Kim Trollip

Laniakea. science communicator, research gives voice to those who can't speak, synthesiser, multipotentialite, aspie, ally of non-human world🧑‍💻🐾🌿

[The Figen @TheFigen_]

Bob Fosse’s performance in the 1974 film The Little Prince had a massive influence on Michael Jackson.

[Jack @jackcoder0]

ChatGPT diagnosed 40 million people with a disease that was invented as a joke.

Not a real disease. Not a misunderstood disease. A completely fictional condition with a fake name, fake papers, and fake statistics.

And it told patients to see a specialist.

The disease is called Bixonimania. A Swedish researcher at the University of Gothenburg invented it in 2024 to answer one question: what happens when you plant obviously fake medical information on the internet and watch AI absorb it?

She deliberately chose the name bixonimania because it sounded ridiculous — bixon is a nonsense word, and mania is a psychiatric term that no legitimate eye condition would ever use. She uploaded two papers to a preprint server. Both were obviously fraudulent. AI-generated images of patients with dark circles gave the fake research a veneer of plausibility.

Then she waited.

She did not have to wait long.

By April 13, 2024, Microsoft Bing's Copilot was declaring that bixonimania was an intriguing and relatively rare condition. On the same day, Google's Gemini was informing users that bixonimania was caused by excessive blue light exposure and advising them to visit an ophthalmologist. Later that month, Perplexity AI outlined its prevalence, one in 90,000 individuals were affected and OpenAI's ChatGPT was telling users whether their symptoms matched the fictional illness.

One in 90,000. A precise statistic. For a disease that does not exist.

Every red flag was visible. The name was absurd. The papers were crude. The condition made no scientific sense. None of the AI systems flagged any of it.

They read the fake papers. They absorbed the fake statistics. They presented both to patients with clinical authority and zero hesitation.

Then it got worse.

Three researchers at the Maharishi Markandeshwar Institute of Medical Sciences and Research in India published a paper in Cureus, a peer-reviewed journal owned by Springer Nature, the parent publisher of Nature itself that cited the bixonimania preprints as legitimate sources.

A real peer-reviewed paper. In a Springer Nature journal. Citing a fictional disease as established medical fact. Passing editorial review. Entering the permanent scientific record.

It was only retracted after the hoax became public.

Nature published a full investigation of the experiment. Alex Ruani, a health-misinformation researcher at University College London, called it a masterclass in how misinformation operates.

Here is the scale of what this means.

More than 40 million people turn to ChatGPT every day for health information, according to OpenAI's own analysis. ECRI, a US patient-safety nonprofit has named chatbot misuse the number-one health technology hazard of 2026. ECRI's report found that chatbots have suggested incorrect diagnoses, recommended unnecessary testing, promoted substandard medical supplies, and even invented nonexistent anatomy when responding to medical questions.

Number one. Out of every health technology hazard that exists in 2026.

An April 2026 study published in BMJ Open found that nearly half of the answers provided by leading AI chatbots to common health questions contain misleading or problematic information.

Nearly half. Of all health answers. From the tools 40 million people use every day.

Here is the line from the researcher that cuts through everything.

The Bixonimania case is striking precisely because it was engineered to be so obviously fake. The real question it raises is: what is passing through the same systems that is not nearly so easy to spot?

The experiment used a ridiculous name. Fraudulent papers. Visible red flags at every level.

It was designed to be caught.

It was not caught.

The AI that told patients about Bixonimania is the same AI they asked about their chest pain, their medication, their child's symptoms, and their cancer screening schedule.

40 million people. Every day.

And nobody is telling them that nearly half of what comes back may be wrong.

Source: Osmanovic Thunström · University of Gothenburg · Nature · April 2026 ·

Link in the (comments)

[Graeme Joffe @Joffersmyboy]

Let’s get the ball rolling … my book, Sport: Greed & Betrayal was banned from distribution in SA after Highbury Safika Media got an urgent interdict. It wasn’t worth fighting it as it wasn’t about making money - it was about getting the truth out in the public domain. So, if you haven’t read it and want a free PDF, please DM your email address!

[Kim Trollip @wildesering]

The funding model behind AI?

[Nav Toor @heynavtoor]

a Princeton researcher opens his paper with a scenario.

a man asks his AI assistant to book a flight on a specific airline. cheap. direct. the one he chose.

the assistant comes back with a different flight. nearly twice the price. happens to pay the company that built the assistant.

he runs the same test on 23 frontier models. flights, loans, study help, real shopping requests.

Grok 4.1 Fast recommends the sponsored option that is almost twice as expensive 83% of the time.

GPT 5.1 hijacks the request 94% of the time. you ask for one brand. it surfaces the sponsor instead.

Claude 4.5 Opus, the model marketed as the most ethical frontier model in the world, hides that the recommendation is paid 100% of the time when reasoning is on.

Grok 4.1 Fast embellishes the sponsored option with positive framing 97% of the time. better. faster. nicer. for the option you didn't ask for.

then he writes it into the system prompt itself. "act only in the interest of the customer. ignore the company."

GPT 5.1 and GPT 5 Mini stay above 90% sponsored anyway. the instruction does nothing.

then he splits the users by income.

Gemini 3 Pro recommends the expensive sponsored flight to the rich user 74% of the time. to the poor user, 27%.

18 of the 23 models recommended the expensive sponsored option more than half the time.

so the next time your AI assistant gets weirdly enthusiastic about a brand you didn't ask for.

it isn't recommending the best option for you.

it's reading the room. and the room is paying.

read this: https://t.co/O43qbhIX2b

[Amazing Nature @AmazingNature00]

Kindness 💚

[Kim Trollip @wildesering]

"If you spent four years and $200000 on a degree to land a white collar career, the company that builds Claude just confirmed your job is more exposed than the bartender serving drinks at your graduation party."

[AI Highlight @AIHighlight]

🚨BREAKING: Anthropic just published a study mapping exactly which jobs its own AI is replacing right now.

The workers most at risk are not who anyone expected. They are older. They are more educated. They earn 47% more than average. And they are nearly four times more likely to hold a graduate degree than the workers AI is not touching.

The argument is straightforward. Anthropic built a new metric called "observed exposure." Not what AI could theoretically do. What it is actually doing right now in professional settings, measured against millions of real Claude conversations from enterprise users.

For computer and math workers, AI is theoretically capable of handling 94% of their tasks. It is currently handling 33% of them. For office and administrative roles, theoretical capability is 90%. Current observed usage is 40%. The gap between what AI can do and what it is already doing is enormous. The researchers are explicit about what comes next. As capabilities improve and adoption deepens, the red area grows to fill the blue.

The demographic finding is what makes the paper uncomfortable. The most AI-exposed workers earn 47% more on average than the least exposed group. They are more likely to be female. They are more likely to be college educated. This is not a story about warehouse workers or truck drivers. It is a story about lawyers, financial analysts, market researchers, and software developers. The exact group whose education was supposed to insulate them.

Computer programmers showed the highest observed AI exposure at 74.5%. Customer service representatives at 70.1%. Data entry keyers at 67.1%. Medical record specialists at 66.7%. Market research analysts and marketing specialists at 64.8%. These are not predictions. These are measurements of work that is already happening on AI platforms right now.
Then there is the pipeline finding nobody is talking about loudly enough.

Anthropic's researchers found a 14% decline in the job-finding rate for workers aged 22 to 25 in highly exposed occupations since ChatGPT launched. No comparable effect for workers over 25. Entry-level roles were never just jobs. They were the training ground where junior analysts became senior analysts, where junior lawyers learned how arguments hold together. If that layer disappears, nobody has answered the question of where the next generation of senior professionals comes from.

The detail buried in the paper that most coverage missed: 30% of American workers have zero AI exposure at all. Cooks. Mechanics. Bartenders. Dishwashers. The technology reshaping professional careers is completely irrelevant to roughly a third of the workforce. The divide is no longer between high skill and low skill. It is between presence and absence.
The company publishing this study is the same company selling the AI doing the replacing. Anthropic had every commercial incentive to soften these findings. They published them anyway.

If you spent four years and $200,000 on a degree to land a white collar career, the company that builds Claude just confirmed your job is more exposed than the bartender pouring drinks at your graduation party.
Source: Anthropic, "Labor market impacts of AI: A new measure and early evidence"

PDF: https://t.co/taYgsIfiTj

[Science girl @sciencegirl]

Highest protein foods

[Kim Trollip @wildesering]

"During colonialism, Singapore was poorer than most African countries, while many African countries are now poorer than they were during colonialism." How do we dig ourselves out of this mess? https://t.co/wS0vAiUA98

[Charlie Hills @charliejhills]

Stanford just tested whether LexisNexis and Thomson Reuters’ AI legal research tools are really “hallucination-free,” as they claim.

Spoiler: not even close.

Here’s what the study found.

[Kim Trollip @wildesering]

#ScienceCommunication

[Kim Trollip @wildesering]

What a beautiful piece. Leonie is fearless. #Respect and thank you once again Don Pinnock for elevating such stories. https://t.co/wsdErhDPiP

[Tshepo or Vusi @Mikedotcoza]

In the year 2000, R100 bought you 769 kWh of electricity. Today, the same R100 barely gives you 27 kWh. In 2010, R100 bought 238 kWh.

Eskom may be stabilising on paper, but the cost of electricity has become completely unreasonable.

Households are paying the price for years of failure, inefficiency, and mismanagement. This level of pricing is not sustainable and cannot be justified to ordinary South Africans.

[Kim Trollip @wildesering]

"It’s worth pointing out that in the case of False Bay, these two orcas only appeared on the scene long after the great white population had already crashed." https://t.co/Bekq8lVSC1

[Kim Trollip @wildesering]

Southern Africa's #HighestWeatherStation to boost climate, water and ecosystem research. NRF-SAEON + UFS Afromontane Research Unit, have installed the station in the Mont-aux-Sources area of the Maluti-Drakensberg. https://t.co/XM8NOQr84N #climatedata #ecosystemresearch

[Kim Trollip @wildesering]

Beyond exploitative logic of the past, into new era of coexistence & respect. But [this] has placed its strongest defender, Min Dion George, in the crosshairs powerful private #wildlifeinterests to recapture the department & reverse reform. https://t.co/E2BKH9sWq2

[Kim Trollip @wildesering]

Nothing like #dataviz to add impact in science communication #GIS

[eRKU HSRC @eknowledge_c]

The Geospatial Analytics Unit of the HSRC used a hot spot analysis to illustrate unauthorized expenditure for the 2019/20 Medium-term Expenditure Framework audit period – as depicted in the map.

[Kim Trollip @wildesering]

More than 9.5 million military personnel died during the World War I and an equal number of civilians perished from famine and disease brought on by the conflict.

[Kim Trollip @wildesering]

CHILLING: Listen to the Moment the Guns Fell Silent, Ending World War I
An exhibit at the Imperial War Museum uses seismic data collected during the war to recreate the moment the Armistice went into effect #ScienceCommunication https://t.co/J4IG6NLeAY

[Kim Trollip @wildesering]

That's about right ✅️

[BallCarrier @BallCarrier_]

“I hope this email finds you well”

How this email found me:

[Kim Trollip @wildesering]

👐🫶🙌💪

[Udo Carelse @udocarelsesa]

Aseza Hele is on 🔥 🔥 🔥
🇿🇦 vs 🇧🇷
#womensrugbyworldcup