380 titles account for 40% of the ratings. Leek argues (persuasively) that this isn't because recommenders are good at "knowing your taste" – rather, they are good at "narrowing the menu."
Leek relates this to her work on creating LLM "personas" – synthetic populations meant to mimic the tastes and proclivities of real groups of people, that you can interrogate "before you spend money asking actual humans." While this would be useful for many applications, "it fails in exactly the way this whole essay is about."
Leek went to enormous lengths to reproduce the traits that make people interesting to study in aggregate, painstakingly replicating the ways that social connections, psychological outlook and demographic factors predict people's beliefs. The result was a set of LLM personas with "elaborate stories" about how they differed from one another, but whose survey responses about planned actions were homogeneous in a way that real populations are not.
This, Leek writes, is the same force that homogenizes other data-driven predictors. Because she'd ordered her LLM to reproduce the statistically validated relationships between different factors that predict a person's beliefs, each synthetic persona was a homogenized average. It's like the paradox of "The Average Man," where military uniforms sized to the average of all service personnel fit no one, because no one is average:
https://archive.org/details/DTIC_AD0010203
The thing is (as Leek points out) the idea that synthetic personas are a good way to understand the preferences of a real population is not a harmless delusion: it's a product that's being actively sold to governments, campaigning politicians and marketers. It's a self-fulfilling prophecy that drives governance, political campaigns and product design to the same homogeneous median that is making every shopping street in London feel the same.
This matters. As Leek writes, ecologists have long understood the importance of variety for systemic resilience: they call it "the insurance value of biodiversity." A diverse system has reservoirs of species and variation that may not be optimized for how things stand now, but that can move into niches created when things change in ways that lay waste to the previously dominant organisms. As anyone whose favorite banana went extinct can tell you, homogeneity works well, but diversity fails well:
https://en.wikipedia.org/wiki/Gros_Michel
The brittleness of algorithm-induced homogeneity is compounded by the fact that recommenders obscure the true preferences of people. If you watch two Scandinavian crime dramas after Netflix recommends them to you, it will keep showing you more Scandy crime for the next decade – even if there's another kind of programming that you'd vastly prefer (if only you knew about it). This means that decision-makers who choose which shows will get made in the future will keep on funding their safe Danish detectives, to the exclusion of whatever might emerge from the same weird attractor that produced the K-Pop Demon Hunter fortune.
Transpose this failure mode onto states, bank managers and landlords, and we see whole ranges of policies, businesses and activities that never come into existence, despite the popularity, prosperity and joy they might bring us.
But Leek doesn't end with this worrisome note. Instead, she identifies this whole thing – model collapse, placelessness, performativity, even Goodhart's Law – as an expression of one of the best-understood tradeoffs in computer science: "exploration vs exploitation":
Any system learning from feedback has to divide its effort between exploiting what already scores well and exploring options it hasn’t tried, in case they’re better.
Computer scientists have long understood that focusing on exploitation to the exclusion of exploration is a trap that locks you into "the first decent option" so you can never discover the best one.
Which means that this algorithmic homogeneity has a well-understood corrective: "forcing exploration back in." The problem is that markets hate this kind of exploration. A company that lives and dies by how many clicks it gets is never going to sacrifice 20% of its traffic by showing its users weird, untested options that score worse than the median because these weird things have never had a chance to prove that they are desirable.
This is a classic market failure, and, as Leek points out, there are regulatory responses in the UK (the Digital Markets, Competition and Consumers Act) and the EU (the Digital Services Act), both of which require the largest platforms to open up their recommendation systems, but so far, regulators have focused on "online harms" rather than variety (though the DSA does require platforms to offer algorithmic recommendations that are not based on your personal traits).
Leek identifies this willingness of states to set conditions for algorithm design as a means by which "exploration" can be forced back into the system. She's also bullish on interoperability, so that users can leave platforms with bad recommenders, without losing access to their media or social circles. As she writes, "the deepest discipline on a feed that has trapped you is the credible ability to leave it and take your data with you." I couldn't agree more:
She's less hopeful about individual responses. Demanding that you be an "adventurous consumer" is a way of letting systems off the hook. When every street has the same restaurants and every bookshop has the same books and the people in your life are all locked into one of two social media platforms, "choosing wisely" only gets you so far. Shopping isn't politics!
Leek is a superb writer. After reading this piece yesterday, I sent it to half a dozen people and then read everything else in Leek's newsletter archives. Not only is it all brilliant, but I also realized that she'd written one of the most memorable articles about cities and platforms I've read in the last year, "How Google Maps quietly allocates survival across London’s restaurants – and how I built a dashboard to see through it":
I should have added Leek's newsletter to my RSS reader when I read that last December. I've rectified that oversight! What a fantastic thinker, scientist and communicator! If she isn't being relentlessly pestered by editors and literary agents offering her a book deal, then it really does prove that the recommender systems are elevating the bland median over the thoroughly, delightfully spiky outliers.
Highlighting a few especially key parts (bolding mine):
This is also a useful move for understanding the AI investment bubble. It's not just billionaires who don't think other people are as real as they are and consequently their jobs can be done by chatbots. It's also billionaires who believe that bosses can be sold AI and don't care if the AI is defective, because that's your boss's problem after he buys the AI and fires you.
[…] Leek's foundational point is that in a data-driven society, "predictions" are self-fulfilling prophecies. As Leek puts it: "Once prediction shapes the choices in front of us, we lose the ability to tell the difference between what people wanted and what the system made easy to want.”
[…] "Personalisation under a standard loss function is regression to the collective mean with extra steps." That is to say, "regression to the mean" (the tendency of varied things to become more standardized) cannot be avoided with the standard personalization algorithm. That algorithm is going to play it safe, showing you things that are broadly palatable, and because your choices are constrained to the average, you will choose average things.
Her research found that the biggest predictor of a pub surviving was its similarity to the median pub; which is to say that the more distinctive a pub was, the more "character" it had, the more likely it was to close. Pubs that are different from the average pub are harder to categorize, which means they're harder for a bank manager to assess for creditworthiness or for a landlord to justify extending a long-term lease to. The algorithms used to allocate capital and real estate are also recommenders, and they also drive variety out of the system.
This matters. As Leek writes, ecologists have long understood the importance of variety for systemic resilience: they call it "the insurance value of biodiversity." A diverse system has reservoirs of species and variation that may not be optimized for how things stand now, but that can move into niches created when things change in ways that lay waste to the previously dominant organisms.
“…billionaires who believe that bosses can be sold AI and don't care if the AI is defective, because that's your boss's problem after he buys the AI and fires you…”
↓
“…[sellers] who believe that [customers] can be sold [a product] and don’t care if [that product] is defective, because that’s [the customer’s] problem after he buys the [product]—”
fraud. you’ve literally just defined fraud.
a third of the economy is currently running on digitial snake oil peddled by billionaire tech quacks.
like. obviously everything else here is important. but tbh it all fades into the background compared to the scale of the blatant fraudulent scheme at the heart of it all.
Yes! Neither the proponents of corporate-owned AI nor the majority of opponents of AI in general are willing to say The Magic Words, formulated by tech journalist Ed Zitron as the answer to his own question about why there are no revolutionary, far-reaching if not ubiquitous AI-based solutions in the fields where it's pushed the hardest. And The Magic Words are: "none of that shit even works".
Those words destroy the sales pitch, and moreso, The Sales Pitch But As A Bad Thing™️: the Great Replacement Theories peddled to Business Idiots from corporate event stages by the likes of Mira Murati, Scam Altman or Dario Amodei find no base in reality, when the business plan is to rope in all the rubes, addict them to a product that doesn't work by allowing them to integrate it deeply with their systema, then bilk, bilk, bilk. With their precious corporate data and all the workflows trapped in the AI corporation data centers, they're effectively held hostage. This is the point that the staggering majority of people is missing.
So most stories take place when Events are Happening, and this means that no matter what kind of job the characters have, they’re probably not too focused on them. Fanfics, on the other hand, often show the down time. Which means that the writer has to figure out what the hell these characters do in their jobs. Unless the characters have a job the author understands or knows well, the author is often at a loss for what to have the character doing.
So they sit them at a desk and give them paperwork. What is the paperwork for? Rarely specified. It is Paper Work for the characters Important Adult Job they have and they need to read or sign it or something. And there’s always a line about how Character Hates Paperwork. Doesn’t matter if Character is a Mafia Boss or a General or a Diplomat, here they are in an office trying to get out of Doing Paperwork.
There’s also a sense of, like, humor and mundanity that comes with it. Like the examples above, it always particularly stands out to me when a dangerous individual is griping about some paper they need to sign or something. The less you can picture Character doing paperwork, all the better to force it upon them. If Character is saddled with Paperwork, they’re usually now concerned about the physical damages their motley crew causes, because damage = More Paperwork.
Anyway I just think it’s fun or funny, Sephiroth doing paper work and Sawada Tsunayoshi doing paper work and this just in, Tony Stark is doing paperwork. Sorry, Phoenix Wright can’t play right now. Yeah, it’s paperwork.
Your Royalty or Nobles in your feudal society should be doing this constantly unless you want to show how they're losing the kingdom and about to be usurped or overthrown.
petitions, speech-writing, complaints, requests, assignations, disputes, judiciary forms, declarations that need signing off on, matters requiring the Royal seal, matters requiring a royal endorsement, pardons, judgments, invitations, rebuttals, census data, crop yields projected or actual, livestock records, water rights, Sumptuary laws, taxes taxes taxes...
even when the royalty look like they're just fucking off to have fun, they're doing Socially Mandated Fuck-Off and Have Fun Time
Tournaments? Gotta show you're manly and virile through sport, or the nobles will supplant you. Feasts? Gotta show you're wealthy through conspicuous consumption, or the nobles will supplant you. Patronizing the arts? Gotta show you're cultured and erudite, or the nobles will laugh at you behind your back and probably supplant you.
Lawyer here. Here’s some more mundane/modern ones for you:
Invoices.
Intake sheets (information about new clients. The one for my firm is 13 pages and requires an hour long meeting. Then you have to do something with the info gathered)
Data entry for invoices and intake sheets.
Billing. (More involved than timesheets because you’re justifying to clients why you’re getting paid so much).
Form letters.
Taxes. Including 1099s for any contracted work.
Bank statements/accounting spreadsheets. Gotta track how expenses are trending.
Insurance documents.
General messages. Lots of office still use a paper system for missed calls and “important” stuff because it just works better than emails and chats for some people.
Memos. Big enough office to have at least one attorney on payroll? You’re getting memos about every legal question and concern and contract. It’s how we’re trained to communicate in formal settings.
And if you want to get into modern military, the forms are numbered, and people will refer to them by either their actual name, or the number. (Have you filled out the 4187 for this? Yes, I filled out the personnel action form.)
If you want to add a rage level, there’s the Regular Paperwork and then the special hell of Fuckup Paperwork.
Cause there’s the invoice, and the follow up invoice and the we are about to have a problem follow-up invoice.
There’s the incident report and then the I swear I yelled at that worker about this don’t be mad at me if they do it again second warning documentation
The requisition form and the it has been six months now get me the fucking thing or you’re gonna hear from Cindy escalations form.
For law firms, there's also the 'I am asking nicely for you to send me this legally mandated document' and then the follow-up 'My please was a social nicety, I am no longer asking, I am telling you to send me this document or explain to a court why you refuse to do so' which, depending on the type of law, can fill a significant amount of the day
For anyone in a corporate or higher ed setting, if you think your blorbo is in too high a position to be bothered with the above, then I guarantee you if they are 1) in any sort of decision making role and 2) not the person actually doing the budget spreadsheet, invoice, grant app/drawdown, etc. then they are very, very likely the person who has to review and approve someone else’s work as a segregation of duties control. The more important the work or function, the more levels of review and the higher they’ll go, and the timeliness and thoroughness of those reviews will matter to people, and there Will be consequences if (due to maiming or other whump or ye olde world saving mission etc.) they were not completed or appropriately delegated by said blorbo.
It's fantastic how after all this trouble and commotion, the version of the EU replaceable battery mandate that actually passed contains an explicit exemption for devices which retain at least 80% capacity after 1000 charge cycles, so the only real effect for the average person is to extend the planned obsolescence cycle from two years to three.
I did some changes to the fit of my riddler cause I thought his coat felt a little too generic (this checker pattern will definitely not come bite me in the ass later)
2087
kianamaiart
romaincrisis
germansingergirl
blakekathryn
