all i want for 2026 is that gigantic rancid AI bubble to finally burst in such a catastrophic way that the consequences will be so good and i'll never have to see another AI generated image ever again
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’m going to level with you. I have listened to The Devil Went Down to Georgia for most of my life. We were a country music household, this was a staple of my childhood along with Johnny Cash, Garth Brooks, and that one Chipmunks country album.
I have no idea what “Fire on the mountain run boys run/The Devil's in the house of the rising sun/Chicken in the bread pan picking out dough/Granny does your dog bite no child no” means and at this point I’m too scared to ask.
this is the key part of the song, that a lot of people miss. people have this misconception that the contest between Johnny and The Devil is about who is the better fiddle player. but it isn't. its about who is the better fiddler.
in a time before things like radios and record players, every time you heard music was because there was somebody in the room with you playing an instrument. and many, many, many social events involved dancing, which requires music. so, if you're planning any kind of gathering in the american south or appalachia, you need to find a fiddler. and the fiddler's job is to play music that everybody knows and likes and can dance to.
the mistake The Devil makes in his bet with Johnny is that he misinterprets the contest as being about technical ability, so he has this big flashy song. he plays fast and impressively with a band of demons playing unfamiliar instruments in unfamiliar rhythms. he's definitely more skilled at playing than Johnny, and thinks he has it in the bag.
but Johnny wins because the contest is about being the best fiddler. the song uses these lines mentioned above as a shorthand for saying that Johnny is playing these songs. Johnny launches into a set of the most popular songs, played well, and that's what gives him his big win. A good fiddler knows all the hits, and can read the room to know what to play next. The Devil loses because he completely fails to read the room, and doesn't know the right songs.
They're squeans I'm pretty sure! If they pop like that anyway. But the term for this kind of "symbol to refer to the general vibe of something in art" is called "Emanata" because it emanates from a person or object.
"the education system traumatizes students it deems stupid" and "the education system traumatizes students it deems gifted" are two concepts that can and should coexist.
Women stick thin and malnourished on the red carpet, and people are saying you can't point out that these women are dying because that's body shaming. Girl.
I want to tell a story to the artists and would-be artists out there.
When I was 19, I made a large oil painting of the nerd I would eventually marry. I poured all my attention and care into this painting. It's the only art I have from back then that still holds up as a work I'm proud of today.
I entered it into a judged show at the local art center. It got an honorable mention. I went to see the show with my beloved model. One of the judges came up to talk to me, and highlighted that all the judges really liked the painting. It would have placed, except, you see, the feet were incorrect. They were too wide and short, and if I just studied a bit more anatomy-
I called over my future wife, and asked her to take off her shoe. Being already very used to humoring me, she did. The judge looked at her very short, very wide little foot. Exactly as I'd lovingly rendered it. I would never edit her appearance in any way.
The judge looked me in the eye, and to his credit, he really looked like he meant it when he said "Oh I'm so sorry."
Anyways the moral of the story is that all of those anatomy books that teach you proportions are either showing you averages, or a very specific idea of an idealized body. Actual bodies are much more varied than that.
So don't forget to draw from observation, and remember that humans aren't mass produced mannequins. Delight in our variation. Because it's supposed to be there.
Allright if 20,000 people are going to post a story about a painting I did of my wife, then I am going to post the painting itself.
For those calling for justice, the same painting won a contest at my college (my university professor had met my model), and the university bought it from me and hung it up there for over a decade, square little feet and all. Then, the library underwent renovations and a librarian I am thankful for forever went through heroic measures to track me down again even though I'd gotten married and changed my name, and offered it back to me. And now it hangs in our house even though my wife doesn't ordinarily want to look at paintings of herself and I don't normally want to look at paintings I made. We both have a soft spot for this one.
Rachel clarified, before locking her twitter account because of all the death threats, that that is not what she meant at all. Entirely writing off any and all work from a recently widowed woman for a single, badly worded a statement in an interview given two days after her husband died seems just a little unfair. She's also the one who brought up a kiss for discussion in the first place, since one wasn't even scripted, because she knew the fans wanted one.
Firstly, That's great she apologized. I was not aware of that -
But what does her being widowed have to do with me not wanting to support her or being upset with her words? Her being a widow isn't the reason I was upset with her or even factor, so how is it unfair because she's a widow? That wasn't even a factor. You're saying that like I was like 'Oh, yes, the poor widow let me take money from her hands' like some cartoonish villain which with all do respect, what the absolute fuck?
Firstly, it's not any of my business to know about her personal life. I didn't t even know she had a husband or any of that. I don't know why the internet acts like I have to care about any and every celeb, director, musician - whoever's personal life. I don't. I'm not parasocial enough to go digging into the personal lives of everyone associated with a production/show/movie because again it's none of my business.
Secondly, She can have my condolences because that's sad but a tragic event, loss, or bad life event doesn't excuse bad behavior from anyone.
She apologized so I might give her work a look I might not - depends on if any of it interests me.
If you haven’t heard, today PolyCystic Ovarian Syndrome has been renamed to Polyendocrine Metabolic Ovarian Syndrome. This change reflects that this is not a reproductive “problem” but a whole body disease.
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