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.
What if you poked me in the chest and your finger broke through like you were cracking into a rotted plank of wood and dust came out and I just crumpled into nothing and my spores got into your nose
This is so validating because the respondents in this paper are saying some of the same things I've been feeling and thinking for years.
I'm asexual. I figured that out not long after I first came across the term in high school. But figuring out my gender took a lot longer. I didn't really think about my gender identity for years, it wasn't until I was in college that I started trying to figure out what my gender was. That process took years.
I didn't really feel attached to my assigned gender, but I also didn't feel the gender dysphoria that trans people described. I didn't particularly feel like I was neither of those either. For a long time, I honestly didn't feel like any of the gender descriptions and identities I was coming across really fit. I just didn't care that much about what my actual gender was. Eventually I decided upon the agender label as that seemed the most apt. As the paper says, it's really hard to be truly without gender in this highly gendered world. Agender is a way of defining myself in a way that people who experience gender might be able to understand when "I'm just me." isn't really an acceptable answer to the "what's your gender?" question.
I don't mind being perceived as a gender, none of them are offensive to me. While I do like when I am perceived as male or at least not female, I think that more has to do with growing up female and not wanting to be pushed into traditional female roles and values than a connection or repulsion to any gender. I'm impossible to misgender because I frankly don't care.
Honestly, the biggest problem I have with my gender, is trying to define it to people. There's been a large push in recent years for asking people for their pronouns, or including pronouns in things like email signatures and surveys. And don't get me wrong, I'm not saying this is a bad thing! This is very affirming for a lot of people. But it feels like I need to pick something that doesn't quite fit. At pride, for instance, there's always pronoun buttons. But they're all she/her, he/him, they/them, she/they, he/they, it/it, xe/xir, etc etc. And that's great. I'm always glad that there are a lot of options for people. But there's never any pins for any/all pronouns. I've never picked up a free pronoun pin at pride, despite always looking, because they all feel like picking what pronouns I don't want poeple to use and the answer is that I don't care. I fround an any/all pronoun pin once at a queer museum and I cried.
I really suggest you read the paper if you haven't. Not just the article, the whole paper. This is probably the most seen I've felt in a long time.
1: The circus members are brain scans, this folder might be the abstracted members. CA_NeuralScans (Obsolete)
2: 'Abel' did exist, and the circles at the beginning of the episode were most likely Caine killing 'Abel'.
3: The circus was created on October 30th, 1996. Kinger's password is Queenie, and this confirms again that Kinger was his username.
4: Ragatha entered the circus on October 15th 2008, Scratch entered the same day but in 1999, and Caine in 1996. Interestingly enough, he is written in LISP, which has links to AutoCAD the modeling software.
5: Not much to actually say here, Caine's internals are protected by both him and bubble.
6: Assuming the GreenGrounds is the circus, Kinger was likely trying to inject the torment program (what the circus members are being tortured with??) with a daemon (automated process) in order to stop it.
Note a censored name [G_AN_]. My guess is on Grant lmao
Since these pictures are covered by Caine and Bubble, I'll write what they probably say.
"This IS a wacky word, but WRONG word!" "System selecting SAFEST option for reliability, cancel automatic selection? [Y/N]"
"Yes"
"Which backup do you want?"
"C"
"NONE Selected"
"Interpreted as: DELETE"
"Confirm deletion of current unstable program (or AI)?"
"No"
"Negative response! inverting to (?) per (?) protocols!"
"DELETE THIS MOTHER*****R, HAHAHAHAHAaaaaaaaaaaaa"
"No"
"Errored: Proceeding with ORIGINAL decision"
"Actually you're CONFUSED let me HELP, should this program EXIST [Y/N]"
"Yes"
"Override instruction error falling back to default metric"
(Kinger tries to yet again load a backup and stun Caine here)
"Are you really going to delete Caine? [Y/N]"
"YES" (This is where he accidentally hit delete)
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