"And Cain says, “When you split me and my brother in the womb, you did not divide us evenly. He got kindness, and I got longing. He got complacence, and I got ambition. I want to kill him sometimes. I think sometimes he wants to die.”
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 have four muffin tips for making bakery style muffins at home.
Tip number one:
Rest your batter for 15 minutes in your mixing bowl after you make it. This is gonna allow the starch molecules to swell and absorb, creating the thicker batter and the thicker batter is known for doming!
Tip number two:
Fill your muffin holes with at least six to eight tablespoons of batter. That’s like a heaping half cup okay. You want them super full so they’re gonna create that dome.
Tip number three:
Kinda goes along with tip number two. You’re only gonna fill every other hole in your muffin pan. And why we do that - that’s so the muffins that are baking can spread and dome without running into their neighbors. Because when they run into their neighbors they get like square edges but we want perfect dome circles.
Tip number four:
You’re to bake your muffins at a high temperature initially. That’s gonna be 425*F for the first seven minutes. And then keep them in the oven and lower the temperature to 350*F for the remaining bake time. Starting the muffins off at a high temperature initially allows the muffins to rise rapidly and it sets the outer surface of the muffin, producing a dome shape.
There you have it. My four muffin tips for creating bakery style muffins.
It has a huge variety of historical recipes from different regions, taken from manuscripts, transcribed and translated, and a whole section of notes about the context for the recipe. It's really fun and I AM going to try a couple of these recipes.
Look at how this lamb recipe is presented:
This is just such a fun way to learn more about historical foodways and discover traditional cuisine.
Look at all the filters you can use:
Maybe if I can forage some good blackberries this year, I can make a 300 year old recipe for blackberry wine!
I don't think you understand how important this kiss is.
Doing this in front of God too.
They sacrificed their love so that we, humans, could love each other and make decisions. They spent 6000 years teaching each other that humanity is not a toy. They died for us, they love us as much, if not more than they love each other. They are our deities, our creators. They are Love. We are children of Love.
You know when you’re learning a Romance language they always explain the subjunctive to you as if it’s something that doesn’t exist in English, but it does. It’s just not as common. It’s used to talk about hypotheticals and such. It can sound a little old fashioned sometimes because we don’t use it as much anymore.
I’d give an example but I’d rather someone else do it if they not be too troubled. If I be found guilty of laziness for this, then so be it. I shouldn’t use the subjunctive much more lest I go mad.
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