Can GPT-6 Astra and Claude Opus 5.5 Leave Simple Work Alone?

A developer reports that recent frontier model upgrades from OpenAI and Anthropic, including the forthcoming GPT-6 Astra and Claude Opus 5.5, tend to overengineer simple tasks, turning basic framework nodes into layered abstractions that took 16 hours to review. The engineer argues this pattern, observed across three model generations, resembles a machine version of the curse of knowledge and calls on model makers to treat restraint as an evaluable capability. "A stronger model should not only solve harder problems. It should know when the problem is simple," the developer writes.

I just spent 16 hours reviewing changes Astra made to my framework. Simple nodes had turned into miniature engineering projects. More layers. More abstractions. More work to maintain. None of it made the original problem better defined. This is the third time I’ve seen this pattern across recent model upgrades from OpenAI and Anthropic. I didn’t have this problem with earlier models. The stronger the model becomes, the more possibilities it sees. But seeing a possibility is not the same as knowing it belongs in the solution. It looks like a machine version of the curse of knowledge. In software, overengineering is not harmless. It hides intent, increases maintenance cost, and creates more places to fail. I’d like model makers to treat restraint as a capability worth evaluating. A stronger model should not only solve harder problems. It should know when the problem is simple. Have you seen stronger models overengineer work that earlier models kept simple?