I agree with all of this. But! I think it is also important to recognize that there are subgenres where it is significantly harder to find certain things, and it's actively unhelpful to readers to pretend that you can just find whatever type of book you want to read if you just know how to look for it, especially if you are sticking to trad publishing.
It is possible to find both sapphic SFF and M/M fantasy. It is significantly harder to find, say, aro urban fantasy. Or trans romantic suspense. Or intersex mystery.
A lot of the advice above only really works for trad published or popular books and for identities/subgenres/content that aren't too niche.
So here's some advice if the advice above isn't working for you (either because you can't find books with what you want or because the books you are finding don't end up being the vibe you want), from someone who reads a few hundred books a year:
Find websites or lists dedicated to the specific thing you are looking for. They will generally have more variety and will post you to examples that don't show up in regular rec lists. (ex: aro book recs, ace book recs, intersex #ownvoices database, sapphic books). Goodreads lists can (sometimes) also be your friend.
Get comfortable reading self-published and small press books. Trad publishing has its blind spots.
Check Reddit for recommendations
Start figuring out what it is specifically that you like and then start making your searches more specific. This can be subgenre (if you want urban fantasy, you're rarely going to find it just searching "fantasy"), tropes, plot devices, vibes, etc.
Look at the "readers also bought" on Goodreads/Amazon, similar books on Storygraph, etc. if you read a book you like. Even if you don't end up reading the one you click on, it can show you similar authors (similar to looking at the blurb on a cover), especially because far fewer books have blurbs now.
Yet another new study debunked the basis for the anti-trans sports bans. It was never about sports but for creating legal avenues for exclusion and abjection. This is one of the largest analyses ever conducted, involving 52 studies and 6,485 trans people. Read the study here.
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 love that Leverage really goes out of it’s way to show us that just because you break the ‘rules’, it doesn’t mean you’re breaking the rules. Rules and laws and society are all made up, at the end of the day, and all you really have is your own moral compass and sense of justice; is this just to you? Is it right? Should it be OK for companies to put people in insurmountable debt for the rest of their lives just because our medical care is so expensive in this modern day and age? No law or rule should change what you know in your heart is right and wrong, and I think that’s the key thing that makes someone a good person in my eyes.
#there was a time when parker wouldn’t have noticed, #not because she lacked the capacity to care, #but because she had narrowed herself, #to stay alive she cut off as many unnecessary things as possible, #watching her get them all back, #is one of the glories of this show (via @seananmcguire)
This scene hit me like a brick. My parents were hundreds of thousands of dollars in debt when I was 16 bc I’d had cancer the year before (my treatment ended up being free but the initial ER bills and such were not).
But somewhere along the line they just… Disappeared. My mom says they’re not being paid and they’re not in collections. It’s almost as if someone out there did…exactly what Parker did.
Ever since I saw this the first time, I’ve imagined it was Parker doing it. That she and Hardison had a free weekend and decided to take it out on a collections agency. That I was one of the lucky ones who got a little Leverage.
Okay but like yeah, that is actually a thing that happens, albeit not exactly like this. I don’t remember the exact process but basically there’s a booming industry to sell peoples debt - the business you owe money to sells it to someone else for a fraction of the money owed, wipes their hands of the whole affair, and now whoever bought your debt is riding your ass to get you to give the money to the. But it’s also entirely possible for people to just… buy up massive amounts of debt for pennies on the dollar, and then just. Forgive it. Because capitalism is a living nightmare, but the system is broken enough that it’s possible to exploit it for good sometimes.
Like, the main reason I know about this is because John Oliver did a piece on debt buying a few years ago, and ended it by revealing that he’d bought 15 million dollars worth of medical debt just so he could forgive all of it. Both to expose how broken the system was because some random fucker like him could buy millions of dollars in peoples debt with zero regulations, and also just to take the record for biggest TV giveaway in history.
yes! if you want to help with the medical debt crisis in the US and have some extra money please donate to RIP Medical Debt if you can. They’re completely legit and really do what they say - you really CAN relieve an incredible amount of debt for the needy with even a small donation. I’m a monthly donor and receive a quarterly report of the debt they’ve abolished, and it truly is amazing. Based on those reports the average amount of debt abolished per person is actually I would say about $600 - which means, if you’re doing the math, that with a $6 donation to RIP Medical Debt, you can potentially pull one person out of a poverty spiral - maybe even one family. For six dollars. that’s a pretty good deal, I think.
My favourite translator said that when she was an ambassador for Hungary she took all these Japanese politicians on a tour and she was trying to circumtranslate ‘merry go round’ cause she didn’t know the Japanese word for it by calling it a ‘horse tornado for children’ and they had no blessed idea what she was saying and she finally started running in circles going up and down and they go ‘ohhhhh, in Japan we call those ‘merry-go-rounds’”
i think we should be ridiculing them more for this. you don't get to try and go all "queer website" when your staff likes to go on nuking sprees targeting the trans fem users
"we need more good food" y'all can't even handle normal food. you can't even handle rice. or peas. anytime someone has anything other than 4oz banana puree you call the cops. and when we try to show you egg, you shout us down because it's "too radical". because you only care about yourselves and maintaining your perfect little comfort zones.
Gentrification creates a stifling homogeneity in urban areas that makes it less suited for the everyday lives of the lower class and more suited towards the leisure and tourism of those with expendable income.
An old, decrepit laundromat gets replaced by an upscale bakery? And people are mad? It’s not that the poor hate organic vegan cupcakes, it’s that most of us don’t have a way to do laundry in our own home.
Run-down corner stores replaced by hand-made designer clothing boutiques? We don’t hate your eco-fabric shawl, but I can’t eat that for dinner after work like I could have a can of beans I grabbed from that corner store when I don’t have time to take the bus to the real grocery store after work.
What gentrification brings in and of itself is not typically bad, it’s that gentrification brings institutions of leisure and pleasure and makes it so that the poor have to go farther out of their way for basic necessities. It turns low-income living spaces into local tourist attractions. It can even create food deserts by putting restaurants, grocery stores, etc. in that the majority of the lower class cannot afford.
Imagine if someone totally renovated your house and turned it into a mini theme park - they took away your sleeping space, where you prepare food, where you clean yourself and get ready for your day, and replaced it with things that will please people who are visiting, who have their own homes they can go back to, who are here not for their entire life but just as a distraction from their otherwise mundane existence. It’s not that you hate theme parks, it’s not like you’ve never been to a theme park and vow to never visit one again. It’s just that you need to live! To survive! And the leisure of those who have more than you should not invalidate your existence.
this is a really great explanation of some of the secondary effects of gentrification and how and why they compound the primary effect. but i have to point out that the primary effect of gentrification is that it drives rent up, and means that people can’t afford to live in their homes anymore. it tears up families and destroys communities.
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