imagine if you will, a fairly dry survival crafting game in which you live in a bunker and must periodically venture out to scavenge food, set up turrets for attacking monsters, etc
now, your computer inside the bunker has a game-inside-a-game on it which is a charming farming sim of undeniably greater quality and scope than the survival game you're playing. therefore, the object of the game becomes to keep your bunker secure so you can play the farming game more.
now, once you achieve the highest rating in the farming game, a secret shop inside it unlocks, and one of the novelty items you can purchase is a game console, giving you access to games-inside-a-game-inside-a-game. most of the games for it are typical mobile shovelware, but one of them is a highly polished, extremely brutal precision platformer with amazing level design and production values exceeding that of the survival game and farming sim combined.
it is only at this point that the purpose of this entire contrivance becomes clear: to create the most deranged speedrun community the world has ever seen.
what’s important to note and missing from the “headline” tweet is that they simultaneously constructed additional good public transit to the public transit already in the city (bus rapid transit, train stations). Just removing highway alone isn’t going to make traffic better, the bigger part of the story is that they improved public transportation. And the current mayor wants to do more - cyclist lanes and reinstate a tram system
“Because the truth is, tech doesn’t have an image problem. It doesn’t have a message problem. It has an intention problem. What’s wrong with the axe murderer who broke into my house is not that he hasn’t successfully persuaded me to buy into his narrative. What’s wrong is that he’s trying to kill me with an axe. Similarly, when you launch a product that’s designed to put millions of people out of work, block access to sources of verifiable truth, replace human creativity with slop, and lower the barriers to every sort of atrocity, the problem isn’t that you haven’t told the public a good story about those things. The problem is that you are trying to do them.”
Lawmakers push DoD to tighten smartphone controls after adversaries exploited commercial tracking data
Unfortunately, the US government seems to be taking the view "we need to restrict what our personnel can do more" and "we need to set up our devices differently" than the actual real answer which is to fucking stop ubiquitous non-consensual data harvesting. This is a problem for military sure but it is also a major issue for civilians. Literally since day 1 this has been an issue for everyone.
Maybe the EU will be smarter and regulate this shit.
Text of tweet under the cut because it is loooong.
But... Stochastic Parrots.
Timnit Gebru was fired from Google in December 2020 for refusing to retract a research paper, and every single warning that paper made about large language models has now happened at a scale the industry spent 4 years trying to make people forget about.
Her name is Timnit Gebru.
She co-led the Ethical AI team at Google. She co-wrote a paper called "On the Dangers of Stochastic Parrots" with Emily Bender at the University of Washington and two other researchers. The paper was 14 pages long. It was submitted to a top AI ethics conference. And it was the reason Google decided that one of the most senior Black women in AI research could no longer work there.
The story Google told publicly was that she resigned. The story she told, confirmed by 2,695 of her colleagues in an open letter, was that she was fired by email while on vacation because she refused to either retract the paper or remove her name from it.
The paper had not even been published yet.
Here is what she actually wrote, and why every prediction inside it has now come true.
The first warning was about scale itself. Bender and Gebru argued that training ever-larger models on ever-larger scrapes of the internet would produce systems that appeared fluent but had no actual understanding of language. They called these systems stochastic parrots because they would repeat patterns from training data with statistical confidence and zero comprehension. The paper predicted that this apparent intelligence would fool both users and developers into trusting outputs that were structurally incapable of being reliable.
This was 2020. GPT-3 had just come out. The paper predicted the hallucination problem before anyone had a word for it.
The second warning was about bias amplification. The paper documented in detail that internet-scale training data contains systematic overrepresentation of dominant viewpoints and underrepresentation of marginalized ones. The models would not just absorb this bias. They would amplify it, because the optimization process rewards confident outputs, and confidence in language patterns tracks frequency in the training set.
The prediction was that hiring tools built on these models would discriminate against women. That healthcare triage tools would underperform on Black patients. That loan approval systems would entrench inequality while presenting their decisions as neutral algorithmic judgment.
Every one of those things has now been documented in deployment.
Amazon's hiring algorithm penalized resumes that contained the word "women" in any context. Healthcare risk scoring algorithms used by major US hospitals were found to systematically underestimate the medical needs of Black patients. Apple Card's credit algorithm gave wives credit lines 10x lower than their husbands for the same financial profile.
The third warning was about environmental cost. The paper calculated that training a single large language model produced emissions equivalent to the lifetime output of 5 cars. The prediction was that the race to scale would create an environmental footprint that would eventually rival entire industries.
In 2024, Google's emissions were up 48% from 2019, and the company explicitly blamed AI infrastructure. Microsoft's were up 29%, same reason. Both companies have now quietly abandoned the climate commitments they were publicly celebrating the year Gebru was fired.
The fourth warning was about documentation. The paper argued that the training datasets being assembled were too large for anyone to actually audit. Nobody at Google, OpenAI, Meta, or any other lab could tell you with confidence what was in the data their models were trained on. This was not a temporary problem to be solved later. It was a permanent feature of the approach.
In 2023, researchers discovered that the LAION-5B dataset, used to train Stable Diffusion and other major image models, contained thousands of images of child sexual abuse material. The companies that had trained on the dataset had no way of knowing. The paper predicted that category of failure 3 years before it was found.
The fifth warning was the one Google cared about most.
Bender and Gebru argued that the deployment of these systems would centralize linguistic and cultural power in the hands of the small number of companies that could afford to train them. The internet would become a place where the dominant voice was a statistical average of dominant voices, presented as a neutral assistant. Languages underrepresented in the training data would degrade over time as more web content was generated by these systems and fed back into the next training run.
This is now happening in real time. A 2024 study found that 57% of new web content in English is AI-generated or AI-assisted. Researchers studying low-resource languages have documented active degradation in translation quality, because the synthetic content fed back into training is itself worse in those languages.
The paper Google fired her for predicted the model collapse problem before model collapse had a name.
The mechanism behind why this all happened is the part of her work that nobody quotes.
Gebru's argument was not that AI is dangerous in some abstract sci-fi sense. Her argument was that AI is dangerous in a very specific structural sense. The technology was being built by a small group of researchers who shared similar backgrounds, worked at similar companies, and were rewarded for shipping products faster than competitors. The incentive structure made it impossible for safety, ethics, and bias concerns to slow anything down. Anyone inside the system who raised those concerns was either ignored, sidelined, or removed.
She was making that argument from inside Google.
Then Google proved her right by removing her.
The team Google had built to make sure their AI was safe was dismantled in 90 days because they did the job they had been hired to do. Margaret Mitchell, the other co-lead of the Ethical AI team, was fired two months after Gebru for searching through her own emails for evidence of how Gebru had been treated.
Gebru did not stop. She founded DAIR, the Distributed AI Research Institute, in 2021. The mission is to do AI research outside the control of the companies that have a financial interest in not hearing the answers.
Every prediction in the Stochastic Parrots paper has now been validated by deployment. Hallucinations are an industry-wide problem the largest labs cannot solve. Bias amplification has been documented in hiring, healthcare, lending, and criminal justice. Environmental costs are larger than entire small countries. Training data audits remain impossible. Model collapse is an active research crisis at every major lab.
The question worth sitting with is the one almost no one in the industry will say out loud.
Every researcher with the technical credibility to call out these problems watched what happened to her in December 2020 and made a calculation about their own career. The number of people willing to speak publicly about safety and ethics issues inside the major AI labs collapsed after that firing and has not recovered.
The researcher Google fired for warning about exactly what is now happening was right.
The company that fired her is now the second-largest deployer of the technology she warned about.
And the people inside that company who agree with her are not allowed to say so.
I think this is just a trend everywhere but I've been very frustrated this week by how much admin work is being outsourced to me as the patient/customer.
My orthodontist tells me I can make an appointment with the surgeon. I call the surgeon. They tell me I need a new referral. I call the orthodontist. They do a referral. I call the surgeon. Referral didn't come through. They tell me about their special unique system we have to use. I call the ortho again and walk them through the referral. I call the surgeon. They say the referral was missing some details so they have to do it again. I call the ortho.
The insurance company calls me about repair shops. I give them the name of the repair shop which I already gave them yesterday. They say they're not in their system but I can use them, but I have to call the repair shop to ask them to contact the insurance company. I call the repair shop and they say the insurance company is supposed to email them.
I feel like at a certain point these constant fetch quests become unreasonable?? Is it too much to expect these groups to communicate with each other instead of making me run back and forth between them???
Made this post and then the new property manager (who started on Monday and only finally emailed us today because I sent a vaguely professionally hostile email to her boss because I hadn't heard anything and was not convinced she existed) asked for a list of open action items which her predecessor should have had but apparently wasn't keeping track of, which I learned when I met her boss and provided her with the list of open action items, which I guess tragically died in a fire in the last 2 weeks since she was sitting at my kitchen table, being menaced by the skull. How many people's jobs am I doing now
My next book is The Reverse Centaur's Guide to Life After AI, out next month. Pre-order it now, including as a DRM-free audiobook or ebook, at my Kickstarter, and help me continue to prove that DRM-free isn't just the right way to reach an audience, it's also the best way to reach them.
From the earliest days of technopolitics, the role of technology in resisting authoritarianism was unclear. On the one hand, there's the indisputable fact that modern cryptography, properly implemented, can deliver a degree of privacy that is proof against all technological attacks.
That is to say, if you pull out your distraction rectangle, fire up the camera, and tap the shutter button, in the ensuing eyeblink instant the image you've captured will be scrambled so thoroughly that it could never be unscrambled without the secret key unlocked by your passphrase or biometrics. Even if every hydrogen atom in the universe were converted into a computer, and even if all those computers spent all the time between now and the end of the universe trying to guess what the key was, we would run out of universe and time long before we ran out of possible keys.
What's more, this extremely robust form of scrambling and descrambling can be combined with other techniques to block tampering with the encrypted data, and to allow parties to reliably identify who scrambled the data and also to restrict who may unscramble it. These remarkable technological facts have inspired many excited debates about what they mean for our politics, most notably among a group of people who called themselves "cypherpunks":
One cypherpunk faction believed that modern cryptography could enable a kind of technological secession: by allowing ordinary people to communicate, transact and collaborate without the possibility of state interception or control, crypto could make states themselves obsolete.
But another faction pointed out that no amount of mathematics could help you if an agent of the state – or a criminal the state failed to protect you from – tortured you until you revealed the secret passphrase needed to unlock your secrets. This was (ironically) called "rubber hose cryptanalysis" (as in "Tell me your passphrase or I'll hit you with this rubber hose again"). Later, this became known as a "wrench attack" after a famous XKCD comic about $1m worth of security technology being defeated by hitting someone with a $5 wrench until they divulged the password:
https://xkcd.com/538/
Once you stipulate to the problem of wrench attacks and rubber-hose cryptanalysis, it becomes apparent that your cryptography is only as good as your physical defenses. What's more, the most effective physical defenses we have come from a strong rule of law, because even the thickest safe door benefits from the threat of prison for anyone who breaks into the safe, and the most effective tool for preventing a cop from hitting you with a rubber hose is the existence of a judge who can send that cop to prison for abusing your civil rights.
But what do you do if you already live under tyranny? The rule of law is a great defense, but cryptography alone can't bring about the rule of law. What is the role of technology in this foundational struggle?
My technopolitics faction – the faction associated with the Electronic Frontier Foundation, where I've worked for a quarter-century – has an answer: the role of encryption is to provide a measure of privacy and security that is best used to organize political struggles to demand the rule of law and respect for human rights. Encryption isn't proof against rubber hoses, but it is effective against many other forms of state repression, and it can provide a technical edge for those engaged in a political struggle.
Another faction – the faction most associated with bitcoin and subsequent cryptocurrency projects – rejects the role of the state altogether, and seeks to replace states (and state-regulated institutions like courts and banks) with mathematics. Rather than asking courts to interpret contracts, we can put our trust in self-executing "smart contracts," and rather than asking banks to safeguard our financial integrity, we can use cryptographic software to ensure that money only moves when the person it belongs to tells it to.
This has many problems. Smart contracts are slow, expensive, and unreliable. The number of people who understand contracts is small, the number of people who understand the software that embodies smart contracts is likewise small, and the Venn intersection of the two is more of a sphincter. What's more, there is irreducible ambiguity in all but the simplest of contracts, which means that even a "self-executing" contract ends up relying on a human adjudicator (an "oracle") who can be bribed or intimidated into cheating:
And when it comes to transactions, crypto proves to be unwieldy, expensive and complex, so that nearly all crypto users end up directing an intermediary (like Coinbase) to hold and move their cryptographic assets for them. The upshot is that cryptocurrency mostly replaces banks – imperfect, but heavily regulated and insured – with unregulated tech platforms with murky ownership and often defective security procedures, who may or may not be insured (or even locatable) in the event of a collapse or a breach. Consequently, cryptocurrency has become a scam magnet of unprecedented and unstoppable power, and hardly a day goes by without people being ripped off in the most ghastly ways imaginable:
https://www.web3isgoinggreat.com/
For bitcoin maxis and other anti-state cypherpunks, this is just a skill issue. Anyone who doesn't understand how to manage their own keys and turns to a platform to hold and move their crypto is getting what they deserve. As the maxim goes, "Not your keys, not your wallet," which is cypherpunkspeak for "caveat emptor."
That's where the wrench attacks come in. Because if you are in possession of keys that can be used to irreversibly and instantaneously steal large sums of money and move it to jurisdictions where the perpetrators are beyond any legal or physical recourse (e.g. North Korea), then there is a massive incentive for your adversaries to kidnap you and hit you with a wrench or a rubber hose.
That's precisely what's going on. People with substantial cryptocurrency holdings face grave personal danger, and the physical attacks on their person grow bolder, more violent, and more sadistic by the day:
As crypto critic David Rosenthal writes, this problem is even worse than it seems at first blush:
https://blog.dshr.org/2026/05/wrench-attacks.html
For one thing, cryptocurrencies depend on "public ledgers" that indelibly, publicly record every transaction in the network. Cryptocurrency is nothing without these ledgers, and they have to be immutable and public to work. This is very bad news for anyone who relies on anonymity as their defense against physical attacks.
That's because "reidentification attacks" (where an anonymous person in a dataset is positively identified) get easier to perform over time. You might be represented in a database of hospital prescribing activities by a random number, and that number might be hard to associate with your real identity…at first. But with every subsequent release of data – whether in the form of an anonymized data-set or a breach – it gets easier to cross-reference the facts associated with your record with other facts from other records, such that a detailed, identifying picture of you emerges one fact at a time.
For example, if the taxi company you use suffers a breach that reveals journeys associated with every doctor's appointment at the hospital, now an attacker can pick out the home or work address of the single person who visited the hospital just before you received your prescription. The longer an "anonymized" data-set sits around in public view, the easier it gets to de-anonymize it:
Combine the fact that permanent ledgers make it progressively easier to identify people whom you can torture into revealing their crypto keys with the irreversible, instantaneous nature of crypto transfers and you get some very juicy targets indeed. "Not your keys, not your wallet" means it's "not anyone else's problem" when you get robbed. You can't ask the bank to interdict or reverse the transaction.
Rosenthal provides a litany of the escalating security measures crypto holders are turning to as this problem goes progressively more dangerous and terrifying. There's the guy who splits his keys up in four physical vaults at four separate locations, whose management is instructed to make him wait a minimum of seven days when he asks to retrieve them. Despite all this, he keeps his identity secret:
Rosenthal quotes Nicholas Weaver, who asks what kind of "internet of money" bitcoin can be if it can't be safely stored on a computer connected to the actual internet:
https://doi.org/10.1145/3208095
But an equally valid question is, what kind of escape from tyranny is it that requires you to hide your identity at all times lest you be snatched off the street and brutally tortured? What kind of "liberty" requires you to spend $860,000 armoring your two top execs' personal vehicles to protect them from gunfire and light artillery?
It costs $6.2m/year to protect Coinbase's CEO – "more than the combined amount that JPMorgan Chase & Co., Goldman Sachs Group Inc. and Nvidia Corp. spent on their respective CEOs":
Crypto true believers exhort one another to "HODL" (hold on for dear life). Selling your crypto during downturns is considered a moral failing. But now, crypto holders – especially those who manage their own keys – are literally holding on for dear life, as they are hunted by crime syndicates and state actors alike.
It's a good reminder of how badly crypto has failed on its own terms, delivering its biggest users into an existence of fear and physical peril that rivals the plight of even the most hunted dissidents in the most repressive societies. Worse: as cryptocurrency lobbyists have fused crypto with the world's largest and most corrupt governments (especially the Trump regime), crypto now has all the exposure to state coercion that made banks so unsuitable, but without the (inconstant, insufficient) protections offered by traditional banking.
And that's before we talk about the energy consumption problems, the scams enabled by crypto, and the rampant human trafficking that those scams necessitate:
People in my technopolitical faction have a saying of our own: "'Crypto' means cryptography." Cryptography plays a hugely important role in protecting people from crime and state repression. It is no substitute for the rule of law and democracy, but it remains a key tool for securing and defending both:
Cryptocurrency, on the other hand? That's the worst of all worlds.
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:
Text of tweet under the cut because it is loooong.
But... Stochastic Parrots.
Timnit Gebru was fired from Google in December 2020 for refusing to retract a research paper, and every single warning that paper made about large language models has now happened at a scale the industry spent 4 years trying to make people forget about.
Her name is Timnit Gebru.
She co-led the Ethical AI team at Google. She co-wrote a paper called "On the Dangers of Stochastic Parrots" with Emily Bender at the University of Washington and two other researchers. The paper was 14 pages long. It was submitted to a top AI ethics conference. And it was the reason Google decided that one of the most senior Black women in AI research could no longer work there.
The story Google told publicly was that she resigned. The story she told, confirmed by 2,695 of her colleagues in an open letter, was that she was fired by email while on vacation because she refused to either retract the paper or remove her name from it.
The paper had not even been published yet.
Here is what she actually wrote, and why every prediction inside it has now come true.
The first warning was about scale itself. Bender and Gebru argued that training ever-larger models on ever-larger scrapes of the internet would produce systems that appeared fluent but had no actual understanding of language. They called these systems stochastic parrots because they would repeat patterns from training data with statistical confidence and zero comprehension. The paper predicted that this apparent intelligence would fool both users and developers into trusting outputs that were structurally incapable of being reliable.
This was 2020. GPT-3 had just come out. The paper predicted the hallucination problem before anyone had a word for it.
The second warning was about bias amplification. The paper documented in detail that internet-scale training data contains systematic overrepresentation of dominant viewpoints and underrepresentation of marginalized ones. The models would not just absorb this bias. They would amplify it, because the optimization process rewards confident outputs, and confidence in language patterns tracks frequency in the training set.
The prediction was that hiring tools built on these models would discriminate against women. That healthcare triage tools would underperform on Black patients. That loan approval systems would entrench inequality while presenting their decisions as neutral algorithmic judgment.
Every one of those things has now been documented in deployment.
Amazon's hiring algorithm penalized resumes that contained the word "women" in any context. Healthcare risk scoring algorithms used by major US hospitals were found to systematically underestimate the medical needs of Black patients. Apple Card's credit algorithm gave wives credit lines 10x lower than their husbands for the same financial profile.
The third warning was about environmental cost. The paper calculated that training a single large language model produced emissions equivalent to the lifetime output of 5 cars. The prediction was that the race to scale would create an environmental footprint that would eventually rival entire industries.
In 2024, Google's emissions were up 48% from 2019, and the company explicitly blamed AI infrastructure. Microsoft's were up 29%, same reason. Both companies have now quietly abandoned the climate commitments they were publicly celebrating the year Gebru was fired.
The fourth warning was about documentation. The paper argued that the training datasets being assembled were too large for anyone to actually audit. Nobody at Google, OpenAI, Meta, or any other lab could tell you with confidence what was in the data their models were trained on. This was not a temporary problem to be solved later. It was a permanent feature of the approach.
In 2023, researchers discovered that the LAION-5B dataset, used to train Stable Diffusion and other major image models, contained thousands of images of child sexual abuse material. The companies that had trained on the dataset had no way of knowing. The paper predicted that category of failure 3 years before it was found.
The fifth warning was the one Google cared about most.
Bender and Gebru argued that the deployment of these systems would centralize linguistic and cultural power in the hands of the small number of companies that could afford to train them. The internet would become a place where the dominant voice was a statistical average of dominant voices, presented as a neutral assistant. Languages underrepresented in the training data would degrade over time as more web content was generated by these systems and fed back into the next training run.
This is now happening in real time. A 2024 study found that 57% of new web content in English is AI-generated or AI-assisted. Researchers studying low-resource languages have documented active degradation in translation quality, because the synthetic content fed back into training is itself worse in those languages.
The paper Google fired her for predicted the model collapse problem before model collapse had a name.
The mechanism behind why this all happened is the part of her work that nobody quotes.
Gebru's argument was not that AI is dangerous in some abstract sci-fi sense. Her argument was that AI is dangerous in a very specific structural sense. The technology was being built by a small group of researchers who shared similar backgrounds, worked at similar companies, and were rewarded for shipping products faster than competitors. The incentive structure made it impossible for safety, ethics, and bias concerns to slow anything down. Anyone inside the system who raised those concerns was either ignored, sidelined, or removed.
She was making that argument from inside Google.
Then Google proved her right by removing her.
The team Google had built to make sure their AI was safe was dismantled in 90 days because they did the job they had been hired to do. Margaret Mitchell, the other co-lead of the Ethical AI team, was fired two months after Gebru for searching through her own emails for evidence of how Gebru had been treated.
Gebru did not stop. She founded DAIR, the Distributed AI Research Institute, in 2021. The mission is to do AI research outside the control of the companies that have a financial interest in not hearing the answers.
Every prediction in the Stochastic Parrots paper has now been validated by deployment. Hallucinations are an industry-wide problem the largest labs cannot solve. Bias amplification has been documented in hiring, healthcare, lending, and criminal justice. Environmental costs are larger than entire small countries. Training data audits remain impossible. Model collapse is an active research crisis at every major lab.
The question worth sitting with is the one almost no one in the industry will say out loud.
Every researcher with the technical credibility to call out these problems watched what happened to her in December 2020 and made a calculation about their own career. The number of people willing to speak publicly about safety and ethics issues inside the major AI labs collapsed after that firing and has not recovered.
The researcher Google fired for warning about exactly what is now happening was right.
The company that fired her is now the second-largest deployer of the technology she warned about.
And the people inside that company who agree with her are not allowed to say so.
"This week I discovered the same pattern, executed by Google. Google Chrome is reaching into users' machines and writing a 4 GB on-device AI model file to disk without asking."
Google Chrome is downloading a 4 GB Gemini Nano model onto users' machines without consent, with no opt-in, no opt-out short of enterprise t
pretty sure I did the chrome//flags thing a while ago, but also i switched to firefox, which is not without the occasional bullshit, but is vastly less bullshitty than chrome.
This is why I treat genai "features" like the invasive blackberry bushes they are: cut, root, burn, and vigilantly watch for new shoots to uproot. I'm 54 years old and the world got by fine without genai for most of my lifetime.
tags via@KKglinka #psa#having read the article#it's not clickbait#chrome is reaching#across all chromium browsers#to link a prepatory structure#this malware packet#will therefore occur#with all chromium browsers#it has nothing to do#with the actual ai interface#instead chrome is either#using your personal computer#as part of a cloud server#the way bitcoin malware works#or it's recording your own#actions on the computer#with a continuously active#background module#either way#that's malware#a 4gig trojan virus
The only ways to make the deletion stick are to disable Chrome's Al features through chrome://flags or enterprise policy tooling that home users do not generally have, or to uninstall Chrome entirely
...
Adding the file took zero clicks. Removing it requires (a) discovering the file exists, (b) understanding what it is, (c) navigating into a hidden user profile path, (d) deleting it (and on Windows, also clearing the read-only attribute first), and (e) accepting that Chrome will silently re-download it on next eligible window unless the user also navigates chrome://flags, enterprise policy, or platform-specific configuration tooling to disable the underlying Chrome Al feature. None of those steps is documented in the place a normal user looks - none of them is even hinted at in default Chrome.
I will vote for any candidate who promises to go scorched fucking earth on every tech company. Break every single one of them up into companies based around a single product and then split those in thirds. Weaponize existing antitrust laws to the hilt and pass the most draconian versions of them ever seen on this planet. Nationalize google search specifically. Pass consumer privacy protections strict enough to kill the data harvesting industry for good. Make all of these fuckers go bankrupt for this rent-seeking shit
My next book is The Reverse Centaur's Guide to Life After AI, out next month. Pre-order it now, including as a DRM-free audiobook or ebook, at my Kickstarter, and help me continue to prove that DRM-free isn't just the right way to reach an audience, it's also the best way to reach them.
I've written before about the futility of "voting with your wallet." Billionaires love it when you try to vote with your wallet, because while billionaires only represent 0.00004% of the population, their wallets are 100,000 times larger than average, which means that when we vote with wallets, a billionaire's vote counts 100,000 times more than yours:
The idea of voting with your wallet is fundamentally antiprogressive, and not only because wallet-voting favors the wealthy. The ideological basis for voting with your wallet is the belief that politics are slow and unresponsive, while markets dynamically optimize for human wellbeing. By voting with your wallet, you are supposedly injecting information about your preferences and dispreferences into a vast, distributed computer we call "the market," which uses "demand signals" to decide how we live our lives.
This belief is incompatible with the idea of politics – that is, the idea that our lives can be shaped by representative democracy, deliberation, and/or solidarity. It's a nihilistic view that insists that the only nice things we can have are the things that "the market" chooses for us. If "the market" doesn't decide to swap out fossil fuels for cleantech, then that's that – any attempt to draw down our carbon emissions through regulation will only "distort the market." If you're roasting in a drought, drowning in a flood, or being incinerated by a wildfire, your only move is to go shopping and hope that by buying a Tesla, you will emit a "demand signal" that "tips the market equilibrium" to "not killing you and everyone you love."
Shopping isn't politics. Politics are politics, and shopping is shopping.
This isn't to say shopping can't improve your life! I am a materialist, and having nice things is nice. If there's a lovely independent coffee shop in your neighborhood where the baristas are treated well and the coffee is delicious and the vibes are impeccable, then by all means, get your coffee there. If you love the staff and selections at your neighborhood indie bookstore, then you should buy your books there. If you love the discourse on Mastodon or Bluesky and find yourself feeling sick and angry when you use Twitter or Facebook, then ditch the legacy social media and take up residence in the Fediverse and/or Atmosphere.
But don't kid yourself that this is politics. No matter how indie your coffee, books and social media, your consumption choices will not have a material impact on Starbucks, Amazon or Twitter. Going vegan won't make the meat industry treat animals better. Taking the bus won't induce improvements to your town's public transit network.
Having nice things is nice, and the more nice things you have – good food, good health, good books, good coffee, good social media and good transit – the more space and energy you'll have to devote to politics.
But what about boycotts? Surely the Montgomery bus boycott, the anti-Apartheid boycott, the California grape boycott and the BDS movement were politics, right?
They sure were. But they weren't shopping. The Montgomery bus boycott lasted 382 days, during which time organizers worked with bus riders, cab drivers, the UAW and community groups to provide material and legal support and alternatives like car pools, all while communicating about their specific demands. After 382 days, the courts ruled in their favor, their demands were met, and Montgomery's buses desegregated:
That wasn't "shopping." The bus boycott didn't consist of a bunch of individual choices to walk to work, repeatedly made by a city full of Black people and their allies. The shopping part was the least important part of the whole matter, and the meaningful part of the shopping was never individual. If the boycott was nothing more than shopping, it would have broken as soon as individual people found themselves unable to convince their bosses to tolerate their late, sweaty arrival at work, day after day. The boycott worked because it was politics.
And because the boycott was politics, it left behind a movement: the boycott brought people into solidarity with each other, and when they comprehensively defeated their political adversary – National City Lines – they went on to form the backbone of the civil rights movement, going from strength to strength.
Of course, shopping is part of a boycott. It's the individual part that each participant in the boycott undertakes. But without the collective, organized part, shopping is no way to effect change.
Is voting politics? Well, sure, but voting is to politics as shopping is to boycotts. For several decades now, most voters have been asked to chose the lesser of two evils (and now they're asked to choose the significantly lesser of two evils). Voting can change things, when there's something good to vote for, or something very bad to vote against, and when lots of people show up at the polls.
But to make voting effective, you have to do politics. You have to get involved in the primary races that select the candidate. You have to go to candidates' meetings and ask tough questions. You have to ring doorbells for your chosen candidate, volunteer to take your neighbors to the polls and volunteer to defend the polls from chuds and ICE fascists. The part of voting that takes place in the booth is the least important part of politics.
It's obvious why we might prefer to substitute voting or shopping for politics: they're activities you do alone. You don't have to find anyone else to do them with you. You don't have to convince anyone else to do them with you. You don't have to argue about them or justify them. They are zipless fucks, a source of satisfaction without connection, compromise or complication.
Of course, that's also why voting and shopping make a poor substitute for politics. All the retail therapy in the world can't lift your spirits the way that solidarity and community will. Doing politics creates solidaristic ties with the people around you, who might help you if you lose your job and can't buy groceries, or break your leg and can't get to the grocery store, or if ICE fascists try to kidnap you while you're out shopping.
Solidarity gets you through times of no money way better than money gets you through times of no solidarity – just ask the psycho billionaires who wanted Doug Rushkoff to invent a system of bomb-collars that would keep their post-apocalyptic mercenaries from whacking them and stealing their bunkers:
Last weekend, I walked through a crowd of tens of thousands of coked-up fascists in central London on my way to meet up with 250,000 comrades marching for an end to genocide in Palestine and a new British social compact based on mutual aid, pluralism, and care. Walking through those flag-draped chuds was incredibly demoralizing:
But when I got off the tube at South Kensington and found there were so many of us we were backed up all the way from the every street entrance to the bottom of the escalators, my morale surged. Hours later, when we all reached Pall Mall together, I was ready to take on the world. That's what politics does for you: it makes you feel like you belong to a polity and that together, you can really change the world.
Politics runs on solidarity, but shopping destroys it. Individual consumption choices don't change the world, but if you've been convinced that the only way to change the world is by voting with your wallet then when the world stays terrible, you can only conclude that your friends and neighbors have ruined by things by voting (shopping) wrong.
In politics, we build bonds of mutual regard and understanding that we use to navigate our differences. But when you vote with your wallet, all that's left is the endless policing of your allies' consumption choices, endless scolding for their failure to leave Twitter, or give up meat, or eschew chatbots. Shopping for change ends up replacing politics with petty snooping and endless sniping and attempts to bully or shame people into consuming different things.
If "the personal is political," then every political disappointment in your life is down to your friends' personal defects. If you let yourself get tricked into organizing your life around "living your politics" – that is, giving up on nice things in the hope that this will make politics change, and then getting mad at people who consume different things from you – then you will end up sucked into the stupidest fights imaginable with the people you need to get along with in order to do politics.
Once again, this isn't to say that you shouldn't choose to have nice things. Buy stuff you like, shop at places you like. And when circumstances allow all of us to start making consumption choices in unison – as when Comrades Trump and Putin stage an orgy of demand-destruction for fossil fuels, catapulting the world into the Gretacene – then by all means, take the win. That is one of the rare instances in which we can do political change with consumption!
And there definitely are times where a single individual can intervene in the system in a powerful way that really fucks up the worst actors in our society:
These usually involve using technology to "move fast and break things," which is fine, actually! It's fine to move fast and break things belonging to Elon Musk, Mark Zuckerberg or some other monster. Indeed, it's practically a moral imperative:
But even in those highly leveraged, highly individualized opportunities to make a dent in the universe, you'll make a bigger dent, and have more fun, if you do it as politics, with a big group of people, in bonds of solidarity.
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:
I’m blindsided by authors using ai in their works. how can readers and writers tell if the writing is ai generated?
I’m gonna assume writers know whether or not their own works are ai because they either write them themselves or have ai write for them.
but as for readers (or writers who read other writers’ works), no, you can’t tell unless the writer themself says their works are ai generated. anything else is witch hunt, speculations and possibly wrongful accusations — all of which harm the writing community as much as ai does, if not more.
so if at any point you think an untagged work is ai and if that bothers you, quietly click away. but you can never know for sure based on vibes. because everything ai writes, a human writer does. that’s what ai was trained on and what it was trained to mimic.
I’ve already talked more about this here, here, here. and more on my other blog @writingdose here and here.
You can notice certain telltale signs in some of the writing, such as short sentence stacking and usage of "not x not y but z" structures. But you have to be familiar with AI writing styles to be able to notice that.
I’ve been writing “not x, not y, but z” way before gen ai became a thing. I’ve read works that have “not x, not y, but z” in them, and I’ve read those works way before gen ai became a thing. I’ve also been using em dash way before gen ai became a thing, and I’ve seen em dash used in so many written works way before gen ai became a thing. I know for a fact some human writers actually prefer short sentence stacking too.
every “ai telltale” is something humans write before, otherwise ai wouldn’t have been able to mimic it in the first place. because it needs human-made works to mimic on.
when I say ai witch hunt, speculations and accusations harm the writing community as much as ai does, if not more, “not x, not y, but z” and em dash are one of the main things I’m talking about.
As I saw someone say recently, when you start declaring "obvious tells," from punctuation to sentence styles, to be proof of AI, what you're actually spotting is trace amounts of the original source material.
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