This content originally appeared on DEV Community and was authored by Yash S
A candid reflection on how AI coding assistants are reshaping the development landscape—and what we might be losing in the process.
Over the course of the last year, I’ve become deeply embedded in the AI-assisted coding ecosystem. I use Perplexity, Claude, Chat GPT, Cursor, GitHub Copilot all on a daily basis, in fact I just wrote a synopsis of this article and gave it to Claude for refinement, but then I had to rewrite and refine it myself, much like my experience with coding with these helpers and ended up doubling my work. So here’s a short account of my experience.
The Evolution of My Coding Practice 
Initially, I approached AI assistants as tools for specific pain points. I’d offload those cumbersome code snippets that would otherwise consume disproportionate amounts of my time. But the more I used these tools, the less effort I wanted to put myself in implementing the tasks directly by myself manually at a low level. My effort started going into thinking how to structure the code better, review what the Agent had written to make it optimised and so high level thinking of the whole repo, whether things are consistent, in proper folders etc started becoming more of what I did. It is making me more efficient at low level design.
Along with that whatever i build has a component of high level design and how it ties with the system’s architecture, so using these agents frees up my mind to think about how things fit on that level and helps me avoid major pitfalls and design more scalable and better solutions.
The “Fail Fast” Advantage 
Perhaps nowhere is this more evident than in project bootstrapping. Setting up a new repository with a hexagonal architecture pattern, implementing MVC structures, or establishing consistent folder hierarchies—tasks that once required hours of setup and reference checking—now happen in minutes. The AI handles the scaffolding while I focus on the design decisions that truly matter.
This acceleration enables a “fail fast” model of development. I can rapidly prototype ideas, test architectural decisions, and iterate on concepts without getting trapped in implementation details. The barrier between idea and working prototype has dramatically lowered.
Skills that matter now 
Counterintuitively, working with AI assistants has forced me to think like a more senior developer by default. The more functional and technical clarity you possess about what needs to be accomplished, the better you can leverage these tools. Vague instructions yield mediocre code; precise specifications produce excellent results.
This dynamic reinforces a crucial truth: even in our AI-saturated era, first-principle problem solving, clarity of vision, and deep technical knowledge remain paramount.
These have always been the distinguishing skills of exceptional developers. Everything else—programming languages, frameworks, even coding itself—has always been tooling. AI assistants are simply the latest, most sophisticated tools in our arsenal.
The NOT so hidden costs 
But this efficiency comes with genuine trade-offs that deserve honest examination.
The most immediate cost is the substantial increase in code review overhead. AI-generated code requires careful scrutiny, often multiple rounds of refinement, and frequent course corrections. What should be a straightforward implementation becomes a collaborative editing session where you’re constantly providing feedback and direction.
There’s a frustrating irony in AI-assisted coding: sometimes the overhead of explaining what you want, perhaps most concerning is how AI assistance can diminish the intrinsic satisfaction of building. When the creative process becomes delegated, some of that joy inevitably dissipates.
When you haven’t written every line yourself, you lack the intimate familiarity that comes from building something from the ground up. This knowledge gap manifests most painfully during debugging sessions, where unfamiliarity with AI-generated code can significantly extend troubleshooting time.
The most subtle but potentially most significant cost is the gradual erosion of low-level coding skills and problem-solving intuition. If you consistently outsource implementation details, those neural pathways begin to weaken.
The Bottom Line
Has my productivity increased? Absolutely. But at what cost? degradation of my low-level coding skills? Peace of mind? NO! The cost is straight forward, roughly ~$40 per month (~$20 for Claude, ~$20 for Cursor).
Looking Forward 
The AI-assisted coding revolution isn’t going anywhere. The question isn’t whether to embrace these tools, but how to do so thoughtfully. The key is maintaining balance—leveraging AI for what it does well (boilerplate, scaffolding, optimisation) while preserving the human skills that remain irreplaceable (architectural thinking, creative problem-solving, and yes, the ability to debug and optimise code you understand completely).
The best developers of the AI era won’t be those who can quickly prompt an AI assistant to build stuff, nor those who steadfastly refuse to use them. But those who are essentially good developers with strong technical knowledge.
Ciao!
This content originally appeared on DEV Community and was authored by Yash S