Will AI accelerate or undermine the way humans have always innovated?

Technological innovation has always relied on experts collaborating across time and geography. EtiAmmos/iStock via Getty Images In graduate school, my experimental archaeology professor told a student to create a door socket – the hole in a door frame that a bolt slides into – in a slab of sandstone by pecking at it with a rounded stone. After a couple of weeks, the student presented his results to the class. “I pecked the sandstone about 10,000 times,” he said, “and then it broke.” This kind of experience is known as individual learning. It works through trial and error, with lots of each. Also known as reinforcement learning, it is how children, chimpanzees, crows and AI often learn to do something on their own, such as making a simple tool or solving a puzzle. But individual learning has limits. No matter how much someone experiments through trial and error, improvement eventually hits a ceiling. Humans have been throwing javelins for a few hundred thousand years, yet performance has largely plateaued. At the 2024 Olympics in Paris, the gold medal javelin throw was about 5% shy of Jan Železný’s 1996 record. The level of expert play in the strategy game Go was essentially flat from 1950 to 2016, when artificial intelligence changed the equation. Throughout humanity’s existence, these limits on individual learning have not applied to technology. Since IBM’s Deep Blue defeated world chess champion Garry Kasparov in 1997, supercomputers have become a million times faster – ...