Alright, welp. It's come to the worst unfortunately. I am opening emergency delayed commissions, as I have to escape my house immediately (either tomorrow or in two days) :/ I will be buying plane tickets to the next state over to live with a close friend. Since I will be on the move for the next week or so and can't draw, the slots will have to be filled now and the art completed later. I hope that's okay. I appreciate anyone who can help, I really, really need it right now.
I will get back to everyone who contributes as soon as possible. I'm only able to pack one backpack so I can bring my PC but not my monitors, so once I'm there I'll try to buy a cheap one to draw all of the commissions that funded the flight and hotels
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.
It is not nearly common enough knowledge that most Native tribes in the U.S. don't actually own all of the land within their reservation. There are millions of acres of reservation land that tribes don't legally own and they have no control over how that land is used. Like, there are a lot of different concepts tied in with the land back movement, but a major one is literally just getting reservation land back into tribal ownership.
girls i know accusations of racism can occasionally be falsely leveraged against trans women in order to discredit them and this Is Bad but this does notttt actually mean that you can just ignore the racism in the community like it doesn't exist. because racism exists in all communities and lately it is Nightmarish to be a nonwhite doll. i have experienced really glaring racism in several transfem spaces and there's been this really kind of awful attitude or like idea enforced that i shouldn't talk about it because speaking up could socially murder another tgirl. and that's really bad. that's really really bad. like it's really bad that i'm hesitant to even post this because of the scrutiny we're under. and yes the fact that it is so dangerous to speak up is because of the massive amounts of surveillance & transmisogyny we're facing but that just CAN'T mean racism gets a pass, okay? BIPOC girls are not expendable and you can't keep throwing us under the bus.
but i know calls to action without any kind of instruction aren't very helpful so here are some tips that i think you can easily do yourself with your own circles:
1) Staring off simple: Look around. Are there any BIPOC girls in your friend group, or are all of you white? Why? Do you often joke about everyone in your friend group being white? Why is that funny?
What to do: Examine why there are no people of color in your group. Examine why people of color may not be comfortable enough to hang out with you & your friends. Correct these, where possible. Do some reading on anti-racism. Talk to more people.
2) Examine how you talk about people of color & racism. Are you downplaying racism in your community? Are you treating women of color as dramatic or unreasonable for bringing it up? Do you find yourself only defending white dolls, always defending them from claims of racism, but never defending BIPOC dolls? Why?
What to do: Think about why you care more about accusations of racism than protecting the women of color in your community. Make sure you're making your space safe for BIPOC girls. Speak openly and loudly about anti-racism. Hold your sisters accountable- they should be apologizing when they say or do something wrong.
3) This one might sound silly, but as we often meet each other over fandom and roleplay- how are nonwhite characters treated in your circle? Are they always aggressive, angry, or antagonistic? Do you find yourself putting nonwhite characters in more roles like cops, dictators, sex pests, etc?
Additionally: If your circle shares sexual content, is there a lot of art where there's a pale/blonde character on the bottom, and a darker character on top? Are you and your friends always drawing darker characters as more dominant, more sexually aggressive, or promiscuous, while the white/pale characters are more innocent, submissive, modest, or clueless?
What to do: Examine how the way your group approaches fandom & art with nonwhite characters in it may make people of color uncomfortable. Examine why your art may make people of color feel unsafe or awkward hanging out with you. It's not wrong to have the occasional character of color be more antagonistic or dominant, but it's a problem when this is a pattern. If it's happening All The Time, question why!
4) Be honest with yourself: Did this post make you feel defensive? Does it make you feel defensive when people say something you did was racist? Why?
If a person of color tells you your actions were racist, they trust you to improve. I don't tell people they're being racist if i think they're going to hurt me for bringing it up. I know many like me. Don't prove us wrong- take these criticisms into account and work on it. You aren't cursed to be some kind of terrible bigot forever because you messed up- panicking without action is useless. Just be sure you examine the behavior in question and work to prevent this kind of thing from happening again. Okay?
I believe all these things are easy enough to check with yourself & your friends. Please work on making yourself & your group safe for girls who aren't white.
Guys, it is 6 AM in Gaza and I still haven’t slept. I am suffering from pain and sleeplessness. Please share or donate if you can… don’t leave me alone in this nightmare.
“Imagine the universe where the price negotiation went the other way. Apple ships a 1998 OS based on BeOS instead of NeXTSTEP. The native framework is Be’s C++ kits, with a Carbon/Blue-Box-style compatibility path for existing Mac apps. Mail.app is built on BFS attributes instead of mbox files. Spotlight is just query(). The iPod’s media playback inherits the Media Kit. iOS, when it arrives a decade later, is a BeOS descendant rather than a NeXT descendant. None of this happened, and we cannot know if BeOS would have scaled to ten million developers or stalled before Mac OS 9’s discontinuation. But it is impossible to read Be’s spec sheet now without imagining it.”
— The BeBox: BeOS Hardware, Photos, and the Apple Deal That Wasn’t
we're not ever gonna make this website a safe space for black people or have a substantial black user base anymore unless y'all (non blacks obv) come to grips with the fact that y'all hold internal biases whether some of y'all be blatantly racist and hide behind your disabilities/mental illnesses queerness/transness or it's something as simple as if you see a black face on your dash you immediately swipe up
