The "Have you been here?" blog is the best gimmick blog because every post is like "Have you been to this remote convenience store in northeastern Latvia?" and the results are 99.9% no's but then you open the notes and there's 3-5 Latvians losing their absolute minds
Millions upon millions of posts, and tumblr has not produced a single critique of plant-based diets that isn't indistinguishable from violently insecure cope.
idk i always kind of roll my eyes at all those posts that are like âpeople used to be ugly in moviesâ likeâŚ.. wellâď¸i donât think thatâs true. i think male actors have always had more leeway to look a bit imperfect. weâve had average/weird looking male actors in every generation, including this one. but people have always needed to be âhotâ for movies. and they did crazy shit for it!! marlene dietrich getting teeth extracted to hollow out her cheeks, carole lombard undergoing her (non-cosmetic) facial reconstruction without anesthesia because they thought it would look better, etc ad infinitum. do you know the kinds of diets they had women on to keep them skinnyâŚ. not to mention beauty standards for women of color tryna be in movies. like thereâs a reason the three biggest black actresses of classic hollywood decades were josephine baker, lena horne, and dorothy dandridge: all pretty lightskinned with smaller mouths and noses -> approximating whiteness (no shade i love these women sm). i think the difference Today is that there are simply way more procedures you can get done since cosmetic medicine has evolved so much. so people can change more of their face with better and more reliable results than they could in the 1930s. point being that people have definitely always needed to be conventionally attractive to be in movies but given the physical scope of what could be conceivably changed about your appearance there was simply more diversity in like facial structure and features
haha yeah man female dogs are bitchy and female horses are bitchy and female cats are bitchy and female ferrets are bitchy and female rats are bitchy and female guinea pigs are bitchy and female birds are bitchy and female rabbits are bitchy and female everything is bitchy. lol! isnât it so funny how you think that! isnât it so silly that this is your perception of literally every species! haha! iâm sure thereâs nothing to unpack there! iâm sure youâre 100% accurate and unbiased in your conclusion that female animals are just inherently evil! iâm sure you have very normal thoughts about human women too! teehee! isnât everyone laughing! isnât it so goofy!
It's often the most male-catered to and even misogynistic media that feature some of the deepest and borderline homoerotic relationships between men. But that is to be expected. People think that patriarchy enabling homoaffectionate relationships between men is a bug, but it's actually a feature. True love can only come from a place of respect, and men often don't respect nor esteem women. They don't see women as capable of intellect, or strength, or imagination or even humanity. Those attributes are reserved to men. Which is why men's true love is often reserved for other men while women are just props to them. That's why it's easier for a lot of male writers to create stories about supposedly heterosexual male characters having the most world-shattering (accidentally romantic) bonds with other men, but not with women that they're canonically attracted to. It's because they can't fathom their male protagonist being so deeply affected by a woman's character, since women are to be lusted for and kept as props for their little domestic fantasies but never truly respected or admired as individuals. The ancient greeks were more honest about this stuff because they understood that patriarchy and male/male affection went hand in hand.
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.
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