we gotta get back to torrent distribution, i just watched someone eat eight grand in bandwidth charges because they ran a direct-download piracy site with local file hosting through cloudflare. torrents were invented literally for this exact reason
i have a file or folder on my pc that i want to share with other people. let's call it gayshit.mp3
unfortunately gayshit.mp3 is 750mb and im not paying for discord nitro so i need another way to send it
i put it into qbittorrent and it makes a torrent file. this is essentially a very small file that points to gayshit.mp3 so other computers can find it. kinda like a treasure map
i send this tiny file to my friend, who loads it into qbittorrent. their computer takes a moment to find mine over the vast expanse of cyberspace and then (as long as my pc is running and the file is still where it should be), it gets copied from my hard drive to theirs
this is the cool part: if somebody else loads that tiny file, they can download it from both of us. if i'm offline but my friend is on, the third person can still get it. this also means that if two people have separate halves of the file, they can download the other half from each other. as long as some combination of people have the pieces between them, they can all have the whole thing.
crucially this does not require a server!!! you can just upload the file to a few people and as long as they keep it, it's still accessible. as long as somebody, somewhere is still connected, it's available forever. the only way it goes away is if everybody disconnects from it.
i love you archival work. i love you alphabetizing. i love you sorting. i love you reshelving. i love you document restoration. i love you shelf reading. i love you inventorying. i love you analysis. i love you archival work.
Handing the Google executive currently chained in my basement a piece of paper that reads "Shall I end your torture?" with one checkbox that reads "No" and another that reads "Maybe later."
I hope this janky gif format doesn't make anybody seasick or worse! because I do think it gives a much better sense (than a still image does) of what the Patches are like as physical objects.
the thing about being a homeschool abolition hardliner is that people always want to come to you with some bullshit sob story about how you hate disabled people because they were bullied for being autistic and homeschooling was so amazing for them. and it's just like well unfortunately homeschooling is also a widespread tool of coercive control in child abuse used by christofascists to raise kids without the tools they need to function in the world so that they will always be afraid, dependent, and unable to think critically and discern right from wrong and truth from lies. so unfortunately i do think this is a situation where in order to prevent enormous harm to untold numbers of children those bullied kids who just don't wanna go to school are going to have to suck it up. (and in reality what i always propose is a more robust system of alternative/magnet public schooling set up for kids who are basically on grade level but who DO struggle to thrive in a mainstream setting, but nobody ever wants to hear that, they just want to guilt you for not thinking anyone who ever gets picked on should just get to stay home and play vidya and do all their lessons on zoom. and it's like honestly i don't think you're having this argument in good faith bc you are very much centering hypothetical discomfort you MIGHT HYPOTHETICALLY have experienced over, again, widespread child abuse and neglect. so.)
so i watched the netflix documentary about piper rockelle yesterday and i would like to once again humbly suggest that homeschooling be permanently abolished and perhaps even criminalized
You learn that the homeschool lobby is literally the reason the USA is one of a single-digit number of countries that never ratified the UN Convention on the Rights of the Child, and you just aren’t the same since
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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