“Because the truth is, tech doesn’t have an image problem. It doesn’t have a message problem. It has an intention problem. What’s wrong with the axe murderer who broke into my house is not that he hasn’t successfully persuaded me to buy into his narrative. What’s wrong is that he’s trying to kill me with an axe. Similarly, when you launch a product that’s designed to put millions of people out of work, block access to sources of verifiable truth, replace human creativity with slop, and lower the barriers to every sort of atrocity, the problem isn’t that you haven’t told the public a good story about those things. The problem is that you are trying to do them.”
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", 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.
AI companies are heavily invested in making themselves relevant. They want people to believe they can't do the things they have done unaided before and to make them become reliant on the AI models, so the AI models' existence is artificially justified.
I think it's so funny how we bred JOBS into dogs. I have two shih tzus and they were bred to be lap dogs. All they care about is looking cute and cuddling with people. Meanwhile my grandma has a border collie and that dog needs to feel so useful all the time, he acts like he will pass away if he doesn't have a job to do constantly
On one hand this is extremely fucking funny, but on the other hand, it really boggles my mind how many people punish their dogs for just… doing the thing they were bred to do.
Your husky isn’t “hyperactive”, it’s bred to pull sleds for 8 hours straight and you have it in a 400 sq ft yard.
Your English sheepdog isn’t “pushy”, it’s bred to herd sheep, and you have neither to space nor the herd to allow it.
Your terrier isn’t “nippy”, it’s bred to kill rats and your hamster looks a hell of a lot like one.
Your Catahoula isn’t “mean to animals”, it’s bred to hunt any and all animals smaller than it, and you didn’t acclimate it to your cat.
Your Lhasa Apso isn’t “yappy”, it’s bred to bark at any tiny noise and alert watchmen to intruders
Like Jesus Christ, if you can’t provide an environment where your dog can’t fulfill its literal life purpose, maybe?? Don’t get that dog??? And if you do, maybe know the breed characteristics so you can redirect those traits into more constructive outlets????
Both your most common doodle's parts (labra and golden) want to hunt and retrieve water birds so the best suggestion I can give y'all is congratulations on your new duck hunting hobby.
Any time someone sees Herschel and says "AWWW I want a Corgi <3" (because he is Very Cute (TM)), I immediately reply:
"Do not get a Corgi unless you have a job for it to do. They were bred to bully livestock across the hills of Wales. This is basically a Border Collie that knows he is cute enough to get away with murder. If you get one and it doesn't have a job, it will apply its livestock-bullying instincts to YOU. Herschel's job specifically is to help manage my crippling ADHD, because I don't have a bull for him to micromanage."
This gets me odd looks at the home depot but it does get the point across.
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.
btw it's so fucking stupid you can be anxious physically in your body even after you've decided mentally you don't care. I'm supposed to be in charge here
you don’t realize how important lunch is until you’re wandering around thinking about how unloveable and untalented and uniquely cursed you are and then it’s 4pm and you finally eat lunch and you go Oh. oh right.
romaincrisis
snowghoul
2087
