It was probably some officious twit obsessed with preventing consenting adults from seeing adult content. Or it was a mistake. Doesn't matter, you can't contact them. They won't respond.
No one seems to care. It's just a blog, they say as they roll their eyes and keep scrolling (exactly the same way they do when someone makes a post that is obviously a cry out for help).
It wasn't just a blog.
It was the first place that you saw the most beautiful woman in the world smile. Those posts are gone now.
It was the place where you first wrote your best friend; all those letters were still in your inbox. They can't be retrieved.
It was where you first came out! Now there's no record of that.
It was where you poured your heart out after your grandad died. Those posts are gone like he is.
It was a record of your existence for over a decade and now it's gone. Snippets exist via the Wayback Machine, thankfully, but they didn't archive everything.
The first story you posted? The artwork you spent hours on? A conversation carried out over multiple posts? Gone, gone, gone.
Oh my dear, sweet children. The Commodore 64 came out in 1982. This was produced on a typewriter and probably mimeographed. And while it may seem funny now, it took more courage to write and distribute this than you will ever know.
Children, in the olden days fanfiction was written on a typewriter, copied and sent by snail mail. Getting one one of those letters from across the world was every bit as exciting as getting a notification that your favorite writer posted a new fic.
It’s been said before, but the fact that this fic begins with the dialogue assertion “We’re by no means setting a precedent” is endlessly amusing to me.
Diane Marchant changed all our lives. May she rest in peace.
The precedent line is especially amusing when you bear in mind that “A Fragment Out of Time” is not only the first Star Trek slashfic to be published in a widely distributed magazine: it’s believed by some to be the first slashfic of any kind to be widely published.
In 1974 it was illegal to send pornography through the USPS. So distributing fic like this via mailed newsletter was literally dangerous. And they knew it.
I don't know how to articulate this well, but I really fucking hate the way a lot of thin writers write fat characters. Like how men write women "breasting boobily" there is something so dehumanizing about how fat characters are often written. "He waddled", "he lumbered", the writer of the book I'm reading always mentions this characters "fleshy hand" when he does something with his hand. Like, we already know that he's fat. There is no need to describe everything he does as "doing it fatly".
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.
Reading it because I'm an Oz fan and I just finished the first three books in the series. I'm currently reading a paperback edition, but I'm going to have to send for the large print edition because the text is too small for my ancient eyes.
When I was but a wee baby high schooler I made this on my school issued chromebook and posted it to my og tumblr blog and I just knew in my heart it would do numbers but alas it got 1 note. (me on my other blog)
A lot of people treat it like a joke or straight up get defensive about it but most world building flaws literally come from the author having a very US/Europe centric view of the world.
This isn't just "think about where the potatoes and coffee come from in your world". This is also about fantasy worlds set in Earth too. The way JK Rowling designed her world so that England has it's own school but has Latin America, Africa and Asia share one school each is heavily influenced by her english centric view of the world around her. Or how she never took two seconds to think about how magic works in different cultures, or how she never explains why the indigenous communities of America didn't fight back against colonizers using magic, or how she decided that european colonizers "civilized" African wizards by teaching them how to use wands. Her way of seeing the rest of the world and obvious rejection to educating herself about other countries influences her world building.
The same happens with Rick Riordan. His entire magical world (which primarily deals with the GREEK gods) revolves solely around the United States. And the reason he gives is literally just "the US is the most important country currently so that's where the gods went". Camp Half-blood is literally located in NEW YORK. Demi gods from other countries are never explored or even mentioned in anyway whatsoever. The wide spread usamerican belief that they are the most important country in the world heavily influences how Rick Riordan (and most usamerican writers) do world building.
ID / TL;DW: young Black man explains the history of voodoo dolls: they originated in England, where Black people where prohibited from learning to read or write, to help witches keep track of what ailed their patients. Eg., person goes to witch and laments headache, they treat their headache and make a small doll (called "poppet"), trying to represent them as good as possible, stick a needle in its head and put it up a shelf. When they return next week, the witch takes their poppet and asks about their headache. If it's gone, they remove the needle, otherwise they know they have to treat a rather persistent headache.
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