maggot fam | also @hello-ello-ello (good omens) and occasionally @lady-macbeth-the-conknifing (globe theatre maggot) | call me nitzi | profile pic lettered by my big sib @sounds-void-fishy
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
day one after changing sheets: ok im gonna kick my bad habits i won't eat in bed or leave knives or scissors or lighters in bed I'll have only normal bed things in my bed and I'll change the sheets more often too
day two after changing sheets: oh yeah this half eaten bundt cake is sleeping with me tonight
i love how weird kids are. they make up the most bizarre stuff when left to their own devices and it's never what an adult would naively predict a kid would do in their imaginative play
my friend's 5 year old recently got a toy veterinary medicine set - it's super cool, like one of those mini play kitchens a lot of kids have, but it's set up to pretend to be a vet (it's this thing) - it has stuffed animals and things to weigh them, give them medicine, take x-rays, write on their charts, etc.
so this kid, who is five and to my knowledge has no experience in the administrative bureaucracy of modern healthcare, puts a stuffed pig named Piggy on the exam table. she pretends to draw blood from Piggy using a fake syringe, and the blood goes into a toy test tube vial that she calls "the resulter"
i'm playing with her, right, so i'm like, awesome, what are the results of Piggy's blood test? and she says "we have to send it to the scientists." so we send the vial to the scientists (put it in her bedroom) and when we get back to the vet playset i'm like awesome what did the scientists say? and she says they have not gotten back to us yet
so she rolls her eyes, exasperated, and says we have to call the scientists. she pretends to call them. apparently, they tell her that Piggy's blood test is "at the bottom of the list" and "we have to WAIT." she frowns. we wait a bit longer and call them back. they tell us it will be a while! she says we should go ask the scientists in person so we go back to her bedroom and she inquires at this imaginary lab, at which point the scientists yell at her and tell her now they will make us wait even longer!
keep in mind she is 100% directing this play. she is making all this up. she is fully in control of this game, and she has decided that what we are going to pretend is that we are dealing with this exhausting nonsense, not actually treating Piggy.
finally the blood tests come back. they are inconclusive. the scientists do not know what is wrong with Piggy. the little girl walks back to the stuffed pig on the exam table, sighs deeply, and says in a very serious voice "we can never help you."
i'm obsessed with this kid. when given complete control over a make believe scenario, instead of becoming the heroic rescuer administering effective cures, she is instead a beleaguered vet making multiple calls to an overworked lab only to be left unable to help her patient.
There's a recurring online tendency to aestheticize consensus itself. The imagined future village is full of emotionally compatible people who enjoy communal gardening, conflict resolution circles, acoustic folk music, mutual aid potlucks, and repairing bicycles together at sunset. Which is nice for the people who genuinely enjoy that lifestyle. But plenty of humans are solitary, prickly, obsessive, urban, nocturnal, sensory-seeking, technologically attached, contrarian, novelty-seeking, private, or just plain difficult. Those people do not evaporate after the revolution. They do not get Left Behind while you are Raptured into the Utopia. They become your neighbors.
I love talking with neurotypical people about my executive dysfunction because I'm like "yeah there's this invisible wall in my head that I'm incapable of getting past no matter what I do and it stops me from doing things" and they're like what the actual fuck
"Haha, I'm a procrastinator, I sometimes take several days to do an easy task!" is true but like
I am consistently late to work. Because I am sitting on my bed, picking at my legs or watching videos or just staring at the floor.
I do not want to be late to work. I do not want to be late to doctor's appointments. I do not want to be late to things I volunteered to do for fun. I do not want to leave work two hours late because I was just sat at my desk, staring at my phone, unable to convince myself to get up and leave even though my work day was over. I do not want to be sat in my car, doomscrolling for half an hour as my car loses heat to the literal freezing temperatures, because I'm just incapable of making myself move.
But I am! I am always late to things! I am so incredibly bad at getting to things on time and it's partly executive dysfunction and partly a bad sense of time and partly just. Something deeply wrong with me.
IDK I also register the 'took weeks to do an easy task' thing (I haven't cleaned my room in uh. months?) (my dirty laundry is in multiple piles) but I feel like sometimes the cutesy 'single task' thing gets paraded around and the 'oh god I'm ruining my own life because I can't fucking move' is swept under the rug.
My comfort Bollywood headcanon is that Bhaskar Bannerjee from the movie Anand lived his life, had Piku as his daughter, and after he died, he reunited with his happy-go-lucky, amazing soulmate Anand in the great beyond where they spend all their time with each other making fun of Bhasker's fuss over constipation and adorable eccentricities. Anand and Babumoshai for the win all the way.
She got the idea for the study while walking with her advisor at Stanford to discuss her thesis topic, and the paper she eventually published in the Journal of Experimental Psychology in 2014 is sharp enough that it should have ended the seated meeting on the day it came out.
She ran 4 experiments on 176 people. Same person tested twice. Once sitting, once walking. The creativity tasks were the standard ones psychologists have used for decades to measure how good a brain is at generating novel useful ideas.
81% of participants in the first experiment produced more creative ideas while walking than while sitting. In the second experiment, 88%. In the third, 100%. Every single person walked into a more creative version of themselves. On average, people generated 60% more novel useful ideas the moment their legs started moving.
The skeptical question is the obvious one. Maybe it was the fresh air. Maybe it was the scenery passing by. Maybe it was the change of environment doing the work, not the walking itself.
Oppezzo killed every one of those explanations with one experimental decision. She put people on a treadmill facing a blank wall. No scenery. No fresh air. No environmental change. Just legs moving in place while staring at white drywall. The 60% boost held.
Then she ran the experiment that closed the case completely. She took participants outside in two conditions. Half of them walked through a Stanford courtyard. The other half were pushed through the exact same courtyard in a wheelchair. Same outdoor stimulation. Same scenery passing at the same speed. The only difference was whether the legs were moving.
The walkers produced dramatically more novel high-quality ideas than the wheelchair group. The outdoors did almost nothing on its own. The walking did everything.
She also tested the opposite kind of thinking. Convergent thinking. The kind where there is one right answer and you have to narrow down to it. Word puzzles where 3 words share a hidden fourth word that connects them. The seated participants did slightly better on these. Walkers got slightly worse.
Walking is not a general intelligence enhancer. It does one specific thing. It opens up the divergent search inside your brain. The part that generates options. The part that produces unexpected connections. The part that takes a problem and finds five ways into it instead of one.
When you need to converge on the single right answer, sit down. When you need to find the answer in the first place, get up.
The mechanism is now well understood. Walking selectively activates what neuroscientists call the default mode network, the system inside your brain that runs when you are not consciously focused on anything. The DMN is where mind-wandering happens. Where memories cross-reference each other. Where ideas that have been sitting in separate folders inside your head finally bump into each other.
When you sit at a desk and force yourself to concentrate, you suppress the DMN. When you walk at a natural pace, the executive part of your brain gets just busy enough handling the walking that the DMN comes online and starts doing the work that focus was blocking.
The most useful finding in the entire paper is the one almost nobody quotes. The boost did not turn off the moment people stopped walking. Participants who walked first and then sat back down stayed elevated. Their next round of seated creativity work was still significantly better than people who had been sitting the whole time. The rest lingered for at least several minutes after the legs stopped moving.
You do not need to do creative work while walking. You need to walk before the creative work. The brain holds the state.
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 keep remembering a run of Hamlet I saw a few years ago, where the Ghost was costumed in full plate armour which was very noisy, and instead of muffling it, they had him crash across the stage, stomping so the whole set rattled, and he said all of his lines in a bellow, like he was furious with Hamlet.
And the thing that made it absolutely terrifying was that Hamlet was the only one who reacted. He was cowering, and covering his ears with both hands, and yelling to be heard over the noise.
And no one else seemed to know why he was doing that. The other actors didn't even raise their voices.
That's scary, something so loud and painful, and REAL, and the people around you don't even notice it, and think that you're the crazy one.
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