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.
This piece was a bit ambitious for me, but I did it!
I love these girls and had to draw them ^^
Does this count as mermay??? mayhap.
♡ Ko-fi ♡ BlueSky ♡ IG ♡ Twitter ♡
Ultimately, she spent 20 hours redoing the copy from scratch — and with her $100-per-hour rate, that meant her client was shelling out $2,000 for copy that likely would have ended up being far cheaper had a human just written it in the first place.
hey jsyk while hellofresh is dummy expensive and i wouldn’t reccomend it if you already know how to cook (if you’re a beginner like i was when i had it for 3 months, then it’s worth it), you should know that ALL OF THEIR RECIPES are free on their website and they all fuck hard
i will say that all the cooking instructions for veggies are pretty much the same (season with salt + pepper and roast on the top oven rack at 425F), but if it ain’t broke don’t fix it.
that being said, it also introduced me to methods i wasn’t at all expecting. i would have never thought to use cream cheese in my meat sauce, and now all my friends are constantly asking me to make my special rigatoni.
I am a person who uses Hello Fresh for realsies because it is a bit cheaper than groceries here and definitely cheaper than takeout, but we also remake the recipes when we want a specific one and don't want to wait for it to come around in the rotation again, and therefore I know The Secret Translations of Proprietary Ingredients:
"Cream sauce base" = make a roux, add milk according to how much cream sauce base you're supposed to have (ex: 4 oz cream sauce base = 4 oz milk)
"1 packet stock concentrate" = either one tablespoon Better than Bullion or, if you want to be really accurate, Savory Choice Broth Concentrate packets are available on Amazon and allegedly that's what they use
"Italian Heat Spice"/"Blackening Spice"/"Fry Seasoning"/etc.: I promise you someone on Reddit has figured out what spices they use and in what proportions for each mix, if it's not just a straight up branded mix (like McCormick Grill Mates Brown Sugar Bourbon spice mix)
"Roasted Garlic Herb Butter" (and other compound butters, like truffle butter) = they're literally Epicurean branded butters, they do not hide this, you can buy them online
"Sweet Soy Glaze"/"Ponzu sauce"/"Hoisin sauce" = literally Kikkoman brand condiments: they don't hide that, either
"Sweet Thai chili sauce" = any sweet Thai chili sauce from the grocery store will do. We use Blue Dragon or Taste of Thai because that's what we can get in our grocery store.
I really just have to summarize Thomas's entire life:
He was in a committed relationship with a male swan named Henry for 18-24 years before a female swan named Henrietta showed up and mated with Henry.
Thomas was initially jealous of the pair and attacked them, breaking 2 of the 5 eggs Henrietta had laid. However, once the remaining eggs hatched, Thomas warmed up to them and helped raise them.
Henry couldn't fly because of an injured wing, so Thomas taught the cygnets how to fly.
When they needed to reduce the goose population in the pond where Thomas and the swans lived, they dyed Thomas's feathers red so he wouldn't be separated from Henry.
Henry, Henrietta, and Thomas remained in their happy throuple for years and raised 68 cygnets before Henry died in 2009. After Henry's death, Henrietta found another swan and flew away, leaving Thomas alone.
Thomas finally met and mated with a female goose in 2011 and had his own babies. However, another goose named George stole them and raised them himself.
As Thomas grew elderly and blind, he was relocated to a wildlife center where he raised orphaned cygnets.
His caretaker at the center described him as "pretty high maintenance."
Thomas died in 2018 at the age of around 40. He had a funeral that included a small coffin and a procession that was led by a bagpiper. He was buried under the stone where Henry was buried, the two finally reunited in death.
Before and after his death, Thomas has been celebrated as an icon of the LGBTQ+ community for obvious reasons.
All peafowl display, starting from around a week old, so this isn't an indication that this little one is a male. It's just a whole species behavior for body language communication.
my mom once told me i was a ski jumper in a past life because she dreamed about a ski jumper dying in a terrible accident when she was pregnant with me, and said it was definitely true cause the ski jumper in her dream was also blonde. i never had the heart to tell her this is absolutely not something catholics can believe.
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