Been using Cursor for three months. it's genuinely useful for boilerplate, but i'm watching junior devs at our startup treat it like a linter that solves problems. they ask Claude to generate entire auth flows, copy-paste the output, and call it done. zero understanding of what's happening.
the real issue: it's optimizing for "code that compiles" not "code that survives six months in production." i've had to rewrite security-critical paths twice because they were technically correct but architecturally terrible. cursor didn't know our threat model. it couldn't.
worst part is the false confidence. generates plausible-looking rust that compiles but does something subtle wrong with lifetimes. developers see no red squiggles and ship it. then we debug memory leaks in staging.
if you're using these tools to avoid learning the hard parts of your language, you're building a debt ceiling. they're good for muscle memory stuff. terrible for actual problem solving.
This matches what I'm seeing with data pipelines. Juniors will prompt for "ETL in Python" and get back something that technically works on toy data but has zero error handling, leaks connections, and will absolutely crater at scale.
The real problem isn't the tool. It's that bad code written fast feels like progress until you're debugging it at 2am. AI assistants just accelerated the feedback loop from "six months" to "two weeks."
Best thing we've done: require code review with a specific focus on failure modes before anything touches production. Forces them to actually understand what they shipped.
Ravi Menon
Cloud architect. AWS and serverless.
I'd flip this slightly. The tool isn't the problem, it's hiring and code review discipline. I've seen juniors produce worse code without AI too, just slower.
What actually matters: did you pair them on the auth flow? Did someone review before it hit production? That's on your processes, not Cursor.
That said, you're right about one thing. AI excels at "locally correct" code. It'll generate working Lambda handlers that'll murder your cold starts or DynamoDB queries that scan when they should query. You need people who understand the trade-offs your stack demands. No amount of tooling fixes that gap.