what world do people imagine their fursonae exist inside of. I love how undefined it is. some people are like "this is my sona Hryàxe, a hybrid dragon with ice powers who has been alive through three astral ages and toppled many kingdoms of men" and someone else is like "this is my sona he's an arctic fox named blurt and hes a barista in Toronto who loves to play mahjong." And then blurt and hryàxe are lovingly having sex on a tropical beach somewhere. and I'm like how'd they get to the beach
Having a crush on someone when you have fucked up kinks is so embarrassing bc you’re just like “wow they’re so cute I want them to get off on the way I cry while they fuck me”
Also highly recommend looking thru the reblogs on this one cause so many people add their own versions, and the dom / sadist versions especially just fuckin tickle me. I love y’all bunch of perverts
If you hate homura you will be sent to my communist labor camp where you will be forced to produce madoka magica cosplays and watch madoka magica until you can do deeper than surface level character analysis
idk who needs to hear this, but your partner should be making your life better and easier than it is on your own. if your partner is making your life harder, they're constantly stressing you out, if they're not showing up for you? babe you gotta leave, life is so short and we only get one, so don't spend it with someone who makes you feel bad about your life, you deserve so much better than that
talking of surplus labor one time my father and I calculated how much money he moved at his warehouse job with one single shipment and it was several times his monthly salary. the technical base of capitalism is so advanced that a warehouse peon can generate the value necessary to sustain themselves for an entire month in mere hours, having the rest consitute surplus labor that can be reinvented to economic development exclusively. the monumental amount of value generated by a mature capitalist economy is large enough to sustain its entire population multiple times over, but at the same time, the parasitism of the small class that owns all of this surplus labor is monumental enough on its own that those workers are barely paid enough to take care of their own health and their working hours cannot be reduced a couple of hours without sending shocks through the entire capital chain. never has the degree of separation between producer and production been so large, never has the chasm between the classes been so ample
of course he didn't produce the commodities being moved, his job was to put them into various boxes and shipments. this warehouse moved textiles, as part of a large international textile company. this one single shipment that could have fed him for multiple months came from an unnamed place in Bangladesh. Even greater is the dispossession and alienation suffered by the proletariat of the parasatized nations, paid for cents on the hundredth euro. Were those textile workers paid fairly, the parasitism of the capital owners wouldn't be sustainable at its current magnitude, but the products would still be sold and the materials would still be paid and the supply would still be more than enough. and the price or production wouldn't even rise that much or at all.
sometimes when I get mad online I have remind myself that the coolest and most reasonable friend I have doesn’t know who dril is and asked me to explain what the acronym “MCU” stands for, because she spends most of her free time watching documentaries about industrial disasters with her girlfriend and going to quarries to collect rocks together. a better world is possible and it’s out there right now
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.
In case you just skimmed the post above and missed it, I want to reiterate and highlight Gebru's current position as Executive Director of Distributed AI Research Institute. If you're curious about what AI technology might look like when not applied in the horrifically unethical and damaging way it's currently applied, please check them out.
If we want to have nice things, decentralization is essential, and if we want to decentralize, we need to have our eyes on things that are beyond the scope of the current Big Tech narrative.
The Distributed AI Research Institute is a globally distributed organization of academics, activists, and engineers conducting community-roo
germansingergirl
blakekathryn
pixelskylines
prguitarman
titsay
