Why Writing Efficient Code Still Matters (Even in the Age of AI) ?
We live in a time where you can ask an AI to write you code But If you aren’t comfortable reading and understanding that code, you can easily get stuck in the loop of:
“What’s wrong with my code? It looks fine…”
The truth is, it’s often not about whether the code runs, but how efficiently it runs. That’s where space and time complexity and a basic understanding of algorithms comes into play.
Let's take an example from building an App : Imagine building a feature where users can search through a large product catalog.
If you naively loop through every item (O(n)), the app feels slow as data grows.
If you use the right data structure (like a hash map or binary search on a sorted list → O(log n)), the same search feels instant.
That’s the difference between a cumbersome app and one users love.
If you are from AI background, you must have experienced there too. Training a deep learning model with millions of parameters is already resource heavy.
- If you store and process data inefficiently (say, dense matrices instead of sparse), you could need 10x more GPU memory.
- If you choose the right algorithm (say, an optimized gradient descent variant or caching strategy), training can finish in days instead of weeks.
That’s the difference between research that scales and one that stalls.
You don’t need to memorize every algorithm, that’s not the point.
What you do need is the habit of thinking in terms of efficiency. AI can write code for you, but if you don’t know how to analyze or optimize it, you’ll struggle to debug and improve it.
Learn how to become better in building scalable products by becoming better in wriitng efficient code here.
