Chasing the Best AI Model Might Be Asking the Wrong Question

Before Boris Cherny built Claude Code, he spent two years in Nara making miso. White miso takes three months to mature; red miso, two years or more. He said the process taught him something: what you start today is ready for a day that hasn't come yet. When he later described how Claude Code was built, he said the team designed it for the next generation of models, the ones that hadn't been released yet. There's a common move in the AI world: a new model comes out, and people immediately ask whether to switch. That's not unreasonable. It just has a short shelf life. A few months later, there's a new answer. Someone spent thousands of dollars stress-testing the strongest models available and noticed something: an experienced AI user with a slightly older model still outperformed a newcomer using the latest one. The gap came from the system built around the model, how to direct it, how to split tasks, how to verify outputs. When he swapped the top model for a cheap one on the same work, results were nearly identical. Cost dropped by several times. Models can be replaced, repriced, or shut down. But the working habits you build while using AI, what you delegate, what you check yourself, how to frame a task so you don't spend three rounds clarifying, that knowledge stays with you when the model changes. Boris's red miso took two years before it was ready. Each version of Claude Code he ships, he says, is aimed at the model that's still coming.