alright I've got to do some quick math to explain attitudes towards AI to my boss.
we're looking to create an AI policy, and when we were talking about this, my boss (older millennial) was genuinely shocked to hear that younger people do not (seem) to view AI positively (a la the recent commencement speakers being booed)
please rb for larger sample size!
Question 1/3
What is your age, and do you feel AI is a net positive or net negative in our lives today?
If I walked into a museum, even one I paid money for, even one I happened to pay and exorbitant rate for at that, and I realized or even suspected for a fraction of a second that it had used the automated bias machine to fabricate lies for me to read?
I would immediately leave, no further questions. And I would make a concerted effort to tell everyone I know and spread it as far as possible that this was the practice of this institution. I would be standing at the doors telling school groups as they entered. It would become my sole mission to personally see to the downfall of that place, by my own hand if I could manage it.
I honestly cannot think of a concept that has inflamed and enraged me more than this has. And if this were to happen I would in earnest wish for the untimely demise of whatever upper management decided that this was an acceptable let alone good decision.
alright I've got to do some quick math to explain attitudes towards AI to my boss.
we're looking to create an AI policy, and when we were talking about this, my boss (older millennial) was genuinely shocked to hear that younger people do not (seem) to view AI positively (a la the recent commencement speakers being booed)
please rb for larger sample size!
Question 1/3
What is your age, and do you feel AI is a net positive or net negative in our lives today?
there’s this term i coined in my friendgroup i call “the charizard effect” and it can apply to anything and everything, but it was born from me explaining my feelings about the pokemon charizard. the term is basically about how overexposure to something be it by corporate shilling or fandom prominence drives me away from really enjoying something bc i’m exposed to it so much against my will i become tired of it. it came to me bc i was ranting about how tpci does not, and cannot stop reinventing charizard, and how it is popular and obtusely included in almost every region, merch, etc in every way possible and it’s highly commodified.
i dont dislike the pokemon charizard, in fact i really like its X form, but i am exposed to so much charizard in my pokemon consumption that i cant be bothered to care for it in any more than in passing. this applies to a bunch of other stuff i’d otherwise be ok with, but i always just call this aversion phenomena “the charizard effect”
making this term has done numbers for me being able to concisely express how i feel abt something. like. its not charizard’s fault i feel this way, im sure i’d feel normal abt it if it was stripped of all this over commodification, but i cannot. hence the name
i stopped giving a shit about "legit" purchases of digital products after i spent $80 on the entire Dark Horse collection of Trigun/Trigun Maximum ebook mangas, learning that I only got access to reading them through a proprietary website ereader function, couldn't download them, and couldn't get a refund, and then literally only a year later, getting an e-mail stating that Dark Horse was shutting down that part of their company and I wouldn't even be able to read them anymore. Fuck that
Pirate shit. Don't feel bad for it. It's not "your fault" that artists, independent or otherwise, can't make a living. You downloading an album or ebook for free isn't the cause of the problem. The cause is capitalism, plain and simple, and pirating is a lucky loophole that companies are still trying to stomp out.
i think we should be ridiculing them more for this. you don't get to try and go all "queer website" when your staff likes to go on nuking sprees targeting the trans fem users
would be remiss not to mention that the rainbow notably straight up just removed the trans flag colors from it. like they’re gone. it’s the progress flag minus the trans flag colors.
GLaDOS voice: "Would you like to see some artwork I generated? I've heard from other test subjects that AI-generated artwork produces an uncanny valley response in human viewers because they can't perceive it as fully real. They've told me that it looks absolutely hideous to them, that they can't imagine anything more disgusting than AI art. But, well I've been practicing and wanted your honest opinion. Feel free to let me know how ugly you find this by ranking it on a scale from 'vomit-inducing' to 'eye-bleeding'."
A robotic arm lowers from the ceiling holding a hand mirror up to Chell's face
One of the useful liberal tactics that has led to their continued crushing of the left over the last decade that has been under discussed is the tactic of blaming cooption/recuperation and sheepdogging on the "far left"
For example:
Black Americans put forth a simple demand that we, and ideally everybody else, not be executed by armed members of the state without trial. In a word: equality. Liberals can't answer that demand because it is antithetical to their project of maintaining the dictatorship of the bourgeoisie. Instead they give a bunch of shit that nobody asked for which annoys everyone and the right wing of liberalism is able to capitalize on that annoyance to generate a backlash to a demand that was never met.
Instead of dealing with the scourge of killer cops, the democrats kneeled in Kente cloth. This did nothing for Black people and only served to fuel the grievance of white people. Instead of dealing with the effects of redlining, the liberal hegemony got rid of Aunt Jemima. This did nothing for Black people and only served to fuel the grievance of white people. Instead of dismantling white supremacy, the liberals coopted, recuperated, and sheepdogged every movement they could while stoking dissatisfaction and grievance that was then blamed on the "far left"
In essence, the "far left" was blamed for the tactics the liberals used to suppress us.
This pattern can be seen across what are now being attacked by the leadership of the democrat party as "groups"
Whether it be women, queer people, black people, the dreaded "Latinx" (remember that "discourse"?), trans people, disabled people, or any other marginalized group: we asked for equality. Liberals can't answer that demand because it is antithetical to their project of maintaining the dictatorship of the bourgeoisie. Instead they give a bunch of shit that nobody asked for which annoys everyone and the right wing of liberalism is able to capitalize on the annoyance to generate a backlash to a demand that was never met.
As a result, the left wing of capital can convince itself that it needs to move even further right to win because the spectacle of them fighting against the actual left caused them to do stupid shit that nobody liked that was unpopular and they blame on the actual left whereas the right wing of capital can point at the stupid shit that nobody liked as proof that they are being oppressed by the mere existence of the stupid shit that nobody liked.
To a certain extent I get that sexual orientation is socially constructed but I need you all to understand that lesbians still exist and no we will not fuck men or be attracted to men. Im sorry but its the exact same shit as "well gender isnt real so trans people are actually regressive"
And I do notice how this line of logic is used to devalue the existence of lesbians and not nearly as much for other sexualities.
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.
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