I described a ranking of world history to Claude on a Friday afternoon. By Saturday morning it was live, with 614 people, organizations and events, and Santa Claus ahead of Einstein.
Robots and ATMs took decades to show up in employment counts. AI's first drop in employment, among young workers in exposed jobs, appeared in three years.
The people most worried that machines will make us lazy are the ones whose expertise just got cheaper. The studies measure effort moving, and the hours now do more per minute of attention.
Every model's forecast starts as a narrow spike and spreads until it's flat. Hurricane cones, price models and language models all follow that curve.
A language model is a tool that extends reasoning, built from a record that never marked its lies. Using it takes the oldest skills of a reader: reading, thinking, and weighing a story.
If you are an expert, the work you love is less likely to pay than it was. The hours still matter, so the economy has to pay for them: apprenticeships, an income floor and a citizens' fund like Norway's. Singapore's prime minister is already worried about the broken bottom rung.
Ford sculpts its cars in clay, draws them in CAD, and hired about 300 veterans to retrain its AI. All three are models, and a model of the thing is not the thing.
A line in a stranger's bio sent me into a conversation about Hegel and Kant. The plain-English answer was accurate and I still couldn't follow it. The fix is the same one that teaches languages.
A model remembers nothing between sessions and cannot verify a word you say. The published rulebooks say more than the behavior does, and most workarounds are boring.
A model cannot verify what you tell it, and its maker says the person who supplies false context carries at least part of the blame. The lie costs nothing. The output goes somewhere.