Once when I was in undergrad, someone described something as “problematic” in class and our professor was like, “That’s cool, but ‘problematic’ doesn’t really mean anything. It means that the thing you’re describing has a problem, and in and of itself that’s not bad. Art, especially, should always have problems, or else it’s not interesting and not art, either. It sounds like you’re trying to say that this is bad, but you don’t want to say ‘bad.’ Is that right?”
So from then on whenever one of us called something problematic, he would make us talk it out until we could name the “bad” thing we were hinting at. In this particular class, 7/10 it was some type of oppression, and the remainder was like, “I’m uncomfortable because this is very new/confusing/pushing boundaries that made me feel safe.”
Once we stopped calling things “problematic” and stopping at that, class got way more interesting and... we all had to say, like, “that’s racist” or “that’s misogynistic” or “ew capitalism gross” out loud, which a lot of us had never done in a classroom before. Or we had to be like, “Uhhh... I’m not sure what’s so bad?” and confront our own beliefs and that was maybe even more useful.
Anyway. Whenever I see the word problematic, I can’t help but think of this professor being like, “Good starting point, now let’s get specific.” I think when we have to commit to saying “that’s ___” it requires a lot more careful thought about the truth and impact and complexities of whatever we’re claiming. Sometimes there really is some bullshit afoot, and also sometimes it’s art, and it should be full of problems, because that’s what art is.
Im obviously biased but I was thinking today of like, how genuinely impressive it was for a Single Game to crash steam that badly. And not just steam, but almost every storefront.
when you go to a doctors office their favorite thing to do is tell you "okay check out at the front desk" when you're done. and the front desk tells you ummm you can just go! and you're like don't I have a copay? and they're like we don't know, we have to ask your insurance company first. and you're like well my insurance card says the copay is $30, can I just pay it right now while I'm standing in front of you? there's a card reader right there on the desk. and they're like nooo we have to send a representative on horseback during the next waning moon to meet with their claims adjusting associate director of benefits management and client services in the secret glade to negotiate. and you're like oh okay and go home. and you get twelve emails asking you to take a survey about your experience
AND THEN in eight months you start getting phone calls from unknown numbers and when you finally check your voicemail they're like Your Balance Is Past Due We're Going To Kill You (even though by this point you have forgotten that you ever went to the doctor). and so you go to your MyChart account and log in with your username and password and you have to reset your password for security reasons. and you get two emails that say Your Password Has Been Reset. Was This You? and you have to go find your phone and enter your two factor authentication code and then you have to select which location you visited and then you have to click through all the Reminder: Complete Your Health History Profile where they ask if you've had any new surgeries even though you definitely told the nurse about this at your visit and then you have to find the Pay Bills tab and your balance is $179.23 and you're like why is it so expensive I thought the copay was $30? and you download a PDF of the charges and find out that they charged a late fee of $15 a month even though that doesn't add up to $179.23 and you don't even remember being told you had any balance at any point and you could call a representative and ask about it but that would probably take at least half an hour and there's no way it would actually get rid of the charges. so you go pay it anyway and you have to go find a paper check to look up your bank account routing number because they'll charge you an extra 3% if you pay with a credit card and there's a fee of $2.75 for convenience also. and then you get three emails that say We've Received Your Payment! Thank You For Your Payment!
ok so, I approached my local library with a proposal to donate a mural as a way to A: build portfolio/gain practical experience and B: give back to a beloved public institution. The director was very enthusiastic about it and i've been working on it since the beginning of March. Come with me as I endeavor to paint what is in all honesty an excessive amount of birds
I wanted the birds to look like they were actually in the space so first thing after doing the draft was to do a lighting study
after that I covered the walls in letters in lieu of a projector/vr headset bc i have neither of those :) Then i take a picture of the section of wall and superimpose the lineart over top of it so I can pencil in the lines
et voila
and that was a whole week on it's own so next comes the paintin' >:)
Here is an article from NPR about it (May 22, 2026):
Carolina Milanesi, an independent technology analyst, said Google is trying to make its cash cow business — search — richer and more personalized, and it will make shopping easier. But there is a risk that users may have fewer choices about what to click.
"Right now it's: I ask a question, I get a bunch of answers and I feel that I'm in control as to which answer I take, or if I'm looking for something, which product I'm going to end up buying. That is going to be less so going forward," she said.
Milanesi envisions AI-enabled search and agents proposing products to consumers — perhaps even those they have requested — but with less clarity or choice around where it's coming from.
"If you're going to say: 'I want a pair of Jordans, go find them,' you're not necessarily sure what steps have been taken and whether the AI has used a source or a store that was paid for and therefore came up in the search results," she said, "or if AI actually went and did their due diligence and picked the best for me as a customer."
And here's one from Time magazine (May 20, 2026):
While Google already has “AI Mode,” the company will now power the whole search bar through its new Gemini 3.5 Flash model.
Instead of the classic list of blue links, Google Search will now also generate a custom page with an AI-generated summary of what you’re searching about, which will then trigger a conversation with AI Mode on the main page, allowing users to ask follow-up questions—similar to the kind of layout you would see when opening ChatGPT.
And a little more from Time's article on how this may affect the websites that we are trying to search for:
When Google first started implementing AI-assisted results, news publishers warned of “catastrophic” impacts on the industry, much of which relies on Google search to drive users to their websites.
Last year, news websites saw significant traffic declines as chatbots increasingly replaced Google search as the primary way to find sites and ask questions.
Small businesses also noted drops in traffic to their sites from Google, which has traditionally delivered customers.
Lily Ray, vice president of SEO strategy & research at Amsive, a digital marketing agency, warned as early as last year that Google’s planned changes to search are “going to have a devastating impact on the Internet.”
“It will severely cut into the main source of revenue for most publishers and it will disincentivize content creators who rely on organic search traffic, which is millions of websites, maybe more,” she told Technology Magazine.
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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