alright I've got to do some quick math to explain attitudes towards AI to my boss.
we're looking to create an AI policy, and when we were talking about this, my boss (older millennial) was genuinely shocked to hear that younger people do not (seem) to view AI positively (a la the recent commencement speakers being booed)
please rb for larger sample size!
Question 1/3
What is your age, and do you feel AI is a net positive or net negative in our lives today?
“Why don’t you use ai” idk man beyond the obvious environmental and “this machine causes psychosis and encourages people to kill themselves” thing I think asking the equivalent of a solid D student who is also a pathological liar if they can answer my question/do the work for me seems pretty fucking stupid
“Because the truth is, tech doesn’t have an image problem. It doesn’t have a message problem. It has an intention problem. What’s wrong with the axe murderer who broke into my house is not that he hasn’t successfully persuaded me to buy into his narrative. What’s wrong is that he’s trying to kill me with an axe. Similarly, when you launch a product that’s designed to put millions of people out of work, block access to sources of verifiable truth, replace human creativity with slop, and lower the barriers to every sort of atrocity, the problem isn’t that you haven’t told the public a good story about those things. The problem is that you are trying to do them.”
“Because the truth is, tech doesn’t have an image problem. It doesn’t have a message problem. It has an intention problem. What’s wrong with the axe murderer who broke into my house is not that he hasn’t successfully persuaded me to buy into his narrative. What’s wrong is that he’s trying to kill me with an axe. Similarly, when you launch a product that’s designed to put millions of people out of work, block access to sources of verifiable truth, replace human creativity with slop, and lower the barriers to every sort of atrocity, the problem isn’t that you haven’t told the public a good story about those things. The problem is that you are trying to do them.”
Google Search is now Google Gemini 3.5 Flash Search
That means that all searches as of today (Tuesday, May 26, 2026) are being run through Gemini’s latest model on both the input and output sides, regardless of previous opt-in or -out preferences
Here’s what you’re going to do:
Download and Install Firefox Mobile
Open the 3-dot Menu
Tap on “Settings”
Go to “Search”
Under “Alternative Search Engines,” tap “Add Search Engine”
Name it however you’d like and enter this: https://noai.duckduckgo.com/?q=%s&noai
Tap “Save”
Back in the “Search” menu, go to “Default Search Engine”
Tap on the newly added DuckDuckGo
Back in the “Search” menu, you’ll see Google enabled in your “Alternative Search Engines” - disable the toggle to remove Google results
Nothing has aged me into a decomposing wreck quite like witnessing the enshittification of google. I feel like an ancient blathering relic when I hear "what did you do before chatgpt?"
Bitch I had google. I could summon the vast expanse of human knowledge with a few choice keystrokes. I could find recipes made by real human beings, written on unknown blogs because their recipe so closely matched my search terms. I could find entire research papers based off of barely remembered tidbits of them.
But now. The search engine that taught me basic particle physics & niche baking techniques & exposed me to brilliant comic artists & bloggers and more, is now a glorified chat bot.
A thing that hallucinates when its programming indicates that inventing is more efficient than copying. A thing that will amalgamate that vast expanse of human knowledge, which it alone can access, into its ever churning soup, but will no longer take me to the sources directly.
The gateway that used to bring me knowledge is now just a fire hose that sprays bullshit. Feels like watching the Library of Alexandria burn.
If you don't want LinkTree putting your imagery into AI... get out now
Just canceled my account (not that I used it that much). But I won’t permit this. Via @unaminh.bsky.social:
IMPORTANT: For any artists/writers/etc etc, using Linktree to point people to their work, from 5 July, they'll be feeding all imagery you use on your landing page into DALL-E by OpenAI.
I need a polite way to email multiple people in a business environment that says, "Are you having an AI chat write your email replies? Because these are incoherent sentences and if it's a chatbot, I need you to stop."
I'm not trying to accuse anyone of communicating like an angry toddler with zero sense of object permanence, but I have received an awful lot of communications which ask for help with "it" while not specifying what "it" is, or asking me to send something while telling me they have it in the same sentence.
I have a growing desire to return emails like teacher feedback on an assignment
"Thesis unclear and supporting arguments contradict leaving reader confused."
and in one particularly memorable and annoying case "You email lacks even one complete sentence or verb. Your audience is forced to wildly speculate what you are asking or proposing."
it’s funny how we’re getting to the point in the AI lifespan where you can feel the desperation from tech companies to have you use their AI features. instagram has moved their AI effects to the top of the menu when you’re creating a post for your story, exactly where the draw/edit button used to be. gmail is creating one-click AI-generated replies right before you open up the text box. spotify put a beta AI playlist generator on the front page that looks just like a search bar so all of their users accidentally click on it when they go to search for a song.
tech companies are shaking in their boots trying to prove to shareholders that their investment in AI is worth it, to the point where they’re tricking their users into using the AI features even for a split second in order to fudge the numbers. like awww is your little environment-destroying toy not wielding the results you hoped for? so sad!
I feel like generative AI is much like the mechanical bird in the story The Nightingale by HC Andersen.
I grew up with Andersen's fairytales and many of them has made a permanent home in my heart. The nightingale (or nattergalen, as is the original title) has always been amongst my favourites.
It is the tale of how the emperor of China learns that a great bird exists in his empire and he asks it to come and sing for him. The song deeply touches him and all the people at the palace, and the little bird is celebrated for his voice and song.
One day, a box is sent to the emperor, and within is it a golden mechanical bird, an artificial imitation of the real nightingale. They are asked to sing side by side, but it doesn't work well. The nightingale improvises and goes with his mood, while the mechanical bird can merely repeat how it has been programmed.
Still, hearing the mechanical birds makes the crowd ooh and ahh, and it can sing without mistakes and much more often than a real bird. It is wound up again and again for the amusement of the emperor and the people. The real nightingale leaves discouraged.
But as the time goes on, the mechanical bird starts to break down, and eventually, it doesn't work anymore at all. When the emperor becomes deadly ill, the soft song from a nightingale is all that can save him, but his little wind-up toy cannot help him.
The real nightingale comes back and saves the emperor's life, for it had been so touched when it first sang for the emperor and it made him shed tears. It remembers that first touch of something oh, so special as sharing its voice. The emperor learns the error of his ways.
Gen AI can only ever be an intimidation of the real thing. It is stuck in the same grooves as a mechanical bird. It can do it "perfect" and faster than humanly possible, but it is and always will be an imitation that cannot stand on its own. It might be enough to impress but it is not sustainable.
Only with the real music, art and writing can what is special be perserved. It must be created by living beings. We are able to adapt and change and create stuff outside of set parameters. But it is very understandable that it is highly discouraging to see gen AI spit out music, art or writing that to the untrained, or uncaring, eye is praised.
I reckon that the well will dry up eventually, whether it will be a crash, or behind a high paywall, and everyone who grew accustomed to it will cry out in despair. The mechanical bird is broken. Death will come and sweet song is not there anymore.
The nightingale flew home and continued with his life. He kept singing to the forest, but in another version of the tale, maybe he had stopped singing. It would have been a tragedy for both himself and all the people who eventually realised their folly in depending on a mechanical bird over the real thing.
So keep creating. Keep making music. Keep making art. Keep writing. Gen AI is imitating us, and it is arguably trying to replace our works, but it is not as good as the real thing and it cannot last.
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.
