"A few years ago, I discovered a tomato sauce recipe that was surprisingly simple: just canned tomatoes, butter, salt, and an onion. It inspired me to experiment, adding this and that each time to see how the flavor changed. Today, I'd call myself an amateur sauce expert. I know exactly how long it needs to simmer, what shade of red signals it's ready, and how to improvise with whatever's in the fridge."
"As my kitchen exploits remind me, experimentation is part of learning. It wouldn't be the same if I'd just asked ChatGPT how to make sauce each time. I'd be outsourcing my culinary creativity and losing the teachable moments that come from trial and error. As New Yorker writer Joshua Rothman observed, "[I]t's becoming clear that artificial intelligence can relieve us of the burden of trying and trying again. A.I. systems make it trivially easy to take an existing thing and ask for a new iteration.""
A simple tomato sauce recipe taught an amateur how experimentation, timing, and improvisation develop practical culinary judgment. Hands-on trial and error enabled learning that repeated use of AI would have short-circuited by outsourcing creative iteration. Artificial intelligence can eliminate the repetitive burden of iterating, making it easy to generate new variations, but that convenience risks eroding teachable moments and creative growth. The appropriate role for AI is as a creative collaborator that supplies ideas and options while humans make final decisions. Generative AI is powerful, but its future improvements may be incremental rather than radically transformative.
Read at Fast Company
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