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.
being a new warframe player is a crazy experience because you will realize you need a frumple gun to do better damage but you can only get it after farming a specific bleeblorp from Mars, and it can only be unlocked on the 3rd new moon of the year. But in order to get the purgle to process the bleeblorp u must first gain enough street cred with the local Crimbus syndicate. You will need to do 5 million missions for them to be able to rank yourself up enough to buy the purgle. Congrats you’ve ranked up enough to buy the purgle. Except you’ve only bought the blue print. You now need to farm even more components. You do all this and the frumple gun is mastery locked.
I can't stop thinking about Baby Kal'tsit and how it reframes the Blue Flashback.
Like. I've already written extensively about that, but even then, it's like... we didn't really know quite how alien Kal'tsit was. Seeing it is different from reading it.
But think about it.
Oracle wasn't Looking at A Human Child or even an android and going "no, don't be my legacy, be your own person."
They were looking at this crazy geometric machine lifeform, the epitome of 'alien nonhuman entity' in scifi. A stock archetype, even, albeit one that they've implemented quite well here.
Think about it, though.
Oracle looked at THAT and saw it and saw a life a child a person and told it to Go, Be Free, Be A Person, Don't Be An Extension Of Me.
Ad ALSO it's not like... in the sense of someone uhhhhh for a lack of a better metaphor, "letting a pet run free when you can't take care of it, hoping it'll do fine in the wild,"
Oracle comments upon waking up that they genuinely hoped Kal'tsit would live a LIFE, and be happy, as a Person.
At this point Kal'tsit wasn't even capable of speech, there is nothing about her that is outwardly human.
And oracle nonetheless sees humanity in her.
In Episode 14, the Damazti Cluster asked Kal'tsit how she had stayed herself over the many thousands of years she's lived, how she maintains continuity, and Kal'tsit answers that the reason is she has a name.
But deeper than that, fundamentally, Kal'tsit's answer is 'love.'
Love kept her going.
Love kept her her.
She doesn't SAY that, she says it's her name that did it. But it's "a name." Her name. Not her designation but her name, given to her by Oracle before they went back into stasis. A name, a real name, a signifier that she's a person, that she's real. To name her in this way was an act so full of love, carried by her into the future, love that she shared with so many in her life.
And knowing that Nothing About Kal'tsit Was Human at the start, NOTHING was, just makes it so much more powerful.
And really this is what sold me on Kal'tsit's return, yanno? Between this comic and its ramifications and the sheer care put into the reveal. The animated PV with all those artists' contributions, the clear joy of creation on an authorial end...
When we got the leaks for her alter, I felt like it was too soon. I was comparing the year of realtime between her death and revival to, say, the five years it took for them to get into Priestess's and Theresa's deal after having them haunt the narrative the entire time, the multiple years they took before showing us the Londinium arc, the buildup for Rhine Lab stuff, etc.
Arknights is a game where they spend a lot of time and effort to show us things 'out of order' as a storytelling style. For their biggest plotlines, they'll show us the outline of a story, the silhouette of an event, the beforehand and the aftermath, and generally only then will they really get into it, which allows for a phenomenally unique reading experience, regardless of the order you take it in.
And I had felt like... idk. It felt too soon. If you had asked me what could make me NOT go "it feels a little rushed doesn't it?", I could not have told you beforehand.
But now I know, because it's this.
At this point it feels to me that this isn't a story about Kal'tsit haunting the narrative after we lost her, this isn't a story of struggle to cope after she left the mortal coil before us. This is a story that uses her brief disappearance from the plot (a year of our time! probably at most six months in-universe!) to show us the shape of others' love for her. To show that her love for the world and for those around her is reciprocated. By her children, by her friends, by her sister, her colleagues, even strangers. By the authors, and by the audience.
Kal'tsit is loved.
She may not have truly found herself, but she did find love.
And I hope that once she returns, free from the weight of Priestess's imposition of legacy upon her, we'll see more of that.
I can't stop thinking about Baby Kal'tsit and how it reframes the Blue Flashback.
Like. I've already written extensively about that, but even then, it's like... we didn't really know quite how alien Kal'tsit was. Seeing it is different from reading it.
But think about it.
Oracle wasn't Looking at A Human Child or even an android and going "no, don't be my legacy, be your own person."
They were looking at this crazy geometric machine lifeform, the epitome of 'alien nonhuman entity' in scifi. A stock archetype, even, albeit one that they've implemented quite well here.
Think about it, though.
Oracle looked at THAT and saw it and saw a life a child a person and told it to Go, Be Free, Be A Person, Don't Be An Extension Of Me.
Ad ALSO it's not like... in the sense of someone uhhhhh for a lack of a better metaphor, "letting a pet run free when you can't take care of it, hoping it'll do fine in the wild,"
Oracle comments upon waking up that they genuinely hoped Kal'tsit would live a LIFE, and be happy, as a Person.
At this point Kal'tsit wasn't even capable of speech, there is nothing about her that is outwardly human.
And oracle nonetheless sees humanity in her.
In Episode 14, the Damazti Cluster asked Kal'tsit how she had stayed herself over the many thousands of years she's lived, how she maintains continuity, and Kal'tsit answers that the reason is she has a name.
But deeper than that, fundamentally, Kal'tsit's answer is 'love.'
Love kept her going.
Love kept her her.
She doesn't SAY that, she says it's her name that did it. But it's "a name." Her name. Not her designation but her name, given to her by Oracle before they went back into stasis. A name, a real name, a signifier that she's a person, that she's real. To name her in this way was an act so full of love, carried by her into the future, love that she shared with so many in her life.
And knowing that Nothing About Kal'tsit Was Human at the start, NOTHING was, just makes it so much more powerful.
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