AI coding assistants like GitHub Copilot, Cursor, and similar tools have become mainstream in software development. These tools can suggest code completions, generate functions from natural language descriptions, explain existing code, help with debugging, and automate repetitive programming tasks. For experienced developers, they function as a highly capable autocomplete that speeds up routine work and reduces the need to look up syntax and library details. Productivity gains of 20-50% on certain tasks are commonly reported, though measuring overall development productivity is more complex than measuring typing speed. The tools are best at generating code that follows common patterns and worst at novel architectural decisions, complex debugging, and understanding the broader context of a system. Code quality varies - AI-generated code can introduce subtle bugs, security vulnerabilities, or inefficiencies that a less experienced developer might not catch. The most effective use treats AI as a junior pair programmer: fast and helpful for routine tasks, but requiring oversight from someone who understands what good code looks like. The impact on how people learn to program is an open question with significant long-term implications.