If my page suddenly stops posting one day, know that my sister has died. I will never forget the people who saw her bleeding, suffering, and begging for help, yet chose silence and continued scrolling.
I feel completely broken and deeply ashamed begging strangers for help every day, because this suffering has turned me into a sister forced to sacrifice even her dignity just to keep her family alive.
I want nothing from this world except seeing my sister walk and laugh again. Please donate so I can buy medicine and bandages instead of expired drugs and torn clothes to clean her wound.
i have a friend in his 50s who doesn't give a fuck about pokemon so we showed him random ones and the way he responded to them made me feel like i was doing flashcards with a very serious baby
heroine of a gothic novel (me) in a long diaphanous nightgown (oversized boygenius t-shirt and no pants) holds her candlestick (3 year old iphone that gets concerningly hot when the flashlight is on) aloft to light the shadowy corridors of a sprawling manor house (modest 2 bedroom apartment) in search of the source of a mysterious, dreadful warbling noise that pierces the night (dog’s water bowl fountain clogged with kibble)
I've kept this blog mostly as a faceless drawing account, but I feel like at this point I should talk more about art related things I believe in. right now, I've been more and more annoyed by the way artists approach merch and their reactions to criticism
First of all, and I'll be blunt: the entitlement towards cheap global south labor from indie artists is insane. You can place whatever marginalized identity in your shop, it does not give you the free pass to hire manufacturers that refuse to inform you about the wages and labor protections of their workers. If the only way that you can do x product is like that, then you don't deserve to have x product as a merch option.
Enamel pins, makeshift plushies, acrylic stands, shitty tshirts and apparel... why is our community filled with dropshipping slop whos production involves labor abuses and environmental damage? why are we constantly playing dumb to these manufacturers being shady and then getting surprised they get in labor scandals? why are we coddling artists that its ok to exploit others for profit because a smol bean who just has no choice or responsibility ever?
If you aren't making your merch by hand or making sure the people making it for you are being paid properly, then you should get another job.
they won't tell you this in therapy but sometimes the best way to stop catastrophizing/anxiety is to interrupt your spiraling with "girl what the hell are you talking about"
i set my name to Paul Enis in the app because then the stickers they put on my grocery deliveries would say P. ENIS. it was like this for months, but now all the sudden they say PAUL E. instead
i can't have that. i won't let that happen. what they could have never predicted is that a beautiful woman named Peni Sausage is about to log on
I enjoy a joke about fucked up German fairy tales as much as the next nerd, but it's genuinely striking how often the source for the really fucked up stuff turns out to be "yeah, this is only in the Brothers Grimm version and doesn't appear in any extant oral tradition, and we're like 80% sure they added it themselves". To a large extent it's not German fairy tales that are fucked up, it's two specific German dudes.
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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