if you are a parent, or may become one, or you are otherwise likely to arrive in the situation of caring for a child while they eat, promise me this: if a child doesn't like a certain food or food group, you will ask them WHY. and specifically, you will pay attention to either confirming or ruling out "it makes my mouth itch" or "it makes my stomach hurt," both of which are medically important info that children may not provide unprompted. which i know because this PSA has been brought to you by "i spent my entire childhood and much of my early teens eating peas and lentils while wondering why everyone else liked the Violently Itchy Mouth Sensation so much, like were they a bunch of legume masochists or something, before i finally realized that Violently Itchy Mouth Sensation was in fact a sinister demon appearing only to me, and her true demonic name was: Legume Allergy"
I know we bitch a lot about every app trying to force generative AI on us, for good reason, but have we bitched enough about how hard they push biometric logins enough??? No I do not want to do it "later," I want to never give my fingerprints to your randomass app!?!?
If you aren't playing Wisher, Theurgist, Fatalist, then you have no legitimate need for the rules of Wisher, Theurgist, Fatalist (WTF 45)
But character generation is done before one begins play (WTF 62)
And the character generation procedures for an RPG are commonly understood as part of the game rules [citation needed]
These three premises add up to a contradiction, where the only two escapes are by asserting that WTF's character creation procedures are not, in fact, rules of WTF but instead something else, perhaps setting elements or norms of play culture, or by coming to the conclusion that it is not possible to legitimately create a WTF character.
I'm also not entirely convinced that examining the structure of something qualifies as using it.
More seriously, to address the original claim, legitimacy does not derive from the text of a roleplaying game but rather from the will of the people. It cannot derive from text because nobody can know what a text actually says.
Nobilis 4: A Preview by Jenna Moran on Patreon. Join Jenna Moran's community for exclusive content and updates.
Preview and rough layout of the first hundred pages of Nob4 is up for patrons, along with boring incomplete explanation of delays and why they will likely continue! Feel free to get it from a patron or subscribe-for-$1-then-quit (I'm not sure if patreon currently charges first month immediately or not) if you feel the need.
Nobilis 4: A Preview by Jenna Moran on Patreon. Join Jenna Moran's community for exclusive content and updates.
Preview and rough layout of the first hundred pages of Nob4 is up for patrons, along with boring incomplete explanation of delays and why they will likely continue! Feel free to get it from a patron or subscribe-for-$1-then-quit (I'm not sure if patreon currently charges first month immediately or not) if you feel the need.
there are many people who like to walk into a ttrpg having invented a blorbo in their mind and have the ttrpg to give them the tools to realize that blorbo and all the best to them i am so thoroughly not one of those people. give me a playbook that prescribes a very specific narrative role. give me a pregen character i have to play as. give me a dozen big stupid tables to roll up a guy on. give me some god damn lifepaths !
text: [ “Some of you have forgotten that only three years ago you were perfectly capable of writing an essay, writing a eulogy, telling a bedtime story to a child, and it should worry you that powerful companies have convinced us we can’t do things we’ve been doing for 5000 years.” ]
And they're absolutely specifically pushing it, make no mistake. It's not just a matter of "it's there, it's convenient, so people are going to take the path of the least resistance", but it is a legitimate and concerted effort on the part of these companies to get people to outsource all these things to their models.
They're preying on insecurities to do it. Yes, you can write an essay - but can you write a good essay, they ask you. Do you not want to improve your output? Do you not want people to think of you as competent and very clever? Why go through the mortifying process of failing and failing and failing until you succeed if you can just skip the "learning" part of doing, and simply generate a ready-made product?
I'm preaching to the choir here obviously but it's a concerning thing to witness nonetheless. My kid is 6 next week and I've been teaching her that failing at things is morally neutral and in fact necessary even before the advent of AI, but it's becoming ever more important that we teach the kids that criticism and failure and discomfort aren't necessarily bad things, but just a part of the growth process.
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.
Presented here are all the wood-engraved illustrations by American illustrator, author, publisher, and master wood engraver Michael McCurdy (1942-2016) for a 1985 edition of French writer Jean Giono's (1895–1970) The Man Who Planted Trees, published in Chelsea, Vermont by Chelsea Green Publishing in 1985. This was among the first imprints to be published by Chelsea Green, which continues to publish today.
The Man Who Planted Trees, perhaps the most well known of Giono's works outside France, was first published in Vogue in 1953 under the title "The Man Who Planted Hope and Grew Happiness." The story, about a shepherd's long, singlehanded effort to re-forest a desolate valley in the foothills of the Alps near Provence, is an inspiration for ecological regeneration that has garnered international attention. In 1988, it was adapted as the Academy Award-winning animated short film of the same title by Frédéric Back, and was the basis for the 2025 comic book L'Homme qui plantait des arbres written by Florence Lebonvallet and drawn by Daniel Casanave.
Michael McCurdy attended the School of the Museum of Fine Arts in Boston and went on to earn both his BFA and MFA from Tufts University. He became a celebrated engraver and fine press printer, founding Penmaen Press in Boston in 1968, which he continued to operate in Lincoln and finally in Great Barrington, Massachusetts before closing the press in 1985.
View more posts with wood engravings by Michael McCurdy.
Oh youre "nonbinary"? Can i put you in a box please. Can i pleaseeeee put you in a box. We have two boxes and i really wanna put you in one. But dont worry. My boxes are very Woke and Nuanced. So its fine to put you in one. Pleaseeeee. Wow... youre such a bitch... not letting me put into one of two nuanced and essential necessary boxes... you obviously go into the box all those evil bitches end up in
Tweet by Ezra @ hyperprisms that reads as follows:
a lot of discussion i've seen talking about like, "how could people who survived the holocaust turn around and do this to others" reminds me of how during a talk about his book "MAUS", Art Spiegelman said regarding: israel, "suffering doesn't make people good, it just makes them suffer"
there is unfortunately no spiritual inoculation against cruelty. the holocaust was not a "teachable moment." a population can experience unimaginable and senseless inhumanity and still inflict it on others. there is nothing morally cleansing about hardship.
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