On a recent Free Thought Project episode, Catherine Austin Fitts spells it out: our savings and 401(k)s are quietly funding the very institutions we push back against. Her fix is simple — pull your money out of the big bankers’ grip and stop financing your own opposition.
Full Episode on Spotify, Apple Podcast & More: https://linktr.ee/tftppodcast
Reasons for hope: Lots of amazing people did a ton of work to make this fantastic, fully interactive resource available - because no matter how bleak things seem, there are millions, and millions of people doing everything they can to protect both the world and their own communities.
You can use this to view and subscribe to updates, project statuses, and for at least some of them even whole dossiers. This is an amazing resource, I highly recommend checking it out
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 just went looking for the post on X and got a message that it didn't exist
I searched for Guri Singh and got search results showing his account existed, but when I clicked on it I got a message his account did not exist
Does anyone know if he made his account private or if he got nuked by Elon? Or do I just suck at X (because I never go there)?
The EPA continued its betrayal of Trump’s campaign promise to “Make America Healthy Again,” approving the use of multiple “forever chemical” pesticides on crops despite public health concerns.
Normalize asking "what experience are you speaking from?" To find out WHY the other person feels the way they do, what lies behind their belief, how they are constructing their reasoning.
Ask them to call up something a parent said to them in childhood, ask them to describe a formative memory.
And then LISTEN to what they tell you, listen with love and compassion. Enter as fully into their personal experience as you can stand, seek to understand them as deeply as you can bear, and sit with that.
It is okay if it makes you uncomfortable. Do not try to explain it away in a manner that fits your own beliefs - that can come later. Sit with the fact that this person believes this and it is real to them, and that it explains, perhaps, many things about them.
This is what we do when it comes to art - we try to understand. We, in fact, suspend our disbelief in reality - our reality, that we believe - and enter into a world made by someone else, and reason with its laws and connect to its people. We find analogues to our world, not to criticize it but to understand it.
Only after understanding can you criticize. Many people both understand and criticize. But very few adequately CONVEY their understanding. Very few sit and express empathy and understanding, comfort if needed, and above all, love.
What is more vital even than understanding itself is that the other person KNOWS you understand, KNOWS you love, KNOWS that this new thing they have told you has not changed your view of them for the worse.
Then, and only then, can you criticize with true gentleness.
So. Normalize asking, "what experience are you speaking from?"
Know that all their words (and yours!) come from something deeper - not facts and not logic, but experience, emotion - and without that component you will never understand them well enough to criticize their ideas, and without being able to criticize each other's ideas, you can never grow.
On this day, 28 June 1830, Joseph Grantham became the first officer of the new London Metropolitan Police to be killed in the line of duty, in an incident which was later deemed to be "justifiable homicide".
Grantham tried to stop a fight between two drunk men in Somers Town, North London, and was then kicked to death.
At the subsequent inquest, the jury determined that his killing was "justifiable" and that Grantham himself was responsible due to "over exertion in the discharge of his duty".
The Met were the world's first modern, professional police force, created in 1829 and based in part on the experiences of the Royal Irish Constabulary, responsible for enforcing British colonial rule in Ireland. They were specifically designed to control the growing numbers of working-class people, and break up strikes and demonstrations, ideally without massacring people, as occurred in Peterloo 10 years prior, and which caused protests to escalate.
As such, they were extremely unpopular, especially in working-class areas of London, where a popular game for children was to hide in doorways until an officer walked past, at which point they would throw a brick or stone at him.
Other police forces began to be created elsewhere, often by colonial authorities. For example, in Kenya, the first police force was created by the Imperial British East Africa Company to protect their stores, and other police forces were established across the British empire. After colonised countries gained independence, in most cases, police forces remained relatively intact.
In the United States, the first forces in the North were set up to control working-class crowds. In the South, many forces emerged from slave patrols, which were responsible for pursuing enslaved people who escaped, and deterring resistance by doling out brutal violence and terror.
Learn more about problems with the institution of the police in this book: https://shop.workingclasshistory.com/products/the-end-of-policing-alex-s-vitale
I for one am glad we have separate teeth after two root canals, because if we had two big teeth I might just have dentures right now. Better to lose one small percentage of your teeth after a problem than lose the whole bottom jaw's worth in one fell swoop.
Wouldn't you have to loose your teeth chewing plates more than once through development? In order for the chewing plates to fit your head/face as you grow?
Which means intermittent periods of having only gums or emerging chewing plates across the entire top or bottom row until they fully grow in? And if they both fell out at the same time, you'd be back to gums completely until they grew back in.
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