all i want for 2026 is that gigantic rancid AI bubble to finally burst in such a catastrophic way that the consequences will be so good and i'll never have to see another AI generated image ever again
I’ve said it before and I’ll say it again. Hippie church moms donating quinoa chips to my local food bank have done more for me materially than any internet idealist ever has.
People get pissed at me for being a pragmatist in my political ideals but I’ve been in the position where I was out of food right now.
And who helped me with that? Not people calling for some nebulous revolution. Not people telling me that the system was useless. Not people preaching at me to grow my own food. It was a church food bank partially funded by the state of Texas that some southern hippies donated a bunch of Whole Foods nonsense to.
And you know what? I’m sick and tired of defeatism. What can we get done right now, huh? Are you gonna accept something a bit better to help people right now or are you waiting for your perfect utopia to come to you?
Yeah, UBI is better than the quinoa chips. Sure. But right now the quinoa chips are stopping people from going hungry and if all we can do is get the food bank quinoa chips to more people, then I say so be it. That’s something. I’ll almost always take baby steps over nothing.
I'm probably never going to find it again, but there was a response to one of those "artworks we think we can make" posts that was like "Okay, go for it." Like, dead serious.
Are you going to come out of it with a Klein-level work? No. Dude was bonkers skilled. But I am here to tell you that if you've ever gone to Home Depot and shuffled through paint chips and been like "God, this is such a gorgeous color, I fucking love this color" and then immediately been like "...but I can't imagine painting a wall with it." and bought a can of soul-killing eggshell off-white or what the fuck ever, you absolutely can go pick up a $10 canvas from a craftstore and a $5 sample of that color and just hang 6 square feet of it on a wall and enjoy the fuck out of it.
For real, buds. If you see an artwork and you're like "Shit, I could have made that," that is a reminder that god can't stop you and probably neither can science.
I love that four different people on my feed scheduled this joyous person to reblog by 8am on June 1. I look forward to seeing this a dozen more times today.
She played bass on 10,000 songs, including the most-played track of the twentieth century. She was paid $55 per session. Her name never appeared on the albums.
Gold Star Studios, Los Angeles, 1964. A woman in a cardigan walks past the receptionist, a Fender Precision bass in her hand like a briefcase. She doesn’t sign autographs. She signs a timesheet.
Her name is Carol Kaye. In three hours, she will record what will become the most-played track of the twentieth century. She’ll pocket fifty-five dollars and head to another studio, on the other side of town, for the next session.
The record label will never put her name on the album.
Between 1957 and 1973, Carol Kaye took part in roughly 10,000 recording sessions. Not as the featured artist, not as a guest, but as a hired hand. She was part of an anonymous collective nicknamed The Wrecking Crew—elite studio musicians who actually played the instruments on your favorite records while the famous bands posed for promotional photos.
The work was relentless. Three albums before the day was over. Stale coffee in paper cups. No rehearsal. The charts arrived minutes before the tape rolled. If you couldn’t read a chart and nail the take in two tries, you didn’t get called for the next session.
Carol could do it on the first try.
She started playing guitar in grimy bars at fourteen because her family couldn’t pay the electric bill. Music wasn’t a romantic dream for her. It was survival. It was a job—factory work with better acoustics and lower pay.
But she was faster and sharper than almost everyone else. She corrected charts in pencil while the producer was still explaining what he wanted. In one session in 1968, she told a famous producer his arrangement sounded like a dying dog. She chose her own line. They kept her version.
That descending bass line that drives the Beach Boys’ “Wouldn’t It Be Nice”? Carol Kaye. The propulsive groove of “These Boots Are Made for Walkin’”? Carol Kaye. The acoustic-guitar intro to “La Bamba”? Carol Kaye. The iconic theme from Mission: Impossible? Carol Kaye.
She invented techniques on the spot, out of sheer necessity. When the bass sound was too muddy for AM radio, she stuck felt under the strings and used a hard pick instead of her fingers. The tone cut through the static like a blade. It became the sonic signature that defined 1960s pop.
Bassists spent years—decades—trying to crack the secret of the Beach Boys’ gear to get that sound. They were studying the wrong people. They should have been studying Carol.
She received no royalties. No residuals. No gold-record ceremony. No credit on the album sleeves. When “You’ve Lost That Lovin’ Feelin’” hit number one, Carol was already back in a studio cutting a soap jingle.
The biggest bands mimed her bass lines on TV variety shows. New York marketing departments decided a mom in classic clothes didn’t fit the rebellious-youth image they were selling. So they simply left her name off the album credits.
For thirty years, almost no one cared. The truth only began to surface in the late 1990s, when music researchers found the same union contract numbers on thousands of hit records. The very documents meant to preserve studio musicians’ anonymity betrayed them.
Think about it. Every time you heard “Good Vibrations,” “River Deep – Mountain High,” the Righteous Brothers, Nancy Sinatra, or Sonny and Cher, you were hearing Carol Kaye. She composed the soundtrack of an entire generation’s youth.
And yet the records still say nothing. She’s now over eighty. She wrote instructional books. She trained countless bassists. She is finally starting to be recognized by music historians who uncovered the truth about The Wrecking Crew.
But she never got what she deserved: her name on those albums. Credit for the music that defined an era. Recognition that those bass lines everyone associates with the “Beach Boys” were, in fact, Carol Kaye’s.
Fifty-five dollars a session. Ten thousand sessions. The most-played track of the twentieth century.
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.
If you feel lost and disconnected from yourself; if you feel like you don't know who you are and question your purpose in this life, the best thing you can do is reconnect with everything that has ever brought you joy. There are so many parts of ourselves we hide and forget because we adapt to the environment and people around us. Other people's opinions, judgements and thoughts influence us and it's so easy to get lost in them and forget what you actually think and want, who you actually are. So revisit the songs that make you feel happy and alive, pick up that hobby that always grounded you, connect to people who bring warmth to your life and indulge in anything that makes your soul feel at peace. All of these things are the essence of who you are, my love. And in order to find yourself again, go back to your roots and engage with everything you have been neglecting for so long. I can see that the light inside of you has been dimmed for a while, but it's still there, waiting for you to uncover it again.
"Sewing is a gateway drug to thinking through complex problems. It seems really simple; culturally, we make it women's work. Let me tell you: real sewing at any kind of level of proficiency is a bloody magic trick. Sewing, like mold making, involves mental frames that require one to think inside out and backwards. It requires one to work on an order of operations that is often taking into account the reverse. It's a really, really important skill, and if you learn how to sew, you're mostly on your way to carpentry and welding and sheet metal work. I'm not kidding: these are planar forms meeting under rules and conditions. And if you can make a sleeve work, I swear to God, you could build a house."
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