I mean you can still scale that? Ask a lighter model to go through every function to find vulnerabilities, take output to bigger model like Opus and classify the critical ones.
I’m at Big tech and our org has our sights on automating product manager work. Idea generation grounded with business metrics and context that you can feed to an LLM is a simpler problem to solve than trying to automate end to end engineering workflows.
I’ve been using AI coding tools (Cursor, Claude Code) for React/React Native side projects. I have experience with these frameworks so I could guide the AI with individual tasks and catch mistakes, and overall it worked pretty well.
Recently I tried building a native iOS app with zero Swift experience, giving the AI just a markdown spec. This was basically vibe coding, I didn’t understand much beyond general software principles. It quickly broke down: hallucinated method signatures, got stuck on implementing extensions, and couldn’t recover. I would run the app on my device and give it feedback and logs. After hours wasted, I spent some time reading the docs and fixed the issues myself in 30 minutes.
My takeaway: AI will accelerate developers but won’t replace them. Still, acceleration means fewer engineers will be needed to ship the same amount of work.
I think music used for commercials, movie trailers etc will move to be AI generated (taking a revenue stream from artists into soul less corporations).
But for cases where music is the primary product, I don’t for see AI generated music overtaking anything