**Mutual Aid, the Commons, and the Revolutionary Abolition of Capitalism**
*Revisiting the Difference Between Mutual Aid and Charity*
https://crimethinc.com/RevolutionaryMutualAid
Much has been made of the distinction between charity and mutual aid. Charity is top-down and unidirectional, while mutual aid is supposed to be horizontal, reciprocal, and participatory. In practice, however, the majority of today’s self-described mutual aid projects remain more or less unidirectional efforts to provide goods and services to those in need.
This has contributed to a situation in which conventional non-profit organizations are rebranding themselves with the language of “mutual aid,” while some anarchists have given up on the concept entirely, fed up with a rhetoric that some say amounts to “mutual aid being good and radical, and charity being bad and conservative.”
Is there more to the distinction than this? How can we unlock the revolutionary potential of mutual aid?
Amy, She/Her, Grown, Writer, Pagan, Queer Vermonter moving to Canada. I cosplay on the clockapp, which is probably where you found me. Or you too like toads and skincare as much as I do.
I post aesthetic spooky/gothic stuff, art by friends, as well as the occasional fandom brainrot as it washes over me. I sometimes do tarot readings on other platforms and may do so on here again too, but otherwise I keep my witchy/pagan practices largely private. Aside from reblogging art of Dionysus with nice cakes.
BLM, Trans women are women, Trans men are men, everyone needs to brush up on their reading comprehension and media literacy and we might just survive all this.
If you're any kind of creative who uses linktree to direct people to your work, well...they're feeding the AI beast too now.
IMPORTANT: For any artists/writers/etc etc, using Linktree to point people to their work, from 5 July, they'll be feeding all imagery you us
There are several alternative options/apps listed in the replies. Meanwhile throwing together a quick messy list on good ol' neocities was free and took me less than 15 minutes. I'm sure you arty people can make yours look way better in barely more time <3
New music blog ICYMI! Tap in for new songs and albums from Gabe 'Nandez, Pearl & The Oysters, Rome Streetz, Patricia Brennan & Sylvie Courvoisier, Rich Jones, and more!
So I've hidden this reply, both because it's obnoxious and because I don't want the person who wrote it being harassed for it, but I need you to understand: I don't know you. We are not friends. This is not fun or cute, we are not sharing a charming joke together. You are just being an asshole.
literally that is what the post is about, I am saying people should be less eager to jump on any chance to be snarky and rude to total strangers on the internet
you said it yourself: you're looking to vent it LITERALLY ANYWHERE
so vent it somewhere private. or at least not literally aimed AT another person, a total stranger at that
Like, this reblogger sounds so insanely self centered in their reblog. notice how both options focus on how being rude would affect THEM. "B has no consequences for me so it's perfectly fine to do"
(the only reason I didn't show their username in the screenshot is because, given how self victimizing they sound in their reblog, I believe that, if I did show their username, suddenly online stuff wouldn't seem so inconsequential to them and they'd accuse me of sending harrassment their way and putting them in danger)
You said it better than I could. Of all the inane and ridiculous things I've seen in my notes because of this post, "I NEED to say fuck you to strangers or I will literally die" is certainly one of them
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.
Hey nerds! My birthday is a week from tuesday, and as usual I have a request.
If you'd like to give me a birthday present, do something artistic, take a picture of it, and send it to me. It really doesn't have to be special, it really doesn't have to be what you think of as good. It just has to be. Doodle me a cat! Work on some writing that you've been putting off! Post a snippet of some music you've been working on! Sing your favorite song loud and proud!
Just express yourself in some cool way and then send it to me. You can just send it here on tumblr, or send a link to knitmeapony at gmail and it'll get to me.
People I know, if you want to make and give me some physical art I would of course be delighted. Postcards with haikus always welcome. Drop me a line and I'll get you my mailing address.
DJ Regular: The Tumblr @djregular - Tumblr Blog | Tumgag
