this is going to be difficult -> i am capable of doing difficult things -> i have done everything prior to this moment -> this difficulty will soon be proof of capability
For context: this came out in 2011 in Australia. Same-sex marriage would not be legalized until December 2017.
It was only legalized in 8 US states (the 8th only a few months before), and wouldn’t be legalized nation-wide until 2015.
It was only legal in TEN COUNTRIES in 2011. We wouldn’t hit 20 countries until 2017. (Australia was 23rd)
As of today (April 14, 2026), I believe only 38 countries have fully legalized same-sex marriage. Out of somewhere around 200 countries in the world. That’s only ~19% of countries.
whenever I see archeological remains of a human who suffered from a terrible disease that couldn’t be treated in their lifetime but could be fixed now, this wave of sorrow and mourning washes over me. a woman in the 14th century who spent her 35 years of life bent at the waist because of congenital scoliosis. a man from the 18th century who died because of a non cancerous mass on his jaw that made eating progressively more difficult. remains of a woman from the Neolithic who died in childbirth having evidence of peri-mortem trepanation on her skull.
and yet she survived to 35. and yet the physicians in his time tried to strengthen his jaw. and yet someone 4,000 years ago tried to save someone they loved from dying of preeclampsia/increased cranial pressure. we tried. we tried and we tried and we tried. we failed and we learned but we tried. that’s what makes humans so beautiful.
My mom sometimes talks about a child in her neighborhood who was born with hydrocephaly and died of it. His parents strove to keep him alive for years, but he ultimately passed after a long decline. No treatment available. No hope at all, and the parents knew it from his birth.
Several decades later my sister had an MRI, as a long shot, to try to figure out why she was sick and deteriorating with a number of symptoms that were close to being written off as anxiety. She was sent straight to the hospital for adult onset hydrocephaly. Two days later she had brain surgery to put a shunt down her neck into her stomach and drain the fluid out. (No, you cannot usually get brain surgery that fast. Yes, it was that urgent.) Recovery was long and squiggly but it happened.
I think of that boy every once in a while. The one who died. I have no doubt that treatments developed for people like him, and tested on people like him, saved my sister's life.
He never knew he made the world better. His condition was severe, he never knew much of anything, I don't think. I think if I ever track down a God or something like one, that'll be somewhere on my List of Wishes. To make sure people like him know that they helped.
I've been type 1 diabetic since I was about one and a half, and was incredibly sick. If my mother hadn't also been type 1 and recognized the signs I likely would have died.
I was born in 1982. Insulin was first given to a patient in 1922, and he survived. Before that, type 1 meant death, often very slow and agonizing. Before insulin, doctors advised a super strict "keto" diet to prolong life, and it could work for awhile - up to a year, I believe. But it was a miserable existence as the body was literally eating itself as the blood turned acidic until the patient eventually died.
60 years. Only 60 years before my birth did that procedure work for the first time. That's absolutely nothing given the span of human history and I think a lot about the people who died from it throughout time.
But yes, people tried. Healers and doctors of all sorts tried all manner of things to allow these (mostly!) kids to live. The fact that it was accomplished at all is nothing short of a miracle. The fact that I've been alive 42 years is fucking insane considering my body doesn't produce a hormone necessary for survival. If you think that doesn't blow me away on a regular basis you have another think coming. It's nothing short of a miracle.
Every medical advancement is. The amount of work that goes into it and the vast amount of luck necessary to get it right even when all the research and information is sound is just astonishing.
Thank you, humanity. Thank you ingenuity and determination to save lives and make them better. Thank you to every medical practitioner and medical researcher in existence now and through all of time. Thank you to all the people who died so I could live.
Diabetes is one of these illnesses that really throws medical history into perspective. It's so common, everyone knows someone who has it, people live pretty normal lives with it. And yet, a hundred years ago, it was an instant death sentence. And then we were able to treat people with insulin and yet - it was extremely disabling. The insulin was extracted from animal pancreas had severe side effects, even with how similar the hormones are, there is always an averse reaction to proteins from foreign species, especially during long-term treatment. Injections had to be given every few hours, at-home-tests were only available from the 70s onwards. Insulin pumps entered the market in the 80s. Genetically produced insulin - humanized insulin - was first available in the US in 1982, in many countries only around the year 2000.
In 1930, having diabetes type I would basically mean being hospital bound, being woken every few hours for regular injections.
In 1965, you'd be able to live at home and get by with a very strict diet and a few timed injections. You'd struggle with chronical side effects. Having children wasn't done - passing on your genes would be immoral, and it might not even be legal for you to marry.
In the year 2000, you'd have a device clipped to your belt that would measure your blood sugar and distribute insulin, you only need to change the needle a few times a day. You might even be allowed to join in P.E. class
In 2025, you stick on two patches that do the same thing. They're synchronized through your phone.
That wasn't fate. It's not natural development that made diabetes a common chronic illness. It was hundreds of people who cared. It was the people who created the keto diet. It was the people who came up with tests. The ones who went through different species, trying to figure out the closest analogon to human insulin. It was the people who fought in court to get genetically produced insulin approved for medical use. It was people who looked at a rare, incurable disease and said "but what if it wasn't?"
Back in the 1960s, my dad was one of the first 100 successful open-heart surgeries in the world. He needed it to fix a hole in his heart, a condition that up until then was basically "take him home and make him comfortable."
He's lived long enough that three of his grandkids have been born with the same condition, and he's been there to assist with the recovery after the laparoscopic version of the same surgery he had.
He has a scar from collarbone to waist that's as thick as my finger--thicker, in some places. My nieces and nephews have scars so tiny you could mistake them for being from a particularly bad cat scratch. And their recovery was measured in weeks, instead of months.
Medicine has improved so much, so fast, that he's lived to see the research done on him save his grandchildren.
There are many wild things about Amok Time as an episode but I feel like we don't talk about the fact that the resolution is pulled from Romeo & Juliet. Like. Administering a drug that feigns death? For the purpose of escaping an arranged marriage? Spock/Romeo falsely believing the trick and being devastated?? This shit is from the most classic romance of all time what the HELL
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.
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