scientists in the 1990s, putting a Get More Purple gene attached to a harmless plant virus into an already purple petunia: please get more purple
the petunia, sensing an apparent honest to god Get More Purple Disease, using the previously undiscovered RNAi antiviral ability to shut down all other purple genes along with it just in case: you put VIRUS in petunia? you infect her with the More Purple?? oh! oh! her children shall bloom white! jail for mother, jail for mother for One Thousand Years!!!!
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 do think it’s interesting how the novel Dracula is meant to be a modern setting from its perspective. It’s very much that genre of story about an ancient fantasy archetype finding itself in a modern setting, complete with the rules-lawyering that often comes with modern parodies (that isn’t to say the stories of Olde didn’t have fun with loopholes either though).
Except Dracula is a story that plays itself straight. The vampire himself is not stupid. He’s possibly the oldest vampire of all which means he upgraded from animal instinct and mindless echoes of past memories to someone who’s regained his critical thinking skills. The story begins because he’s already adapted to how the modern world works now by hiring a solicitor who understands modern laws.
He knows now that he doesn’t have to march into London with an army like he used to; He can just buy property and the laws of London are forced to respect that. Similarly he’s already experimented in and discovered loopholes to vampire rules and limitations; Vampires are bound by the permission of owners so he simply uses his solicitor to buy and own a bunch of properties. If he needs to be invited in, Dracula hypnotizes someone to let him in.
Vampires need to return to their grave every dusk/dawn (whichever comes sooner), which causes their coffin to act as an anchor that limits how far from it they can travel? Dracula simply rations the earth of his grave into fifty coffins and spreads them across London so his range becomes exponentially larger.
All of these things make the story almost come across as a deconstruction and it might just be! It’s just that Dracula the novel became such a trendsetter that people nowadays see it as playing things fully straight. It almost feels as if the novel is written with the idea that readers have a basic understanding of vampires and their rules, so part of the thrill comes in the revelation of how the titular vampire is working around these rules. Likewise I’ve heard it used to be a trope in English literature for a traveler to visit some foreign land with a monster and escape by going home. But here the foreign aspect of the story is just the first (and final) arc; The monster’s plan hinges on coming to the UK itself!
So yeah. Dracula isn’t stupid and he reflects the idea that people of the past had just as common sense as the rest of us, they just had access to less/inaccurate knowledge and things worked differently back then. Dracula would be like… That bit of someone showing a medieval peasant a meme as they comprehend it perfectly and aren’t even wowed by the Doritos. If Dracula was set in the 21st century he’d probably understand social media well enough to become an influencer if he wanted to, though the issue of being invisible in cameras wouldn’t help.
I have this idea for a video game called Are You Out There? where two players control two different alien civilizations and the goal of the game is to invent spaceflight and then manage to find one another in a ginormous universe. You can try to leave signs for each other to find, or send out probes and radio waves, or colonize many systems so you're a bigger target, but its hard because the universe is really big.
man you really gotta give it up for skin, the best organ ever
what's keeping out practically every disease, infection, and contamination? skin
what's responsible for 90% of cooling you down when it's hot outside? skin
what can absorb radiation from the goddamn sun and somehow you only get hotter for it? skin
what organ can you decorate with paint, cosmetics, tattoos, and also use as a to-do list when you've got your wrist and a sharpie? SKIN BABEYYYY
practically every organ is annoying in some way. the human body is cursed. every bone and muscle is built spitefully and your immune system is halfway trying to kill you. but skin? skin is trying its very best and it's doing a great job of it. skin is healing up after the most fucked up stuff imaginable and growing back twice as strong because it loves you and wants to protect you. this is not a shitpost i just genuinely want more people to take a second to appreciate something about themselves they've always taken for granted
talk shit about acne all you want, but that infection would have been a death sentence if skin hadn't taken the hit for you so be nice
The best part of that video is that the owner found the ORIGINAL plush later on the beach and took another video with it after their grandmother stitched it back up
