Historical analogies for large language models

First, guns replaced bows because guns need less training. Then guns became better than skilled archers. Then they replaced spears. Then infantry switched to guns with mini-swords on the ends. Then they dropped the mini-swords. These shifts weren’t driven by “productivity” so much as the fact that you  had  to switch to guns since you kne

We used to harvest huge amounts of natural ice and ship them long distances. The first machines to make ice were dangerous and expensive and made lousy ice. Then the machines became good and nobody harvests natural ice anymore.

In this analogy, LLMs are bad at first and don’t have much impact. Then they improve to match and then exceed human pe

In this analogy, LLMs become better than humans at writing. But it’s still widely understood that learning to write is good for you (maybe writing is “the best way to think”) so people send their kids to writing camp and humble-brag about writing in their free time. But in the end, most writing was done for the mundane purpose of  making text

Mass production made suit/cars/teapots cheaper and more plentiful. But Bentleys are still made by hand—artisanal goods are still seen as higher quality and higher status.

In this analogy, LLMs make writing vastly cheaper and more plentiful. But they never quite reach the quality of the best human writers. Most  writing  was always emails a

In 1800, half of the people in rich countries were farmers. After technology made farmers more productive, we weren’t interested in eating 50× more food per capita (and we weren’t  that  interested in expensive ingredients and eating white truffle saffron wagyu caviar burgers for every meal) so today only 1-4% of people are farmers.

In thi

Chess AIs far exceed humans, yet human chess is more popular than ever.

In this analogy, LLMs become better than humans at writing. But so what? We  like  human writing. Text isn’t interesting if it’s not part of a (para)social relationship.