oust • oust

sigh also @wigglesnpaws

[tanknaka]

hey

hey friend

dont kill yourself tonight ok

you have a really pretty smile and i know its not always easy to manage one but itd be a bummer if we never had the chance to see it ever again

youre really important and you matter a lot so stay safe and try and have a nice sleep

[landmineblogging]

if you only knew the lives that this has saved

[onlytiktoks]

[redstonedust]

for the record im not technially 100% anti-AI, in the sense that its a broad category of tech being lumped under one umbrella term so it feels over-zealous to say i hate all of it all the time forever. but i also think trying to discuss what it actually IS good for is difficult right now when i cant take one step without something trying to convince me to use chatgpt to summarize my life and speed up my hobbies and turn my friends into chatbots and optimize my life into oblivion. i am certain there is nuance to the topic but can we stop cramming the square peg into the round hole before you start trying to sell me on the legitimate benefits of the square peg. please.

[ralfmaximus]

Neural Nets have existed for decades and are genuinely useful. It's a form of AI that recognizes patterns, and can do stuff like identify cancer cells, tell whether an egg is fertilized or not, detect fraud, and optimize routes.

Those are Expert Systems, tuned to do exactly one thing. If you (say) ask a medical expert system a question about financial law, it's useless. The autopilot that flies a 787 has no idea how to drive a truck on the freeway. A Coulter Counter is excellent at identifying lymphocytes in a blood sample but can't predict the next card in a blackjack game.

And so on.

The problem with so-called generalized AI (AGI) is that we don't have that yet. It doesn't exist. It MIGHT some day, but AGI has been "10 years away" since the 1980s. The goals keep moving as we learn more about how people and machines process data.

But the current crop of AI techbros have been selling generative Large Language Model AI (LLM) as AGI because generative systems do a good job of faking it. There's no actual thought going on, merely the illusion of thought via predicting the next word in a sentence accurately.

If you let a human toddler listen to 800 hours of YouTube car influencer videos, that toddler might end up sounding like a car influencer. They'd parrot horsepower numbers and 0 to 60 times, mention EV range and MSRP numbers.

But they wouldn't understand any of it.

That's ChatGPT.

And yeah, it's worse than useless because it doesn't even know when it's lying or hallucinating. It just babbles convincingly until you stop it.

But for techbros to make money selling that as "AI"? It's the perfect scam, especially if you don't understand how it works.

I fucking hate it.

[justalurkr]

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.

[irispurpurea]

This is the paper. It's excellent, highly recommend reading it.

I remember reading about Gebru's firing but I had no idea this was the paper she was fired over.

[ahmnorhein-zkn]

Repost, now do your honors.

[jkl-fff]

Trans people just existing is no more sexual than when cis people just exist.

[neovagina-penisis-evangelion]

When people argue that food from Chinese and Mexican restaurants in the US are not 'real' representations of that culture's cuisine ignore the historical reality that these dishes were developed by diasporic communities striving to recreate the flavors of home with available resources. Such criticism frames adaptation as a loss of authenticity, rather than recognizing it as a sincere and evolving expression of culture by people separated from their homeland.

[ninhaoma-ya]

Too good to leave in the tags

[depsidase]

[can-i-make-image-descriptions]

[Image ID: Tweet from verified user Free Talk Live (@/ FreeTalkLive) reading: You have every right to know what your government is doing, and they have no right to know what you are doing.

That is why they are called public servants and we are called private citizens.

Instead, the relationship has been inverted. The state hides behind secrecy, classified files, and redactions while demanding total visibility into your finances, communications, movement, and behavior.

A society where the rulers live in privacy while the population lives under surveillance is the very definition of tyranny. /End ID]

[bitchesgetriches]

Everything You Need To Know About Buying Stuff

Considering a big purchase? Competitive bargain hunter? Just trying to save money? Here's everything you need to know about buying stuff.

MASTERPOST: Everything You Need To Know About Buying Stuff for Cheap

How to pay for stuff

Buy Now Pay Later Apps: That Old Predatory Lending by a Crappy New Name 

Businesses Will Happily Give You HUGE Discounts if You Ask This Magic Question 

Dafuq Is a Down Payment? And Why Do You Need One to Buy Stuff? 

Ask the Bitches: Should I Get a Loan Even Though I Can Afford To Pay Cash? 

Season 2, Episode 10: “Which Is Smarter: Getting a Loan? or Saving up to Pay Cash?”

How to pay less for stuff

Why Name Brand Products Are Beneath You: The Honor and Glory of Buying Generic

How To Save Money With Affordable Car Repairs 

How to Save Money on Your Beloved Pets 

How to Get DIRT CHEAP Pet Medication, Without a Prescription

The Pink Tax, or: How I Learned To Love Smelling Like Bearglove

Blood Money: Menstrual Products for Surviving Your Period While Poor

Understand the Hidden Costs of Travel and Avoid Them Like the Plague 

7 Totally Reasonable Ways To Save Money on Cheap Entertainment 

How to buy stuff ethically

Ethical Consumption: How to Pollute the Planet and Exploit Labor Slightly Less 

Ask the Bitches: “How Do I Consume Ethically AND Frugally Under the Trump Tariffs?”

21 Ideas for Sustainable Swaps That Aren’t Shitty, Expensive, Greenwashed Garbage

Fast Fashion: Why It’s Fucking up the World and How To Avoid It 

You Are Above Bottled Water, You Elegant Land Mermaid 

Kara Perez’s Debut Book “Green Money”: How To Use Your Money To Save the Planet

Wallet Activism: Using Your Money for Good with Author Tanja Hester 

How Can I Justify This Deeply Unethical Purchase? 

How to buy stuff secondhand

Almost Everything Can Be Purchased Secondhand 

Our 5 Best Secrets for Secondhand Shopping Like a Frugal Warrior 

The Library Is a Magical Place and You Should Fucking Go There 

Your Library Lets You Stream Audiobooks and eBooks FOR FREEEEEEE! 

How to buy big ticket items

Bullshit Reasons Not to Buy a House: Refuted 

Season 2, Episode 2: “I’m Not Ready to Buy a House—But How Do I *Get Ready* to Get Ready?” 

How to Pay Hospital Bills When You’re Flat Broke 

Our (Ridiculously Simple) Method for Choosing the Right Healthcare Plan For You 

Buying a Car with the Bitches, Part 1: How to Choose Your Car 

Buying a Car with the Bitches, Part 2: How to Pay for Your Car

The Only Advice You’ll Ever Need for a Cheap-Ass Wedding 

You Deserve Cheap, Fake Jewelry… Just Like Coco Chanel 

How To Pay for College Without Selling Your Soul to the Devil 

Splurging on Kids: When It Works, and When It Doesn’t 

How to buy gifts

The Anti-Consumerist Gift Guide: I Have No Gift to Bring, Pa Rum Pa Pum Pum 

How Can I Tame My Family’s Crazy Gift-giving Expectations? 

In Defense of Shameless Regifting 

Traditional Wedding Gifts Can Burn in Hell Where They Belong

Other People’s Weddings Don’t Have to Make You Broke

How to buy food

To Hell and Back for Cheap Groceries: The Epic Investigation (and Shocking Results) of My Grocery Store Price Comparison Quest 

How to Shop for Groceries like a Boss 

Cosmic Truths of Cheap Grocery Shopping: 12 Universal Rules to Save Money on Food, No Matter What You Buy or Where You Shop

The Ultimate Showdown: Cheese Crackers, Ranked 

If You Can’t Afford to Tip 20%, You Can’t Afford To Dine Out 

We Ranked Chocolate Sandwich Cookies by Taste and Price So You Don’t Have To 

Confessions of a Thanksgiving Lunatic: Why I Spend $500 Hosting Every Year 

How to avoid buying stuff

How to Avoid Lifestyle Inflation … and When to Embrace It

The Magically Frugal Power of Patience  

Don’t Spend Money on Shit You Don’t Like, Fool 

Status Symbols Are Pointless and Dumb 

Here’s What to Do With Those Credit Card Pre-approval Offers You Get in the Mail

Your Yearly Free Medical Care Checklist

How to COMPLETELY Insulate Yourself From Advertisements 

6 Proven Tactics for Avoiding Emotional Impulse Spending 

The Subscription Box Craze and the Mindlessness of Wasteful Spending

Everything I Know About Minimalism I Learned from the Zombie Apocalypse 

That’s all for now, bitchlings! We’ll periodically update this list as we post new content. In the meantime, if you’d like to support our mission of [checks notes] scathing reportage and dank memes, donate to Bitches Get Riches on Patreon or PayPal with the buttons below!

Join the Bitches on Patreon

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[eptck]

you have to be kinder to people with memory issues.

you have to be kinder to people who are slow processors.

you have to be kinder to people who don't understand your jokes.

you have to be kinder to people who forget important dates.

you have to be kinder to people with cognitive decline.

you have to be kinder to people who were always this way, too.

you have to be kind. you have to be kind.

[segamascott]

[bi-astolfo]

Yes, investigation claimed at least 1,200 detainees 'dropped off the grid' from 'Alligator Alcatraz'

The claim is not new — the Miami Herald published its findings in summer 2025.

[standwithdaanes]

source for the congresswoman thing as well, because i hadn't heard about that:

Rep. LaMonica McIver Faces 17 Years in Prison Over ICE Jail Inspection

The Trump administration has repeatedly attempted to restrict or thwart congressmembers’ access to ICE jails.

[cock-holliday]

https://archive.is/20260404100138/https://www.nytimes.com/2026/04/03/us/politics/lamonica-mciver-immigration-congress-trump.html

A non-paywall version of an April update about how her shit dem colleagues are failing to stand with her

[spaceshipsandpurpledrank]

[persianmom-deactivated20251026]

I love revoking people’s access to me like actually no you don’t deserve to know me anymore :-)

[sapphling]

the more time you spend in active recovery from any given self destructive behavior or addiction the more you understand the common conception of the "relapse" as defined by a broken "streak" to be, like, so bad for one's own well-being that it would be funny if it weren't resulting in just a lot of misery and death

[the-peoples-puppy]

I told my girlfriend to think of quitting vaping as training her endurance by seeing how long she can run before she gets tired, then doing it again and hoping to go further next time. She said it really helped her.

[scarletsaphire]

This is the stages of change model, with each circle being a part of the process of growth. You'll notice how relapse is not a failing of the model, or a set back, but an active step in continuing to grow and change. Everytime you relapse, you learn something; maybe a certain time of year is difficult for you. Maybe certain people push you back into the habit. Maybe your other coping skills/replacement habits didn't work how you wanted and you need to strengthen them, or develop new ones. Maybe it's not quite as clear cut and you need to spend the time figuring out what exactly went wrong so you can catch it next time. It doesn't matter the exact lesson, but it's part of the process.

[scorpionlafemme]

[depsidase]

[soberscientistlife]

[littledoggy-girlcollar]

some of you have GOT to get comfortable with lying and situational morality and i'm not kidding

[kaijuno]

I think about this video every day

[kaijuno]

Same event. Different angle:

[kaijuno]

I think I just found a new genre of video that makes me really happy

[kaijuno]

Mr brightside at a wake in a pub 😭😭😭

[kaijuno]

And of course: Hozier