HAPPY PRIDE MONTH!! ESPECIALLY TO MY FELLOW BLACK ACE, ARO, AND AROACE SIBLINGS!!!
Also happy pride month definitely to ones we don't talk about enough!
Happy to micro labels, to contradictory labels, to unlabeled siblings!
Happy to ones who don't get represented enough which is my queer Black and POC siblings.
Happy to ones going through recent spikes of bullshit I'm starting to see in a few platforms here: bisexual, trans men, and others I haven't mentioned yet that's happening? A very happy pride to you, I'm so sorry for what y'all going through and here for you.
not every mutual fits neatly into an archetypal medievalism but there are some mutuals that im like yeah addressing you as “my liege” would come strangely naturally
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.
roald dahl was antisemitic and misogynistic. george orwell was openly homophobic. edgar allan poe married his 13 year old cousin. dr seuss cheated on his wife (and was racist as well as antisemitic!). hp lovecraft was racist as fuck.
anyways they’re fucking dead it’s not like you’re enabling their behaviors in the afterlife or something. then again I think they bleed into the books so uh keep an eye out for that
the difference between these old white guys and jk rowling is that the former group is all dead. jk rowling is alive and using your money to oppress trans people
to get real for a sec, which i know like nobody likes to read but whatever i wanna say it. to any of my white followers especially. a whitey-to-whitey moment
i have seen too much of the whole ...white people telling on themselves (especially in fandom, public spaces) and it just coming off like...? looking for forgiveness? acceptance? validation? like they want "aww its ok" like no. it wasnt okay that you did that or thought that or said that 😭 and people hurt by you don't owe it to you to tell you it's okay for YOU the ONE white individual that for some reason feels they should be excused
because now you're "one of the good ones" because you're a Little tumblr-leftist (which the flavor of focuses almost solely on queer issues with White queer people in mind if we are being honest)
well for one thing: if you say "i don't see color" with sincerity you're not one of the good ones i'll tell you that much
if you shut down people of color, especially Black or Indigenous people, or even just people lacking full access to white privilege trying to talk about racism, colorism, colonial mindsets, anti-blackness, etc. in your fandom at all, you're not one of the good ones
if you join a group fandom mob to show an obvious-racist what for! but then don't show up or even think stuff like "y'all are just blowing this out of proportion..." "it's not really like that" "i've never seen that happen" when the people same people targeted by that easy-to-spot-racist in your fandom feel ostracized, unwelcome, and talked over still when the smoke clears. you're not one of the good ones
god i hate how normalized diet culture and shit like bmi and calories are. bmi is based on eugenics. calories are a measurement of how much energy something gives u and not at all of how much weight or fat ull gain. diets have been proven to be harmful and ultimately unhelpful in actually losing weight. fatness has been largely proven to not be inherently unhealthy and doesnt inherently cause health issues.
if anyone has more good links to add on then please do and if anyone knows more on this stuff than me then dont hesitate to correct me!
The BMI was invented by Adolphe Quetelet, the 19th century statistician who invented phrenologist anthropometry. He wasn't just a eugenicist, he was one of the founding fathers of racist pseudoscience. Please do not listen to anything he has to say about your body.
“And get this: While epidemiologists use BMI to calculate national obesity rates (nearly 35 percent for adults and 18 percent for kids), the distinctions can be arbitrary. In 1998, the National Institutes of Health lowered the overweight threshold from 27.8 to 25—branding roughly 29 million Americans as fat overnight—to match international guidelines. But critics noted that those guidelines were drafted in part by the International Obesity Task Force, whose two principal funders were companies making weight loss drugs.”