the worst thing about those fancy pears is that you think “there’s no way a pear could be worth that much” but if you actually make the mistake of tasting one you will be forced to confront the fact that what you thought was pleasure is but a shadow of a shadow and there is a world out there more real than real that your senses have been waiting for, where the colors are richer and the water is wetter and sleep is refreshing. and you’re not invited.
If you have a friend that wants to vent to you but doesn't want solutions but you are a solutions-oriented person, may I suggest Silly Solutions (TM)? For instance, whenever my friend complains about the people at her job being dumb, I remind her that if only one of us had studied engineering, we could create a giant hippo robot with laser eyes to destroy them. It fulfills my need to offer a solution, doesn't violate her boundary of not wanting to problem solve, AND it cheers us both up!
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.
The main reason I’m pushing for people to stop using the term ‘pedophile’ and instead use the term ‘child sexual abusers’, is because since all discussions of child sexual abuse focus on this idea of an evil person who is just out to get kids because they are sexual attracted to them, it makes it hard for kids who where sexually assaulted by people who don’t fit that description to realize they were sexually assaulted.
It didn’t register for me until recently that my experiences of being forced to strip naked multiple times at the mental hospital to be ‘checked’ when I was 14 was sexual assault, because the people who did it were nurses/doctors who clearly didn’t find me sexually attractive but instead used it as a form of humiliation and control towards children they deemed as ‘unruly’ and ‘uncooperative’ (ie. children who asked to be treated like people). I thought only people who fit into this idea of a child attracted pedo could be child sexual abusers, so I thought my experience didn’t count.
Stepping away from the idea that there is a pedophile boggieman and instead highlighting that anyone can be a child sexual abuser will help more people realize that their experiences are sexual assault.
my stepfather would openly sexually harass me and my siblings at dinner while also loving the fantasy of killing pedophiles. whether he personally found our abuse sexually gratifying was frankly irrelevant to whether it traumatized us. whether he would be considered a pedophile or not doesn't change that he committed sexual abuse of children
I think in his mind there was a type of horrible person out there who does horrible things, and because he didn't think of himself as fitting that category, his actions could not be judged
it is unfortunate that there's no reason for most people to remember high school chemistry because the best analogy I have found for "the amount of energy that it takes me to initiate a task, which can be higher than the amount of energy it takes to actually complete the task" is "activation energy" and it's not precisely perfect but
yeah. and you can even include "thing that reduces the barrier to doing the task" as a catalyst/enzyme
anyway. unfortunately this does not actually clarify anything for the average person. but #ToMe it works
Hi biochemist here! Where catalysts come in handy is reducing the kinetic hurdles of the task and making the reaction rate in both directions go faster. Catalysts work through an alternative mechanism than the original reaction (ie an alternative transition state).
Most importantly, a catalyst is not consumed by the reaction.
There are other things that can improve or reduce a catalyst’s effectiveness:
Catalyst Inhibitor: reversible inhibits a catalyst
Catalyst poisoning: irreversibly deactivates a catalyst; steps can be taken to replenish the catalyst or remove the poison, but it requires external intervention.
Catalyst promoters: enhances a catalyst’s performance but does not have an effect on its own
Another factor that can make initiating tasks hard can be the thermodynamics: is the energy states of the reactants (initiating tasks) vs products (the finished tasks). Chemical reactions can absorb energy to form products (endothermic) or release energy to form products (exothermic).
Task initiation is usually endothermic. The bonds broken in reactants (initiating the task) are stronger than the ones in the products (finished tasks). As a result, the journey is generally uphill.
Occasionally however, finished tasks can see exothermic reactions (e.g. creative endeavors). Once initiated, it can be easier to start a new one. Energy is given off to the surroundings.
Some rough examples of the metaphor from my own life in the mental health realm are the following:
Endothermic task: completing my laundry; completing my laundry absorbs energy from its surroundings. It’s a lot easier to dirty laundry than to clean it.
Exothermic task: writing a book chapter
Medication can be a catalyst for me getting things done. On the other hand, it can also reduce the activation energy needed to not complete a task.
Stress can be a catalyst inhibitor reversible reducing the effect of medication on getting my tasks done
A consistently stressful environment (e.g. a terrible working environment) may be an irreversible catalyst depending on the amount of damage. Usually therapy is needed to repair the damage and even then, sometimes it hasn’t been effective.
A catalyst promoter would be being well-fed. On its own, it doesn’t really initiate me to complete tasks faster. However, it does improve the ability of medication to enable task completion.
If we want to improve task completion, we need to work on converting generally endothermic reactions to exothermic reactions, add catalysts if they don’t exist, avoid poisoning our catalysts, and maximize catalyst promotion.
