“AI coding assistants truly shine when they augment developers, taking on routine and repetitive tasks like generating boilerplate code or suggesting code snippets, functions, or even entire classes,” Wang says. “They accelerate rapid prototyping, exploratory design, and experimental coding, turning initial ideas into tangible code much faster.”
Then, there are all the practical tasks AI can achieve for developers outside the actual code. Spencer Kimball, CEO of Cockroach Labs, describes how their engineers often use AI for design scaffolding, fixing tests, observability data, and blogging. 70% of the time, that’s not direct coding, but it’s giving back more time to developers to program, he says.
Where AI coding assistants fall short
In other situations, you may struggle to get AI working. Generative AI tools can falter when engineering goals go beyond a one-off function, aren’t well-specified, involve large-scale refactoring, or span entirely novel projects with complex requirements.
