call me Ad! | any pronouns | admirari on ao3 My hobbies include crying about the rooftop gang and making the rooftop gang cry I mainly reblog bnha and d20 stuff!
i'm ad! i use any pronouns ^-^ i like clouds, ducks, the color yellow, and torturing my favorite characters.
this blog mainly contains stuff pertaining to bnha, d20, whatever else i like at the moment, and some ramblings! always feel free to send asks :]
im admirari on ao3, and on there i just obsess over kurogiri and the rooftop gang. i only have one real wip right now: kill 'em with kindness, kurogiri! updates are sporadic because i am overworked and underslept
tag guide:
#adwritesstuff - my writing
#ad.txt - my ramblings
#kewkk! - kill 'em with kindness, kurogiri!–related content
#ad.asks - asks i've answered
#recs to read - fic recommendations
#bnha - all reblogged bnha content
#d20 - all reblogged d20 content (+ some other dropout stuff)
#spiderverse - all reblogged spiderverse content
below the cut you can find a summary of my published works, as well as wips that will likely never see the light of day! thanks for stopping by :>
Published Works:
Kill 'Em With Kindness, Kurogiri!: In a world where the Aoyama family is safe and happy, Kurogiri, a conveniently undead 17 year old, takes the place of the UA mole (ongoing).
we are beautiful, we are doomed (series): Hizashi kills Kurogiri. Shouta finds out. (Weird extended metaphor analysis of the rooftop trio. 4k words total).
Somewhere in the Between: The (failed) first few iterations of Kurogiri, with a bit of a more hopeful ending than canon (20k words).
underneath the stars we came alive: A space vigilantes AU of the rooftop gang grieving Oboro in the least healthy of ways - by inserting him into their ship's AI (my personal favorite of my fics. 9k words).
and i'm so sick of seventeen: A Kurogiri character study spanning a decade, in which the whole fic takes place in a bathroom (5k words).
Other WIPs that have never seen the light of day and only maybe will:
Feel free to ask about any of these!
Formaldehyde Boys: Kurogiri and Tomura's first year under All for One, in which Kurogiri is not quite perfect yet and tries his best to save Tomura. It does not have a happy ending.
The Kids Are Throwing Up in the Garden: Oboro, as per canon, is taken by All for One. But instead of turning him into Kurogiri, they return him to UA with one single command: Bring Erasure Back With Him.
Erasermist fic set in their early 20s in which Kurogiri is made to assassinate Eraserhead. Fortunately, he is really, really bad at it.
Cloudmic silly secret identities hero/villain AU, in which these civilian roommates have no idea they are each other's arch-nemeses. Simultaneously enemies to lovers and friends to lovers.
Cloudnight fake dating AU that is outlined on vibes alone.
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.
If you'll notice, the co-author Schmargaret Schmitchell is writing from the aether. Because Margaret Mitchell was also a on Gebru's team at Google and was prohibited from writing the paper. She gathered evidence about Gebru's discriminatory firing and was fired as well.
writing the 1a kids for kewkk is simultaneously so exciting and so terrifying. like oh no the characterizations of them ive had in my head for years are now subject to peer review for the first time ever. gah.
