swinging at the bees nest here but a criticism of trans women i've never really understood is that they just "take the idea of womanhood and co opt it and run with it based on their own whims." like ummm yeah? that's kinda what i did too. i wasn't handed some objective universal definition of womanhood that i faithfully carried out at every turn with no objection. i built my own understanding of what being a woman meant based on the women around me and the women i admire and the things that felt authentic to me and the things i rejected or embraced. like we all inherit ideas about womanhood and then negotiate with them. otherwise we'd all genuinely think the only "real women" left were those fucking trad wives
can we please acknowledge that tma/tme is a meaningful distinction? a justice of the US supreme court is openly saying that it's legal to discriminate against trans women because the same laws do not target trans men. the transmisogyny is what makes it permissible. your transphobia is fine as long as it has carveouts for men.
Fascinating parallel here. This incapacity of the legal framework to protect groups who are at the intersection of two protected groups (trans women in this case, black women in the original) even while protecting both groups individually is Crenshaw's original reason for coining the word "intersectionality", which has since gone through the wringer.
This Juneteenth I want people to know and remember Alfred Irving who was finally freed from chattel slavery in 1942 and Mae Louis Miller who was finally freed from peonage slavery in 1963.
Slavery was not "200 years ago," the last living former enslaved african american passed away in 2014 at 70 years old.
Chattell slavery in America did not go away, it adapted. It became peonage, sharecropping, vagrancy laws, "chain gangs" or convict leasing, and continues into the modern day prison system and the systematic incarceration of black and brown people and expanded to forced detention of immigrants in modern day America.
Do not believe the common narrative that the emancipation proclamation freed every slave. There are many more who were unable to tell their stories out of shame or fear of harm.
I reccomend that people read Slavery by Another Name by Douglas A. Blackmon or watch the 90 minute documentary / film adaptation
If nothing else please remember the names of the two people that American society wants you to forget.
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.
The minister says the situation in Cuba is "extremely tense", as a US-led blockade of oil to the country causes widespread power cuts.
Fucking sickening, heartbreaking, disgusting. Hospitals, ambulances, water pumps, so much infrastructure shut down and so many lives hurt and lost because of it, all of this at the whim of the world's most brutal empire because they can't stand to have their will resisted so close to their shores by a society that has chosen time and time again to stand proud for the dream of a better world, a world without this blood-soaked hegemony.
when i was a kid i was so mad all the time bc i thought someday i'd have to be somebody's wife i didn't know it was optional. is everybody reminding the young girls in their lives that it's optional.
is wanting to be a wife and mother a requirement for being a woman?
why might OP be annoyed with replies assuming that this post is about being aroace or transmasc if a woman doesn’t want to be a wife or mother?
are there reasons unrelated to sexuality and romantic interest that might make a woman not want to be a wife or mother?
are there reasons unrelated to gender identity and expression that might make a woman not want to be a wife and mother?
core concept: what is gender essentialism?
is it gender essentialism to imply that all women inherently want to be wives and mothers? could this be what OP is critiquing?
look at the notes OP responds to. is it gender essentialism to imply that being a wife and mother is so affixed to womanhood that to not want to be those things means you’re incapable of sexual/romantic feelings, or not a woman?
what trait are you perpetuating when you assume that women who do not want to be wives and mothers must be aroace or trans? is it gender essentialism?
it turns out that making big positive changes in your life is great but it also forces you to reckon with the years you spent not really living and makes you feel like you're carrying the corpse of a 20 year old girl on your back
