How AI Quietly Made Me a Multi-Stack Engineer
Reflecting on 2025 and the impact AI has had on my career, I noticed a significant shift. I’ve gone from being a specialized frontend developer to feeling confident writing backend code. What started as a small task from my manager ended up with me owning the product end-to-end.
How I Became Bullish on AI
Ironically, I wasn’t initially keen on using AI in my day-to-day work. That changed after a workshop with Google’s team at their Bengaluru HQ. We got hands-on with Gemini 2.5 agents and sub-agents, building end-to-end workflows and seeing how these systems actually operate in practice.
Watching an LLM work and then hallucinate, self-correct, and still produce usable output, felt genuinely magical. (Maybe that’s why AI icons are always the ✨ emoji.)
That experience flipped a switch. I went from being skeptical to genuinely excited, enough to later run a workshop within my team on writing tests with AI.
How AI Entered My Workflow
I started this journey back when neither ChatGPT nor Gemini had the strongest coding agents. Claude was the go-to, though it certainly made mistakes. My manager, gauging my potential to pick things up quickly, gave me the opportunity to take on a few backend tasks.
AI helped me:
- Grasp existing coding practices
- Navigate domain architecture
- Build context around the business logic of the repository
For my first task, I tried to one-shot the feature using Claude. And it worked! The feature ran, passed tests, and I raised a PR.
That’s when reality hit.
A senior backend engineer reviewed the PR and flagged multiple issues: design flaws, optimizations, and edge cases that Claude (and I) had missed.
That’s when it clicked:
Vibe-coded code is only as good as the reviewer promoting it.
Learning the Hard Way (and the Right Way)
Back to the drawing board.
Over the next few PRs, I went through heavy reviews: long comment threads, deep discussions, and constant back-and-forth. Painful, but invaluable. It forced me to understand not just what the AI suggested, but where its limits were.
What AI did exceptionally well was reduce the learning barrier.
What it didn’t do was replace judgment.
As I absorbed feedback and improved, I started shipping faster, not because AI wrote more code, but because I understood the system better.
The Bigger Shift
AI is quietly making programmers multi-stack.
With my frontend background and my growing backend understanding, I can now own features end to end, from UI to API to data flow. AI blurred the boundaries between stacks just enough to make that possible.
But one thing hasn’t changed.
AI can review syntax.
AI can suggest patterns.
AI can even refactor.
What it cannot do (yet) is develop intuition:
- How systems behave at scale
- Which trade-offs protect uptime
- What decisions will hurt six months later
That’s still human territory.
And that’s where seniors... at least for now... still have the upper hand. 😄