do not forget the patron saint of these weeks that we celebrate ourselves proudly and openly in the streets
her name was Marsha P Johnson, and we have her to thank for so much.
remember, the first Pride was a riot, and she was one of the brave souls who endured it to help carve the path which so many of us walk today. she helped found several activist groups regarding LGBT safety and wellbeing. and she was absolutely radiant, too.
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.
really a good tool for any aspiring critic or creator to develop is the ability to ask yourself - and answer honestly! - whether your disdain for something comes from a place of (perceived) wasted potential or bitter anger that anyone else dared to create something less than perfect while you were too paralysed by dissatisfaction to even attempt to do so yourself. because if it's the latter, then you'll save yourself a lot of time and stress in the long run if you start to recognise just how much of what's holding you back is your own insecurity.
Still rocking an NFT icon in this day and age is like seeing a confederate flag on the back of someone’s truck. You lost 100 years ago, fucker, it’s over.
Yet another new study debunked the basis for the anti-trans sports bans. It was never about sports but for creating legal avenues for exclusion and abjection. This is one of the largest analyses ever conducted, involving 52 studies and 6,485 trans people. Read the study here.
I can't access the full paper, but their conclusion is right there in the abstract:
While transgender women exhibited higher lean mass than cisgender women, their physical fitness was comparable. Current evidence is mostly low certainty and has heterogenous quality but does not support theories of inherent athletic advantages for transgender women over cisgender.
call me crazy but i think public transportation should explicitly also be for actively drunk/high people. so they don’t, you know, drive under the influence.
i literally don’t care how afraid you are of drunk people. if they’re behaving well enough then there’s no reason to kick them off the bus.
if you can’t recognize it’s better for society for drunk people to have a way home that doesn’t involve them driving and potentially getting people killed then you just kind of suck actually.
At the risk of sounding anti-intellectual, I think that college should be free and also not a requirement for employment outside of highly specialized career fields
technically you can, if you don't care about degrees.
Free Harvard courses.
Free Courses from Stanford.
Free Courses from MIT.
Free courses from Yale.
Free courses from Princeton.
Free courses on Coursera.
Free Courses on EDx
Free Courses on Alison
For paid, there's The Great Courses+/Wonderium. 20$ a month for unlimited courses.
When searching, the phrases you're looking for are Massive Open Online Courses (MOOCs), or you can do a general search of say, "free online college courses."
Oh, and so you don't get surprised like I did, have an avoid: Hillsdale College is a conservative Christian site and not a valid MOOC place. Sign up with them and you will get things like THIS IS WHY THE LEFT IS TURNING YOUR KIDS TRANS AND GAY in your inbox.
Leaf your leaves on the ground (no, seriously.) They provide so much for bugs, places to lay eggs places to hibernate. This comic does a great job at showing WHY we don't see our little friends as often, because our systems and social expectations are anti-earth and anti-life. Don't eradicate your friends (maybe just that one) let the leaves lay
This Pride Month, we’re celebrating the beauty of diversity above and below the surface. The ocean is full of vibrant life in every color imaginable. It reminds us that nature thrives when everyone has space to belong.
Environmental advocacy and the LGBTQ+ rights movement share a common purpose: protecting vulnerable communities, caring for the spaces we all call home, and creating a world where we all can flourish.
Our world is brightest when people can live authentically, love freely, and be embraced for who they are. From rainbow reefs to shimmering tides, diversity makes our blue planet stronger, healthier, and more inspiring.
Here’s to protecting our ocean, uplifting every voice, and honoring the colorful communities that make this world so wonderful.
PayPal is specifically not a bank! They hold state licenses for conducting businesses and transmitting money in the United States, but they are not FDIC insured for deposit accounts.
When you see FDIC on your actual bank, that means your money is federally protected in case of bank failure. PayPal could shutter tomorrow and you'd be SOL when it comes to any funds left in your account.
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