autism tests are so funny. I'm extremely literal most of the time, but people don't tell me that generally, so I'm inclined to answer disagree. because I'm taking the statement too literally
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.
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21). Association for Computing Machinery, New York, NY, USA, 610–623. https://doi.org/10.1145/3442188.3445922
the other day i saw a tiktok of a woman talking about how her hyper-militant abusive parents would sometimes punish her by “taking away her name” and referring to her as a prisoner number. genuinely terrible stuff, obviously. but i skimmed the comments and. listen. i truly DO NOT mean to dunk too hard on this person, like they could be a kid or something, but.
just. breathtaking. imagine if your primary reference for the concept of the un-personing of prisoners was (check notes) a book series about owls.
This is why it's important to Include stuff like this in fiction, especially ya fiction. It can be a lot of sheltered and/or indoctrinated children, in the case of a lot of rural "Christians", first introduction to these types of concepts in a way they can understand.
I don't think there's anything weird or shameful about it. Knowledge is knowledge, regardless of where it came from.
I was once listening to one of the ten billion animorphs podcasts out there, with two hosts, one who'd read Animorphs as a kid and one who was reading it for the first time as an adult. For those who don't know, Animorphs is a war story in which a handful of children have to secretly hold off an alien invasion until the "good" aliens arrive to save Earth. It starts off with fairly clear-cut Bad Species of aliens and Good Species of aliens but as the series goes on it becomes clear that there is no such thing as a good, clean or glorious war, that a clean Good Side and a clean Bad Side is usually propoganda, that heroism is a matter of circumstance and that war will chew up and spit out even the victorious; there are no winners in war, just the side that lost less.
It's a lot, for books aimed at eleven year olds who want to read about kids turning into fun animals.
On the podcast, the two (American) hosts happened to get onto the topic of the post-9/11 Iraq War and their reactions to it. They were both children at the time and as such could not be expected to have particularly nuanced views of US military policy. The person who hadn't read Animorphs was unsurprised by the declaration of war; that's what you did. Someone attacks America, America goes to war. That's how a country protects itself, through military revenge. The Animorphs fan, about the same age, had been devastated and against the war from the start. War was a Big Deal and, while sometimes unavoidable, should be a last resort; a lot of people were going to die, and a lot more were going to get hurt, and no matter how the war shook out it was still going to be horrible. They attributed this perspective, of course, to the series that had taught them about the horrors endemic to war in an engaging way at such a young age -- to Animorphs.
When I get blood samples at work sometimes they’re still warm from being imminently inside the patient’s veins and my hands are always cold because all the labs Ive work in are in the basement and they keep it kinda cold for whatever reason (and I’m also just a chilly kid).
And I clutch the little warm tubes of blood and feel this sick person warming my hands and I think about how kind you might be and how I wish I could hold your hand and how badly, how really really badly, I want you to get better and stay warm and hold someone’s hand again.
And anyway sometimes it’s better to not think so vividly about the people I’m doing tests for. I’m a good little cog in a vast machine of people all trying to heal and cure, and my cog feels so fucking small sometimes. But I hope the blood I prepare for you helps you breathe better and laugh and wake up feeling well rested.
We’ve never met but you warmed my hands and I want you to know I love you and I’m rooting for you.
I have over 40k notes on this and it’s the most wonderful thing I could have hoped would get tumblr-famous. I wish you all comfort and compassion every day
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