Hi, I'm Henrie! This is my main blog, where I post about whatever I feel like. He/they please.
I also run a blog called @henriesgenderblender, in which I explore and illustrate a world of inverted gender norms.
Feel free to reach out and enjoy!
~Henrie Thalias đ
June is also menâs mental health month so Iâd like to drop in and say that mine is not so good. Itâs also Pride Month so Iâm going to be treating the problem with yaoi.
Let me settle the 'is it fetishism and is it bad?' debate once and for all:
Being attracted to any kind of body is normal. Fat bodies. Trans bodies. Disabled bodies. All normal. Being extra attracted to a specific kind of body is normal too. Totally normal to have a type.
Not unlearning the societal stigma attached to those kinds of bodies, the people who inhabit those bodies, and the people who fuck them, to the point where you do any of the following:
Only want to date/fuck the person in secret.
Reduce the person to the feature that you desire and ignore the rest of who they are as a person.
Expect the person to be a walking porn fantasy instead of a real person with their own sexual preferences and boundaries.
Would no longer love the person if the stigmatized aspect of their body changed.
Consider yourself superior to the person, think the person should be 'grateful' that you love their body, etc.
See the person as a temporary adventure while planning to eventually settle down with someone whose body isn't stigmatized.
Is bad and harmful and you shouldn't be dating anyone until you've worked on your shit, because this makes you a very terrible partner.
This doesn't mean you are a bad person with bad-fetishist-desires who can only desire people badly, it means you need to unlearn societal stigma so you can be a better partner to the people you desire.
unfortunately very true. Doing Better does not always mean never being upset or never being triggered or never having trouble. often Doing Better means experiencing those things and being able to keep going/cope healthily/move on. if youâre in a bubble with no sensation, if youâre numbing yourself out, thatâs not what recovering really is. it wonât help you have a happier life itâll just make your world smaller and smaller until you canât fit anywhere anymore. gotta learn to make peace with the hard stuff too, thatâs the only way to keep going
I'm still Big Mad from the animation industry AI news, and I had some thoughts I needed to get out. There are lots of more important reasons why artists shouldn't use AI, but here is my philosophical take on it.
One of the relatively small things I find annoying about people insisting that clearly gendered terms are ungendered, is that it just completely deflates any interest in analyzing how these terms are actually used in practice.
Like it's genuinely interesting that people who frequently use both "girl" and "dude" are more careful about how/when/to whom to apply the former than the latter, both because culturally "male" terms are unmarked and therefore easier to cast as "gender neutral", but also because men are more likely to react negatively to misgendering colloquialisms and how they feel about it somehow matters more!
The most fascinating trend I've seen w this topic is actually on this site, where "dude" is used in a wide variety of contexts but "girl" is most frequently used when the speaker is in some way condescending to the presumed reader.
I almost never see "girliepop" used in a non pejorative way, and the specific situations in which Tumblr users will call a man "girliepop" are particularly illustrative of the pejorative effect.
These are interesting questions!! and if we actually engage with gendered language as it is currently actually used (instead of pretending it's all neutral so we don't have to think about it) we might learn a lot about how our culture thinks about gender!!
Times That Copyright Expansion Has Historically Fucked Over Artists On An Institutional Level:
Sampling rights becoming prohibitively expensive to use by small artists
Musicians being forced to sign over sampling rights to their record company, making any benefits they would hypothetically gain moot.
The Digital Milennium Copyright Act leading to the vidmaker-stomping nightmare that is ContentID
The DMCA leading to making it harder than ever to preserve media due to the way it prohibits tinkering with any locks the megacorps put on it, meaning it's way easier for artists' hard work to end up vaulted and lost.
The way basic chord progressions and musical styles have become copyrightable thanks to various lawsuits by the Marvin Gaye estate
The fact that the artists of the past used to be able to remix; adapt and iterate on art made within 56 years of them, likely created in their lifetimes, and now artists can only do those things with art produced nearly a century ago by people long dead.
New and independent artists being crowded out of the market by megacorp-owned IPs that would be public domain (and thusly convey less of an overwhelming advantage-via-marquee-value to megacorps) if the US had its pre-1976 copyright laws.
Times That Copyright Expansion Has Actually Materially Helped Artists On An Institutional Level:
Artists' rights and copyright are two completely different concepts and the sooner we as a society can understand that the sooner we can get actual protections for individual creatives.
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.
It is funny with bigotry where people unaffected by it act like that somehow disproves it. At an old job, talked to HR about a co-worker who kept calling me a faggot, and the HR person was like "Are you sure? He's never said anything like that to me."
Gee, Hetero Jessica, he's never called you a faggot? Then I must have misheard him!
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