Three new studies seem to disagree about AI and new graduates. They don't. Fewer start in the jobs they trained for, but barely more are out of work. Here is where they went.
Palantir's chief executive says only two kinds of people have a future in the AI era: tradies and the neurodivergent. Look closer and he isn't talking about job titles at all.
The US government expects data scientists to grow a third by 2034 and procurement clerks to shrink. On this site those two jobs score 43 and 41. That gap is the most useful thing here.
In India, factory and home workers now wear head cameras so robots can learn what their hands do. Why robots need the footage, how much they would need, and which jobs it points at.
AI scribes now listen in on GP appointments and write up the notes. The biggest study found they save about two minutes an hour. The bigger change is where the doctor is looking.
AI is now the reason US employers give most for job cuts, yet cuts are falling. McKinsey says the real test is moving 11 million people into new work, and most of the roads aren't built.
The question we get more than any other: what should my kid study? An honest answer about what this data can support, what it can't, and five things that actually hold up.
AI tools help beginners far more than experts, which sounds like bad news for a long career. But the evidence tells a second story: what experience buys you is changing, not disappearing.
A production line and a plumber under a sink are both "hands-on", and a robot would find one far harder than the other. The measure that tells them apart, and what it still can't say.
A bit of fiction, clearly labelled. Six job adverts for roles that barely exist yet, each one grown from something already happening today, with a note on the real trend behind it.
In 1988 a roboticist noticed the hard things were easy for computers and the easy things were hard. Nearly forty years on it still explains a lot of the numbers here, with one big asterisk.
Crop farm labourers scored 90.9 on this site, and we called it resilience. The number was arithmetic. The word was an invention, and it was wrong in exactly the jobs where it mattered most.
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