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maybe i’m crazy but i’m not actually convinced you care about human dignity and inalienable rights if your outlook on the world is primarily about who should die and suffer
not to sound like the friend that's too woke but why does this police station toyset for a 5yr old need a weapons safe full of tiny toy-sized automatic rifles. just asking.
If there's sexual abuse subtext no there isn't, if there's allusion to sexual abuse you're ruining it for everyone else by talking about it, if there's a metaphor for sexual abuse you're reading too far into it and it's actually a metaphor for anything more palatable, and if there's on-screen sexual abuse? Well that's exploitative torture porn, of course.
the idea that restrooms, locker rooms, etc need to be single-sex spaces in order for women to be safe is patriarchy's way of signalling to men & boys that society doesn't expect them to behave themselves around women. it is directly antifeminist. it would be antifeminist even if trans people did not exist. a feminist society would demand that women should be safe in all spaces even when there are men there.
btw this is maybe the single most key distinguishing feature of the terfy strains of radical feminism, the seed all the rest of it springs out of: they have absolutely no faith in the ability of feminism to actually destroy patriarchy. they do not think feminism can truly build a better world. they cannot really even imagine that possibility. they think patriarchy is an inevitable natural consequence of unchangeable biological facts, and therefore the goal of feminism can only be to mitigate the worst effects of patriarchy, not to get rid of it.
they can imagine a society where women get some designated safe spaces without men around. they cannot imagine a society where the presence of men is not inherently a danger to women.
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i really do not think it can be overstated how much hatred against trans men on this site is driven by people finding trans men Just Kind Of Annoying. people have pipelined themselves into posting on the daily about how tmes should be raped to death because 6 years ago a teenage trans boy was somewhat overexuberant on a fandom post, and other such minor irritations. finding someone (and therefore their entire segment of the population) Just Kind Of Annoying is a direct line, especially on the internet, to completely unpersoning them. nothing one of those people ever says or does should be taken seriously, even if it's a matter of life and death. because they were kind of annoying :/
hang on yeah i want to talk more about this ^ specifically. been seeing people here discuss the phenomenon of the fascist "right to comfort" mostly in terms of people not wanting to have uncomfortable conversations or not wanting to be exposed to topics that are harmless but make them feel weird. and cringe culture is very much an extension of that perceived right to comfort. if you cannot extend your tolerance to someone who is really fucking annoying, not to be best friends with them or anything, but just to protect their basic rights and dignity, you lack principles. point blank. you need to build a bulwark of values that prioritize autonomy and dignity for everyone and it needs to be strong enough to weather the most irritating person you know, or else you will also inevitably become a fascist or part of the fascists' body count.
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.
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I'm thinking about AI again because we're in the Nexus that creates Torment for profit.
And there's a thought rattling around in my head over the fact that a lot of the issues with AI are not brand new to the technology era--a source of chronic and easy misinformation; a technology that causes people to withdraw and isolate and often self-harm; an environmental scourge; an absolute machete to critical thinking, disciplined learning, and meaningful study because why bother with hard and long tasks when you can get fast and easy dopamine hits--all of these can be applied to social media as well.
But something makes AI so foot-to-the-gas-pedal worse in such a festering and pervasive and sudden way, and I think my conclusion for that lies in this emergent technology debuting to the public already under the control of big tech mega-conglomerates who--and this part is critical--were absolutely panickingly desperate at the exact moment ChatGPT hit the scene.
And I'm going to bother you with graphs about this. Because I think it will help explain a lot of the Full-Shove-Down-Throatedness you've no doubt noticed with AI.
And like to back up a second--modern social media is controlled by these exact same mega-conglomerates. But the social media platforms didn't start that way. They were a lot of guy-in-his-basement style inventions--tumblr included--which developed to serve an organic and natural niche people wanted. No throat-shoving yet. Most of us (tumblr demographic) were probably sparkle-dogging on deviantart and--yeah whatever--superwholocking over here. And that was by no means perfect or necessarily even good but there was opportunity to ease in and learn the good and bad.
THEN the mega-conglomerates got it and Facebook is a festering cesspit and Twitter is Like That. But there was a bit of a gradual enshittification there, and I imagine most people following my blog are old enough such that you were able to recognize what enshitted itself and disengage appropriately.
AI--for all the way its ills mirror social media--was born a fully formed fucking asshole directly into the cradles of big tech. And it's weird to consider the technology behind LLMs is not, inherently, evil. And that feels weird because it never really had the chance to exist in the public eye in any capacity before it was the throat-shoved and desperate weapon of big tech downright begging you to rot your brain out your ears and steal your work and pollute the earth and stop thinking and stop trying and stop learning.
Which maybe then begs the question, WHY the crazy desperation? Why the complete disregard for sense, law, legal ownership, and customer voices? Why does 'fetch' need to happen and why do Tech CEO's talk about it like they're gonna bite the cyanide pill in their molars if you don't buy a fucking gemini subscription.
Well I promised you graphs.
Here's your first graph. And here's a little fun fact for those following at home: ChatGPT came out Dec 20, 2022.
This graph shows the stock price for Amazon, Google, Microsoft, and Meta (nee Facebook) between August 2016 (10 years ago) and Dec 20, 2022.
Now see that peak right before the start of 2022? Cover the downward side of it with your finger. Go on. Don't you see a beautiful exponential future of infinite growth? Don't you love when exponential growth is forever? Some of those returns were 400% in just 5 years. Sure, Covid caused a blip but what's a global pandemic in the face of a bullish stock run?
Okay now uncover the right side. That side is the screaming crying sobbing coaster of rich people feelings. And they were so very in their feels.
I'm zooming in on the graph for you. Welcome to November 2021 to Dec 20, 2022.
If you bought Amazon at peak? You were down 50%. Meta at peak? Down 60%. And every rich person investor is a money genius until the market turns, at which point it's panic to right the ship.
There were whispers of recession all throughout 2022 and it made the tech market crumble, because tech stock prices are highly speculative, and they ride market whiplash with more aggressive slingshotting than companies grounded in material value.
And then something happened on Dec 20, 2022. Obviously you know, I've said it 3 times. ChatGPT released. And oh boy.
Companies started promising AI. Investors started listening. The stock prices got happy.
Welcome to the graph 2022 to current day
And if the dance is working, they don't fucking stop dancing.
Do you WANT AI? Do you NEED AI? Will it actually help you? Might it in fact harm you? Will it harm the planet? Will it harm learning, and discipline, and the vulnerable, in the exact ways big tech companies are shoving it down people's throats?
Who give a shit! Whogiveasshitwhogiveasshitwhogiveahit. Money up! Desperate crying tech company HAPPY now. Money happy now!
Now the entire 10 year graph looks like this
And that scary scary scary scary drop off in 2022 that course corrected itself nigh exactly on December 20, 2022?
Well, you love AI, don't you? 🙂 As long as it's shoved down your throat.
Dude thank god for Zoran Mamdani cause ever since that guy got elected the amount of left-bashing I've seen from regular libs who aren't either deeply imbedded in mainstream Dem politics or some kind of parasocial hater has gone down dramatically
For a long time now Bernie Sanders has been the face of the American left and I like Sanders but a lot of liberals legitimately loved Hillary and the fact that he ran against her in a year that was supposed to be a coronation for her has forever soured a percentage of the base on him, and that souring extended out to his most zealous supporters as well. Zoran, on the other hand, not only gets to point to a number of concrete policy Ws, but saved NYC from being run by either a serial sexual predator or a comically corrupt oaf.
You listen to music regularly? Why? Have you even tried quitting? Could you quit? You get music stuck in your head? Wow. You're so ruined and music brained. I bet you make your partners listen to music with you when you have sex. Music addiction has really ruined a whole generation. You know it's not realistic to expect reverb in real life, right? You're probably so desensitized that you don't even feel anything anymore when you hear a bird singing that it wants some fuck.
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