[id. A twitter post by @/Bennieeexyz
Jury duty letter came addressed to my cat.
Not a mistake. "Felix Martinez" - that's his full name according to his vet records.
My last name. His first name. Somehow he's a registered voter now.
Called the county clerk.
Me: My cat got summoned for jury duty.
Clerk: Is the name correct on the summons?
Me: Yes, but he's a cat.
Clerk: Is Felix Martinez a legal resident of this county?
Me: He's a legal cat.
Clerk: Sir, if the name matches our records, he needs to appear or file an exemption.
Me: He can't file anything. He has paws.
Clerk: You can file on his behalf.
Me: Under what exemption? There's no box for "is a cat."
Clerk: (pause) Check "unable to serve due to medical reasons."
Me: What's the medical reason?
Clerk: He's a cat.
Me: That's not a medical condition.
Clerk: It is if it prevents him from serving.
Sent in the form. Got rejected two weeks later.
"Insufficient documentation. Please provide medical professional's statement."
Took the letter to my vet.
Me: I need you to write that my cat can't do jury duty.
Vet: Why is your cat summoned for jury duty?
Me: Excellent question. No good answer.
Vet: This is the weirdest request I've gotten.
Me: Can you just write that he's medically unfit to serve?
Vet: On what grounds?
Me: He's a cat.
Vet: (started typing) "Patient is unable to serve due to species-related limitations including inability to speak, read, or comprehend legal proceedings."
Me: Perfect.
Sent it in. Got another rejection.
"Summons is mandatory. Failure to appear will result in contempt of court."
My roommate thought this was hilarious.
Roommate: Felix is going to jail.
Me: This is serious.
Roommate: Bring him to court. See what happens.
Decided that was actually the only option left.
Day of jury duty, put Felix in his carrier. Brought the entire paper trail of rejection letters.
Checked in at the courthouse.
Clerk: Name?
Me: Felix Martinez.
Clerk: (looked at the cat carrier) Is that Felix?
Me: Yes.
Clerk: (long stare) He's a cat.
Me: I've been saying that for six weeks.
Clerk: Why didn't you file an exemption?
Me: I filed three. All rejected.
Showed her the letters. She read through them, expression shifting from confusion to disbelief.
Clerk: Someone rejected the veterinary documentation?
Me: Twice.
Clerk: (called her supervisor over) You need to see this.
Supervisor read everything. Looked at Felix. Looked at me.
Supervisor: How did a cat get registered to vote?
Me: You tell me.
Supervisor: This is a data error.
Me: Took you six weeks to figure that out.
They dismissed Felix immediately. Apologized for the inconvenience.
Supervisor: We'll remove him from the voter registry.
Me: Appreciate it.
Supervisor: (pause) Out of curiosity, how would he have voted?
Me: Probably whatever party supports universal treats.
Got a formal apology letter a week later and a voter registration card.
For me this time. Apparently I wasn't registered, but my cat was.
Roommate: Felix committed voter fraud.
Me: Felix committed nothing. He's innocent.
Roommate: That's what they all say.
Felix is sleeping on the jury summons now.
Fitting end to his legal career.
end id]
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.
i love you trans women with big noses, i love you trans women with hooked noses, i love you trans women with wide/flat/broad noses, you are so unbelievably loved!!!!
your natural features are NOT "manly" or "masculine" or "ugly" or whatever the hell else society says!!!!
this goes double for Black, Indigenous, Arab, and South Asian trans women and other trans women of color btw <3
[do not derail. make your own post if you feel inspired]
I started posting my trans art on this day last year, and during that year, I’ve made so many friends and been part of so many awesome projects. I’m so grateful of all the people I’ve become friends with because of this amazing community!
This drawing includes some of these awesome friends, and is probably one of my bigger, more involved pieces I’ve done, I’m really proud of how it turned out!
It features (in order of top left to right): @lost_and_kinda_dumb (reddit) @starzdustkin @lilyfaewood @fenrisfawkes @milkypancakess @ms-moxxie @vviviv (bsky) @nooodlesartstyle @pennymations @maxine302 @deadeyedfae @squiretilde @lambinajam @liddimothie @komorebigold @skelotom @haarlow @nikkinoxcomics @paintedbytosia @biblicallyaccuratemoth @chloestransart @alice-arty @tigergirltail and Me!
yousharknotpass
annaxmalina
kaledo-art
andulkaphoto
discoholicmusic
2087
vaporwave
