My TV Tropes: Brainy Brunette, Middle Child Syndrome, Bi The Way, But Not Too Bi, Saying Sound Effects, Rightly Self-Righteous, Healing Hands, Someday This Will Come In Handy, Sarcasm-Blind, Nice Guy, Motor Mouth, Sesquipedalian Loquaciousness, Lovable Nerd, Adorkable, Feminine...
At age 23, Tina Fey was working at a YMCA.
At age 23, Oprah was fired from her first reporting job.
At age 24, Stephen King was working as a janitor and living in a trailer.
At age 27, Vincent Van Gogh failed as a missionary and decided to go to art school.
At age 28, J.K. Rowling was a suicidal single parent living on welfare.
At age 28, Wayne Coyne ( from The Flaming Lips) was a fry cook.
At age 30, Harrison Ford was a carpenter.
At age 30, Martha Stewart was a stockbroker.
At age 37, Ang Lee was a stay-at-home-dad working odd jobs.
Julia Child released her first cookbook at age 39, and got her own cooking show at age 51.
Vera Wang failed to make the Olympic figure skating team, didn’t get the Editor-in-Chief position at Vogue, and designed her first dress at age 40.
Stan Lee didn’t release his first big comic book until he was 40.
Alan Rickman gave up his graphic design career to pursue acting at age 42.
Samuel L. Jackson didn’t get his first movie role until he was 46.
Morgan Freeman landed his first movie role at age 52.
Kathryn Bigelow only reached international success when she made The Hurt Locker at age 57.
Grandma Moses didn’t begin her painting career until age 76.
Louise Bourgeois didn’t become a famous artist until she was 78.
Whatever your dream is, it is not too late to achieve it. You aren’t a failure because you haven’t found fame and fortune by the age of 21. Hell, it’s okay if you don’t even know what your dream is yet. Even if you’re flipping burgers, waiting tables or answering phones today, you never know where you’ll end up tomorrow.
Never tell yourself you’re too old to make it.
“In the musical of my life after I’m long gone, my wife Vanessa is going to be the one who steps forward as the hero. Vanessa is not particularly fond of musicals—she only likes good ones. She is not effusive in her praise, or boastful. But when I looked up from that Chernow book and said “I think this is a hip-hop musical,” she didn’t laugh, or roll her eyes. She just said, “That sounds cool.” And that was all I needed to get started. As I fell in love with the idea of a love triangle between Eliza, Alexander, and Angelica, she said, “Can you have Angelica rap? That would be cool.” I am someone who is so averse to travel that I wrote a whole musical about not wanting to leave my block in Washington Heights. It was Vanessa who booked us trips and time away from New York. “You don’t get any writing done here because life keeps popping up.” Thanks to her, Hamilton was written in Mexico, Spain, Nevis, Sagaponack, St. Croix, Puerto Rico, The Dominican Republic—long trips where Vanessa would take me there and then leave me alone to write while she explored. She is my first audience, and she’s a tough audience, so I know if I impress her I’ve cleared the highest possible bar. She’ll come home from work and say, “Your king tune was stuck in my head all day—that’s probably a good sign.” This started out as a note trying to explain how my wife really is the ‘best of wives and best of women,’ but I’m trying to get at something more important—this show simply doesn’t exist without Vanessa. It’s a love letter to her.”
— Lin-Manuel Miranda on the role of his wife, Vanessa Nadal, in the creation of Hamilton. From the annotated libretto in Hamilton: The Revolution (via darrenburr)
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 just went looking for the post on X and got a message that it didn't exist
I searched for Guri Singh and got search results showing his account existed, but when I clicked on it I got a message his account did not exist
Does anyone know if he made his account private or if he got nuked by Elon? Or do I just suck at X (because I never go there)?
I spent so much of my life romanticizing the Great and Powerful Enormity of the Sea, reading about the salt and the sweat of the sailors straining to haul the sails or anchor while dreading the monsters in the cold, icy deep fathoms below…and now you tell me that a fathom is only 6 feet deep -
While I sort of get the impulse, it does always get my back up when people talk about something like Animorphs with this attitude of 'omgggg remember these books, how on EARTH were we allowed to read these books, they're so grim and dark and violent and tragic, no adults could possibly have known what they actually contained or they'd have been banned.'
And like. Allowing for the fact that there absolutely are adults who think every distressing topic ever should be banned from children's literature - they're children's books. You were allowed to read them when you were a kid because they were written for kids. Bridge to Terabithia is also a children's book. So is Where the Red Fern Grows and Old Yeller and Roll of Thunder Hear My Cry and The Giver and loads of other books that deal with heavy, difficult topics. It is appropriate and good for children to have books about these things that are tailored to their reading levels and it genuinely really bugs me when people act like they're somehow not really for kids because bad things happen in them or they end tragically.
Hi, Catholic here—the only part of the holy trinity with a human body is the Son (Jesus) so yes, but also God the Father doesn’t have a human body so no, but the Father, Son and Holy Spirit are all the same being as well so yes, but all humans including women are created in the image of God so hypothetically He has everything else too. Hope this helps.
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