ok but this unironically works. talk about how the working class is exploited and you can basically sell full-on marxism to your average republican if you do it right. all you have to do is avoid the words "Marx," "capitalism," "socialism," "communism," "means of production," etc - just use synonyms. say "big business" or "corporate shareholder interests" instead of "capitalists." say "a government that prioritizes the needs of the working people" instead of "socialism." it WORKS. I've DONE it. the hardest sell are usually things like social and racial equity, welfare, things like that, because people have been primed with the racist/classist idea that those things are somehow unfair - but you can get your foot in the door to getting them to buy into those too if you start with class issues. read up on your theory, make sure you REALLY understand your own ideology, because that will enable you to reword it and successfully sell it.
In my experience, you can often help sell 'welfare' stuff by appealing to self-interest with a touch of Aren't We Great.
Disability benefits: "I mean, sure, there are probably some sad sacks who are gaming the system, there always are, but hell, with the amount of taxes we pay, the government can afford a few freeloaders, right? I'd rather pay for a couple people who don't really need it than not have the system at all for if I need it, or my kids do, or whatever. I mean shit happens. What if some asshole drunk driver puts me in the hospital and it takes me a year to get back on my feet? Or Heaven forbid something permanent happens. I'll sure be glad that I can get disability then, won't I?"
UBI: "I dunno, the kind of guy who'll just sit on the couch playing Call of Duty all day if he doesn't have to work, I kinda don't want him on my job site anyway. That type is just taking up a place that you could fill with someone who'll actually get the job done, you know? You end up short-handed even though you technically have enough people because everyone else has to pick up his slack. And it'd mean that if your boss is a dick you can tell him to shove it and not worry your kids are gonna go hungry while you find a better place. We can sure as hell afford it."
Racial equity: "I've got a lot more in common with a Black guy who's just trying to get the job done than I do with some rich white asshole who thinks the sun shines out of his ass because of how much money mommy and daddy have."
having feelings that contradict your morals is soooo fucking annnoooooyyyiiingggggg. can the emotions and logic department get on the same page im tired of having to like strangle myself into being a tolerable person
#i appreciate how genuine and non-judgemental this comic feels
#like left one is not upset at right one for caring while being powerless
#and right one seems genuinely distraught and not performative
I'm glad the facial expressions are coming across accurately!
It can feel so absurd to say gosh I hope the torment maze removes some fire and rusty nails soon, but alas, sometimes that's all one can do.
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.
people who don’t watch horror movies are SO confident that they know everything there is to know about the genre. like it’s okay to not know things. it’s okay if you don’t like friday the 13th or whatever. i promise you don’t need to make an ass out of yourself on the internet about it
horror is an incredibly diverse genre, because there is potential horror in everything. it’s in nature, it’s in architecture and technology, it’s in human relationships, it’s in folklore, the past, the future, the mundane. there are horror movies from all over the world. it is straight up anti-intellectual to pretend that the handful of B slashers you’ve vaguely heard about comprise the totality of what horror has to offer. If you’re just not interested in horror, or if you dislike certain subgenres of horror, then that’s fine, you’re not obligated to like anything at all. but smugly announcing that you don’t like horror because you dislike a handful of VERY specific non-universal tropes is just as stupid as saying that you hate comedy because you don’t like adam sandler movies.
They’re so funny let’s all read the book called ‘the book that kills you’ from hit audio drama ‘do NOT under any circumstances read the book called ‘the book that kills you’’.
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“what’s your dream job” im so glad you asked. picture this. i am the lone employee of a strange and mysterious tchotchke/bookshop in the middle of nowhere, full of fun and interesting things that i am allowed to take for the low low price of free of charge. i get one, exceedingly interesting, customer per hour. i work no more than twenty hours a week and am salaried 3 million dollars
So true! I was sure I was a dog person for so long. I had even met cats that belonged to family and it liked them. Then I ended up with a cat and realized I drastically prefer them to dogs. I love my cats so much
why go to the grocery store or to a restaurant when you can just get food delivered why go to the mall when you can get same day shipping on amazon why go to the library when you have kindle why make art when there’s ai why go to the cinema when you can stay at home and watch netflix. we are in a loneliness epidemic btw
