What if AI tools disappeared?

There's an old quote from artist, Paul Cézanne: "It's so fine and yet so terrible to stand in front of a blank canvas." The same could be said for developers staring at an empty file. It's that moment of hesitation—where do I start? What's the best approach?—that AI tools now conveniently erase.

But what if they were gone?

What if, overnight, Cursor, ChatGPT, DeepSeek, and Copilot vanished? No auto-generated functions. No neatly scaffolded components. No AI-powered explanations of code you haven't seen before. Just you, a blinking cursor, and a problem to solve.

Would we still be as productive? Would we still know how to structure a new project? Would we still be able to debug complex issues without AI leading the way?

I've been thinking about this a lot—because, I remember a time before AI.

Back when I first started coding, there were no AI copilots to lean on. My first exposure to programming was watching my dad write Borland C++ on an old computer. That curiosity led me to break things apart—literally. I'd dismantle anything I could get my hands on, trying to understand how it worked, and then (mostly unsuccessfully) put it back together again.

That same instinct carried into software. I didn't just copy code—I needed to know how and why it worked. Whether it was reading ebooks, tinkering with Arduinos, or making a complete mess of my early projects, every mistake was a lesson that stuck with me.

Fast forward to today, and I find myself questioning whether AI is removing that struggle too much.

I started using AI tools because they helped me deliver projects faster, explore new technologies, and navigate massive codebases. But as I've leaned on them more, I’ve noticed something unsettling:

• I'm reading less documentation and trusting AI's output more—sometimes to my own detriment.
• I hesitate to make small changes manually because AI might misunderstand and undo them later.
• The more I rely on AI, the less I exercise my problem-solving muscles.

This isn't just a personal dilemma. I recently saw a post where someone built an entire platform using AI—only to find himself unable to fix it when things broke. He didn't know Python, so when the AI struggled to help, he was stuck.

So I've started asking myself: Am I using AI as a tool, or am I letting it replace too much of the thinking?

That's what I want to explore here. Not just how AI tools have changed the way I code—but whether we, as developers, are losing something important in the process.

Because if AI tools disappeared tomorrow, I want to know that I'd still be able to sit down, open a blank file, and just start coding.

How I Learned to Code

Before AI-assisted coding, before Copilot and ChatGPT could generate entire functions in seconds, the closest thing I had to "assistance" was Stack Overflow and a lot of trial and error.

My first exposure to programming wasn't through a structured course or an online tutorial—it was watching my dad work in Borland C++ on an old computer. I didn't understand what he was doing, but something about it fascinated me. Code wasn't just words on a screen—it was a way to control a machine, to make it do things.

That curiosity wasn't limited to software. I had an obsession with taking things apart.

• Remotes, radios, old VCRs—if it had screws, I saw them as an invitation.
• Did I understand how they worked? Not even remotely.
• Did I successfully put them back together? Occasionally.
• But the curiosity was there—I wanted to know what was inside, even if I had no clue what I was looking at.

When I finally started programming myself, that same instinct kicked in. I didn't just want to write code—I wanted to understand how it all worked under the hood.

• I taught myself to code by reading ebooks and messing around with Arduinos, where I could see my code physically affect the real world.
• I wrote terrible programs, made a ton of mistakes, and probably deleted more code than I actually kept.
• Stack Overflow was my safety net. Not in the "copy-paste without thinking" kind of way, but as a knowledge repository that helped me understand why things worked (or didn't).
• And while I didn't have AI autocomplete, I did have CodeBlocks, which gave me just enough assistance—showing me what was in my class, what methods an instance had access to, and whether I was mistakenly trying to access private fields. But that was it—no "fill in the blanks" autocomplete, no "guess what I'm trying to write next."

Back then, if I didn't know something, I had to look it up, experiment, and figure it out manually. And because of that, when I learned something, it stuck.

That's what makes me wonder: are AI tools removing too much of that struggle? Are we losing an essential part of learning—the deep understanding that comes from wrestling with a problem until you finally crack it?

Because while AI is great at generating solutions, I don't want to wake up one day and realize I've forgotten how to think through them myself.

The Tools That Shaped My Workflow

Every developer has that one tool they started with—their first coding environment, their first debugger, their first "Why won't this compile?!" moment.

For me, my early years were a mix of trial, error, and switching between editors in search of the "perfect" setup. Spoiler: it doesn't exist. But along the way, each tool taught me something new about coding, debugging, and how I worked best.

The Early Days: Just Trying to Get Code to Run

• CodeBlocks – My first serious C++ editor. Basic, functional, and the closest thing I had to an assistant. It wouldn't write code for me, but at least it told me what was in my class, what methods I could call, and whether I was violating access modifiers.
• Eclipse – When I thought, I need something more powerful. Turns out, more powerful also meant more confusing.
• NetBeans – When I dabbled in Java and JavaFX. It had everything I needed… but somehow, it never quite clicked.

Stepping Into Real-World Development

• Visual Studio – This was a game-changer. Once I started writing applications beyond simple scripts, Visual Studio gave me debugging tools, a solid UI, and an actual sense of control over my projects.
• Visual Studio for Mac – Great in theory. In reality? Constant crashes. 😩
• Sublime Text & Atom – When lightweight editors were all the rage, I jumped on board. They were fast, sleek, and had an endless supply of plugins.

Where I Am Now

• VSCode – The one editor I've stuck with the longest. Customizable, powerful, and pretty much an industry standard at this point.
• Cursor – My introduction to AI-assisted coding. It promised to streamline development, generate useful boilerplate, and act as an intelligent coding partner.
• ChatGPT & DeepSeek R1 – No longer just search engines with better branding—these tools have become part of my workflow, answering questions, generating code, and even refactoring existing logic.

Each tool shaped how I think about coding—from debugging strategies to workflow efficiency. But as I've moved from manual trial-and-error to AI-assisted development, I've started to wonder:

Have I gained speed at the cost of deep understanding?

And more importantly… if AI disappeared tomorrow, would I still know how to solve problems the way I used to?

Why I Started Using AI Tools

At some point, every developer has faced the dread of the empty file. Where do I start? What's the best structure? What's the correct way to implement this feature?

That hesitation is what first led me to AI-assisted coding.

I didn't start using AI tools because I was struggling—I started using them because they promised efficiency. The idea of an intelligent coding assistant that could help me write boilerplate, suggest improvements, and guide me through unfamiliar technologies was too good to ignore.

Here's what AI brought to my workflow

At first, everything was great. The tools did exactly what they promised.

But then I started noticing some unintended consequences.

I wasn't reading as much documentation anymore—I was trusting AI's answers without verifying them.
I hesitated to make manual edits—because AI might misunderstand and "correct" my changes.
And worst of all, I started feeling like I was outsourcing too much of the thinking.

It reminded me of when Resharper first started gaining popularity.

Back then, I remember some developers being resistant to using it because it felt like it was "writing the code for you." Resharper wasn't even an AI—it was just a really good refactoring tool. But even then, some people worried that relying on it too much would make them lazy developers who didn't fully understand what their code was doing.

At the time, I thought that was an overreaction. After all, Resharper was just making existing workflows more efficient. But now, looking at AI-assisted coding, I find myself asking the same questions:

If I let AI handle too much, am I still fully in control of what I'm building?

I still believe in AI as a tool, but I started wondering: at what point does assistance become dependency?

And if AI tools suddenly disappeared… would I still be as effective as I think I am?

The AI Trap

At first, using AI-assisted coding felt like a superpower. Instead of staring at an empty file, I could describe what I needed, and AI would generate a solid starting point. I could explore new technologies without reading pages of documentation. I could refactor code in seconds.

But over time, I started noticing small but significant shifts in my workflow—shifts that made me question whether I was relying on AI a little too much.

1. AI Tools Can Be… Stubborn 😬

One of the first red flags appeared when I started using Cursor's Composer feature. It's fantastic at generating multiple files when given a feature description. But as soon as I manually edited one of those files, things got weird.

• If I asked AI to generate something new, it would re-add the code I had just removed.
• It would assume the file hadn't been changed and apply updates based on old logic.
• Eventually, I found myself hesitant to make even small manual changes, because I knew AI would "fight back" the next time I used it.

I realized that instead of working with AI, I was adjusting my workflow around its quirks—which seemed backwards.

2. The Documentation Black Hole

Another change? I started reading less documentation.

At first, this felt like a productivity boost—why sift through docs when I could just ask AI for the answer? But then I ran into SST (a framework for deploying applications). AI kept giving me incorrect information, likely because it wasn't trained on the latest version. I trusted it without verifying, and it sent me down a rabbit hole of debugging issues that shouldn't have existed in the first place.

That was my wake-up call:

AI isn't always right, and skipping documentation is a dangerous habit.

3. Losing the Problem-Solving Muscle

Software engineering is, at its core, a problem-solving profession. When AI tools start handling too much of the thinking, we lose that muscle memory—the ability to work through complex issues ourselves.

There's a specific euphoria that comes from struggling with a problem and finally solving it. AI can generate solutions in seconds, but it robs you of that struggle—and with it, the deep understanding that comes from figuring things out on your own.

I started asking myself:

• If AI generates a solution, do I fully understand why it works?
• Am I thinking critically, or just accepting what it gives me?
• Would I be able to solve this problem without AI's help?

That's when it hit me—I wasn't just using AI as a tool. I was depending on it.

And that's a dangerous place to be.

When AI Fails, Who Picks Up the Pieces?

It's easy to praise AI when everything is working. It writes boilerplate, suggests optimizations, and helps you move fast. But what happens when things break?

Recently, I came across a Reddit post that perfectly captured this dilemma.

A developer had built an entire platform using AI. At first, everything seemed fine. But as the project grew, bugs started appearing—bugs they didn't know how to fix.

Why?

Because the developer didn't know Python.

They had relied entirely on AI to generate the code, but when it came time to debug or extend the platform, they were stuck. The AI, which had seemed so helpful before, couldn't fully understand its own work. No matter what they tried, it kept giving vague, unhelpful suggestions.

They were effectively locked out of their own project.

OP has 0 knowledge about python, no knowledge = no stress

The Rise of AI Code Debt

This story isn't unique. As AI-generated code becomes more common, we might start seeing:

• Developers struggling to maintain AI-written projects they don't fully understand.
• AI-generated "spaghetti code"—inconsistent patterns, random abstractions, and no clear design philosophy.
• A new kind of consulting industry: specialists who don't build software, but fix what AI has built.

Essentially, we could be looking at AI debt—just like tech debt, but worse, because no one truly understands how the system works.

It's one thing to let AI speed up development. It's another thing entirely to let it build something we can't control.

Because when AI fails—and eventually, it will—who's going to pick up the pieces? 🤷

What Am I Doing About It?

At this point, I had a choice. I could keep leaning on AI for speed and convenience, accepting the risks that came with it. Or I could make some changes—not to abandon AI entirely, but to make sure I was still in control.

So, I started making some adjustments.

1. Switching to Zed for a Fresh Coding Experience

I recently downloaded Zed (zed.dev)—not because I needed another editor, but because I wanted to reset my approach to coding.

• Zed does have AI integration, but I intentionally did't turn it on.
• I wanted to see what it felt like to code without AI assistance, to rely on my own problem-solving skills again.
• I also wanted to check it out because I'd heard about it in Rust circles, and I was curious about the experience.

It's not that I want to abandon AI-assisted coding entirely, but I don't want my first instinct to be, "Let me ask the AI." I want to think first, code second, and only use AI when absolutely necessary.

2. Planning Features Before Writing a Single Line of Code

AI is great for filling in gaps, but I realized I was sometimes skipping an important step:

• Sitting down,
• Mapping out the feature,
• Thinking through the edge cases,
• And structuring the code before touching the keyboard.

Now, I force myself to plan my implementations on paper or in a markdown file first. This keeps me from blindly generating code before I fully understand what I need to build.

3. The "What If AI Disappeared?" Test

Every time I use AI, I ask myself:

If the answer to any of those questions is no, I take a step back and rethink how I'm using AI.

Because at the end of the day, I don't want to be the person staring at an AI-generated codebase I don't understand. I want to know that if these tools disappeared tomorrow, I'd still be able to sit down, open a blank file, and just start coding.

Final Thoughts

AI is not the enemy. It's a tool—one that, when used well, can make us faster, more efficient, and more capable developers. But like any tool, it shouldn't replace fundamental skills.

I've come to see AI coding assistants like autocorrect for writing.

• It speeds things up.
• It helps with structure.
• It can catch mistakes before they become problems.

But if you only rely on autocorrect, eventually, you stop thinking about how words are spelled—and that's where the danger lies.

The empty canvas is an intimidating foe, and AI has done a great job of helping us get past that initial hurdle. But once the canvas isn't empty anymore, we still have to be the ones holding the brush.

So now, I focus on staying in control of my code.

Because if AI tools disappeared tomorrow, I don't want to be left staring at a codebase I don't understand.

I want to know that, even without AI, I can still solve problems, write great software, and think like an engineer.