I want to make a claim that might age badly, and I'm making it anyway.
The more we offload thinking to AI tools, the more we need deliberate practice to stay sharp. And LeetCode — for all its baggage — is one of the few structured ways most developers have to do that.
Not for interviews. For your brain.
Something is quietly happening to developers right now
Nobody announces it. There's no error message.
But ask yourself honestly: when was the last time you worked through a non-trivial logic problem without reaching for Copilot, ChatGPT, or a Stack Overflow snippet in the first five minutes? When did you last choose a data structure because you reasoned through the trade-offs — not because autocomplete suggested it?
AI coding tools are genuinely useful. I use them daily. But there's a cost that compounds slowly and invisibly: you stop exercising the reasoning muscle, and eventually, it weakens.
You don't notice until someone asks you why your solution works — and you have to think harder than you expected.
What forgetting actually looks like
It doesn't look like incompetence. It looks like learned helplessness disguised as efficiency.
You paste a problem into ChatGPT before sitting with it for even two minutes
You can read code well but struggle to write logic from scratch under any pressure
You know a nested loop is "probably bad" but can't articulate why, or what the better approach is
You feel vaguely anxious when the AI gives you a wrong answer — because you're not sure how to verify it
That last one is the one that should worry you. If you can't sanity-check the output, you're not using a tool. You're depending on one.
Why DSA and algorithms are actually the right antidote
Not because Google-style interviews are a good measure of engineering talent — they're often not.
But because DSA problems are the most concentrated form of a skill you use constantly in real work: breaking down a problem you've never seen before, reasoning about it carefully, and arriving at a solution you can defend.
Working through a sliding window problem trains you to notice patterns in data. Implementing a graph traversal from scratch forces you to hold state in your head. Getting a time complexity wrong and figuring out why teaches you to question your own assumptions.
None of that is interview prep. All of it is thinking.
And thinking is exactly what AI is tempting us to skip.
The bar is low — and that's the point
I'm not suggesting grinding 300 hard problems on a leaderboard.
Twenty minutes, a few times a week. One problem. No solution tab open. The goal isn't to solve it — the goal is to try before you give up. To sit with the discomfort of not immediately knowing the answer, and work through it rather than outsourcing it.
That habit — trying first, thinking before reaching — is what erodes fastest in an AI-assisted workflow. It's also what makes you the kind of engineer who can actually steer AI tools well, rather than just accept whatever they produce.
This isn't nostalgia for a pre-AI world
AI tools are here, they're useful, and they're not going anywhere. The developers who will use them best aren't the ones who delegate the most — they're the ones who've kept their own reasoning sharp enough to know when the output is wrong, when the approach is suboptimal, and when the problem itself is being misunderstood.
DSA practice won't make you immune to bad AI output. But it keeps the critical faculty alive - the part of you that reads a solution and thinks "wait, that doesn't feel right" and knows how to follow that instinct.
That instinct is worth protecting.
Are you still doing any deliberate problem-solving practice, or has AI tooling changed how you think about that? Genuinely curious where other devs land on this.
Top comments (5)
Important counterweight to the hype. The risk is real: outsource the thinking and you atrophy the thinking, the same way GPS quietly killed people's sense of direction. But I'd draw the line by task type rather than rejecting the tool. Offload the mechanical (boilerplate, lookups, formatting) and fiercely protect the parts that ARE the thinking: problem framing, tradeoffs, deciding what's even worth building. AI as a calculator is fine; AI as a substitute for knowing which equation to write is the trap. The danger isn't using AI, it's using it on the exact work that was building your judgment. I designed Moonshift around that split on purpose, agents take the grunt work so the human's attention goes to the decisions that actually compound skill. Where do you personally refuse to let AI in, to keep your edge sharp?
Personally I refuse letting AI make big decisions... For example, I am working on ideapick.app and I am making all of the sys design decisions while letting AI debug, write docs and refactor in some cases.
P.S design that AI provides just sucks
It is dangerously easy to confuse access to answers with actual understanding. When we let LLMs do all the cognitive heavy lifting, we aren't just saving time—we are outsourcing our critical thinking skills and problem-solving muscles. AI is an incredible bicycle for the mind, but you still have to pedal. If you don't know how to structure a thought, validate an assumption, or spot logic flaws on your own, a tool that generates text at lightning speed just helps you fail faster. Brilliant reminder to keep our brains engaged.
One thing I've noticed is that the more I use AI, the more important deliberate problem-solving practice feels.
Not for interviews, but to keep my reasoning sharp. AI is great until it gives a wrong or suboptimal answer, and then your ability to verify it becomes the real skill.
That's one reason I still do the occasional LeetCode/DSA problem. It's less about solving algorithms and more about exercising the "figure it out yourself" muscle that AI makes it easy to skip.
Exactly, I knew I am not alone