Funny that the stereotypical cynic is an idealist who aged out of it. In my experience, the reverse is true. I was an extreme cynic as a teenager and then I noticed how profoundly limiting it was, and also that "cynics are cool and smart" was a message that was being constantly reinforced by corporate media for some reason.
#yes! cynicism reads as very juvenile to me#and yes prev often stemming from teen pain
Yeah, like I see black-pilled people on here and my default reaction isn't "oh, these must be world-weary old warriors who've lost their faith in humanity", it's "these people are in their 20s and need a hobby"
I also think that the present era has proven that authoritarian leaders don't actually want a population of wide-eyed idealists, they want a population of jaded assholes who are convinced that everyone is lying, any resistance is either a scam or doomed to failure, and nothing can ever get better.
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The father of Warhammer 40k art direction and the man that has inspired me as an artist, down to inspiring my current artstyle (and I am sure will continue to inspire me, even in death). 40k just wouldn't be 40k had he not it been for the foundation of grimdark sci-fi that he laid.
Rest in peace, John Blanche. You had a truly monolithic, formative impact on both Warhammer as an IP, and the lives of countless fans and artists around the world.
Once when I was in undergrad, someone described something as “problematic” in class and our professor was like, “That’s cool, but ‘problematic’ doesn’t really mean anything. It means that the thing you’re describing has a problem, and in and of itself that’s not bad. Art, especially, should always have problems, or else it’s not interesting and not art, either. It sounds like you’re trying to say that this is bad, but you don’t want to say ‘bad.’ Is that right?”
So from then on whenever one of us called something problematic, he would make us talk it out until we could name the “bad” thing we were hinting at. In this particular class, 7/10 it was some type of oppression, and the remainder was like, “I’m uncomfortable because this is very new/confusing/pushing boundaries that made me feel safe.”
Once we stopped calling things “problematic” and stopping at that, class got way more interesting and... we all had to say, like, “that’s racist” or “that’s misogynistic” or “ew capitalism gross” out loud, which a lot of us had never done in a classroom before. Or we had to be like, “Uhhh... I’m not sure what’s so bad?” and confront our own beliefs and that was maybe even more useful.
Anyway. Whenever I see the word problematic, I can’t help but think of this professor being like, “Good starting point, now let’s get specific.” I think when we have to commit to saying “that’s ___” it requires a lot more careful thought about the truth and impact and complexities of whatever we’re claiming. Sometimes there really is some bullshit afoot, and also sometimes it’s art, and it should be full of problems, because that’s what art is.
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The idea of “but everyone knows that” needs to stop.
I saw a post about someone chiding Millennials for not knowing about JKRowlings transphobia, and asking how it is at all possible that people can exist in the world and the internet and, you know, not know.
Which I mean, I get. It is so present in so many of my online spaces that it seems astounding that someone could simply be ignorant! It feels impossible!
But let me tell you a story:
I went on a girls trip with a bunch of friends. All of us are rather incredibly liberal and all of us are incredibly online.
One girl would not stop talking about Harry Potter.
At one point, another girl asked her why she was ok with supporting it, and she had no real clue that JK Rowling was at all transphobic. She had heard that she likes to support Lesbian causes and thought “oh ok cool!” And that was it. She was AGOG with the news and rather horrified.
I must once again emphasize that she was an incredibly online person. She’s a foodie and a restaurant blogger.
Later in the trip we were picking restaurants and I suggested one I found on Google, and she gasped at me. Actually gasped, asking how I could ever be okay picking that one.
The shock must’ve been on my face, because she then told me all of the shitty things that restaurateur does. He abuses staff. Underpays them. Fires them on a whim. Is known for being one of the worst people to his employees in the entire restaurant business on this coast.
And she was so shocked I had never heard of this. Because in her mind, I was just as online as her. And in her online world, EVERYONE knew about this guy.
So I think the moral of this story is: always approach the other person with some empathy. Even online people, even people you think MUST know about how bad people are, may not have heard. It may truly be just them being on a different sphere of the internet than you.
So be gentle, be kind when letting people know they might not have heard about the cancellation of XYZ person. Don’t assume that everyone knows all the same info as you.
By all means, let them know so they can make informed decisions, but being kind will go a lot further than attacking them for some info they might not know yet.
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i just lowered the price on a bunch of adopts i have on kofi! uwu;; if any of them catch your eye but the pricing is still too high, just reach out to me and we can chat about it.
i have a few designs in my for sale/trade folder on toyhouse too that aren't on kofi-- same deal goes for them.
and, of course, commissions are always open. i am still doing a "pay what you want" style thing, but if you aren't sure what to offer i can give guidelines on what i'd normally charge for something if it wasn't a "pay what you want" deal.
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Living in a conservative part of a blue state and watching tv during an election year is really trippy because nationally people are like oh you’re all liberals over there you don’t know what it’s like living in a conservative area but then the local attack ads are like my opponent wants to be NICE to ILLEGALS and the RADICAL TRANS AGENDA and BURN DOWN POLICE STATIONS. You should vote for ME. I will SHOOT immigrants PERSONALLY in THE STREET. I am a former NAVY SEAL. BARK BARK.
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.