every Porsche vehicle in Russia stopped working, for reasons that are unclear but apparently related to their anti-theft satellite connection, and I need everyone to understand that this is bad.
it's bad if this was intentional and a company can just decide to brick your car remotely whenever they like. it's bad if this was accidental and your car needs a constant connection to a satellite or it stops functioning.
it's bad even though it's Porsche because if they can do it to rich people with luxury cars they can do it to anyone. it's bad even though it's Russia because a) not every Russian is responsible for the acts of the Russian government and b) if they can do it in Russia they can do it anywhere.
this is not a "lol Russia get pwned" moment, this is another example of corporations ending the concept of ownership.
STAY SAFE!! [ID: the Gilbert Baker pride flag with the words “Happy pride to all those who are unable to celebrate openly and safely. You are loved and seen!” in all-caps black text over it. /end ID]
also we have GOT to collectively come to terms with the fact that me or any other stranger online disliking or even making fun of something you like is not saying “no fun allowed” “no one can ever enjoy this” you have simply got to grow a spine and be able to like the shit you like. you don’t even have to defend it! like 90% of the media i really enjoy is divisive and half my friends actively hate it. i really don’t give a fuck though because i like it. you can write whatever you want! you’re allowed! even if it’s MY least favorite genre or style of writing and i have active distaste for it!
hey, what do you know about distances in jane austen's time? if i'm reading it right, in emma it says that mrs weston lives half a mile away from emma. and then when emma is thinking about how nice it is to have harriet, it mentions that emma walked alone to mrs weston's house once and found walking that far alone unpleasant! is that area like, super hilly? muddy? or is this meant to indicate that emma is easily bored?
Firstly, it turns out England used a different set of measurements until 1826, when they changed to Imperial. However, a mile is the same length in both, so 1.61 km. Then I put this into a calculator:
It says about 16 minutes total. I think we can assume it's measured as she walks and not as the bird flies. I set the walk at beginner because it doesn't seem like Emma walks a ton otherwise.
Now is 16 minutes a long walk or is it justified for her to be bored? I don't know. I get bored taking my dog on a 16 minute walk alone so I usually listen to music or a podcast. Let us consult the masses. Keep in mind that it's not like Emma has anyone at home to really talk to, she's managing her father, not chatting with him. Also, this is the same walk every day, probably along lanes of farmland, so it's not a scenic walk or a hike.
Walking 16 minutes alone back and forth without music or a cellphone:
Boring, I relate to Emma not wanting to go it alone
So ok look. The point is not the flared leg by itself. These cannot be yoga pants. These are, and you have to understand this if you are too young to have worn them, BLUE JEANS. And this was the last years before all jeans were 70% spandex.
They were denim, and they weren't bell bottoms. They hung loose from the knee in a way that would make a wizard envious. We all walked around like we were wearing hakama. And they dragged on the ground. That was important. Ragged cuffs. If your jeans weren't so long that they had ratty cuffs, they were embarrassingly short.
And the thing about denim is that it's a twill weave and it's cotton. So not only does it hold a lot of water, it wicks. Walking around in these suckers on a wet day could get you wet to the knees even if you never stepped in a puddle.
Then you'd go inside and take off your shoes and try to avoid letting your freezing, wet, filthy pant legs touch your skin.
Aunt Laura threw out her hands and spoke with a sudden passion utterly foreign to her. "But there's only one way of loving."
"Oh no, dearest of Victorian aunties," answered Emily gaily. "There are a dozen different ways."
Younger people, one thing I want you to understand about Millenials is that, overall, our parents taught their daughters to aim for careers and employment, but they didn't teach their sons to keep house. This causes a whole lot of Situations.
when i was a kid i decided that killing people was bad therefore war was bad therefore the military was evil. and adults would tell me it's more nuanced than that and i would understand when i grew up. well i'm a grown up now and idk i still think that killing people is bad and war is bad and the military is evil
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.
I’m a costume designer and there are live bears outside the theaters on a regular basis. We have received multiple warnings. They have already gotten in to some other buildings.
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