I’m a SWE with ~5.5 years of professional experience now and anecdotally see AI used primarily by juniors who use it as a crutch. Moreover, the vast majority of the “best” engineers I know do not use any AI-assisted coding tools (e.g. Copilot). They do, however, occasionally use LLMs as a search engine for unqualified questions (I.e. where they identify that there are unknown unknowns). Is my anecdotal experience representative of reality? If not, I’d love to hear peoples’ workflows (especially the historical high performers)
I use it as a pair programmer some of the time, especially in areas that I'm not super knowledgeable about, like arcane configuration details. I just use the ChatGPT app with cut and paste; I have not yet graduated to AI IDE tools. I'm thinking about it though.
1. Exploration: LLM first, docs second—cuts discovery time by ~3×.
2. Boilerplate: AI generates, I refactor on the spot; never merged blindly.
3. CR: bot leaves a first-pass checklist, humans focus on architecture.
4. Legacy spelunking: 200k-context summary + mermaid call-graph.
5. Rule of three: AI writes glue, I write core, tests cover both.
Result: 30-40% more features shipped per quarter without quality drop.