for the record im not technially 100% anti-AI, in the sense that its a broad category of tech being lumped under one umbrella term so it feels over-zealous to say i hate all of it all the time forever. but i also think trying to discuss what it actually IS good for is difficult right now when i cant take one step without something trying to convince me to use chatgpt to summarize my life and speed up my hobbies and turn my friends into chatbots and optimize my life into oblivion. i am certain there is nuance to the topic but can we stop cramming the square peg into the round hole before you start trying to sell me on the legitimate benefits of the square peg. please.
Neural Nets have existed for decades and are genuinely useful. It's a form of AI that recognizes patterns, and can do stuff like identify cancer cells, tell whether an egg is fertilized or not, detect fraud, and optimize routes.
Those are Expert Systems, tuned to do exactly one thing. If you (say) ask a medical expert system a question about financial law, it's useless. The autopilot that flies a 787 has no idea how to drive a truck on the freeway. A Coulter Counter is excellent at identifying lymphocytes in a blood sample but can't predict the next card in a blackjack game.
And so on.
The problem with so-called generalized AI (AGI) is that we don't have that yet. It doesn't exist. It MIGHT some day, but AGI has been "10 years away" since the 1980s. The goals keep moving as we learn more about how people and machines process data.
But the current crop of AI techbros have been selling generative Large Language Model AI (LLM) as AGI because generative systems do a good job of faking it. There's no actual thought going on, merely the illusion of thought via predicting the next word in a sentence accurately.
If you let a human toddler listen to 800 hours of YouTube car influencer videos, that toddler might end up sounding like a car influencer. They'd parrot horsepower numbers and 0 to 60 times, mention EV range and MSRP numbers.
But they wouldn't understand any of it.
That's ChatGPT.
And yeah, it's worse than useless because it doesn't even know when it's lying or hallucinating. It just babbles convincingly until you stop it.
But for techbros to make money selling that as "AI"? It's the perfect scam, especially if you don't understand how it works.
i simply don't think nonbinary people should have to see having our gender/s respected and acknowledged as a luxury. i think it's frankly sickening that that's the state of things tbh.
sorry i just think that the fact that popular binary jokes are stuff like "god isn't it so much better to exist as my gender, i didn't even realize how incredibly depressed i was until i transitioned, my life is so much more rich and colorful now that im out and transitioning :)" and popular nonbinary jokes are stuff like "i think i might be nonbinary but that doesn't matter because i have real adult concerns and no one would respect it anyways lol" and that's. normal and funny and cool. yeah. clenches teeth so hard they turn to dust
happy nonbinary people's day. treat every nonbinary person you meet with as much compassion and respect as you possibly can or ill grind YOUR teeth to dust.
once tried to share a poem with my uncle about how awful it feels to be reduced to binary ideas of gender as a nonbinary trans dude who doesn't pass. hes a very supportive cis gay man who'd never intentionally disrespect my identity and even he kind of laughed at me when i shared it thinking it was like. supposed to be lighthearted or a joke in a way? even from one of the most compassionate people i know i feel like my identity and a massive chunk of my dysphoria comes off as a funny concept/nonserious. idk
I've been thinking about June Egbert and the title page poem. It makes me emotional. This kinda kicked my ass but I think I'll like it once I stop looking at it for a minute
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.
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