A few years ago, I wanted a blog site.
Simple enough goal. I found a nice Hugo theme on GitHub, cloned the repo, copy-pasted some commands from a tutorial, ran hugo server, and if I got lucky, the site was up.
When it broke (and it always broke), I’d Google the error, find someone with a similar issue, copy-paste their fix, and move on. Problem solved. Site running. Goal achieved.
Until I needed to push a new article.
Site breaks again. Same loop. Ask friends. They help. Issue gets fixed. We’re back up.
But here’s the question I didn’t ask myself at the time: What did I actually learn?
The honest answer? Nothing.
I had a working blog site, sure. But I had zero understanding of how Hugo worked, how GitHub Pages deployed content, or what was actually happening when I ran those commands. I was just stitching together other people’s solutions and hoping they’d hold.
This was surface-level Googling at its finest. And today, we’ve upgraded to something even more seductive: vibe coding where AI writes the code, you ship it, and hope it works. Same problem, faster execution, same lack of understanding.
Both approaches work, until they don’t. And when things break in new ways, you have no foundation to troubleshoot from. You’re stuck in an endless loop of surface-level fixes without ever building real understanding.
The Pattern: Solutions Without Understanding
This isn’t just a blog site problem. It’s everywhere in security learning.
Someone’s setting up their first Active Directory lab. They Google “AD lab setup.” Copy commands from a Reddit post. It works. They move on.
A challenge requires decoding base64. They Google “decode base64 online.” Paste it in. Get the flag. Next challenge.
A script throws an error. They paste it into ChatGPT. It gives them a fix. They apply it. It works. Done.
In each case, the immediate goal is achieved. The challenge is solved. The lab is running. The script executes.
But the actual learning never happens.
You end up with a Frankenstein knowledge base; a collection of copy-pasted solutions held together by hope and luck. It works until it doesn’t, and when it breaks, you’re back to square one because you never built the intuition to understand why it worked in the first place.
How AI Makes This Worse (Welcome to Vibe Coding)
If surface-level Googling made shallow learning easy, AI has made it effortless.
ChatGPT and Claude and the larger family can give you confident, detailed answers to almost any technical question in seconds. Need a script? Done. Stuck on a config? Here’s the fix. Don’t understand a concept? Here’s a full explanation.
It feels productive. It feels like learning.
This is what I call vibe coding - writing (or in this case, generating) code based on vibes and autocomplete instead of actually understanding what you’re building. AI writes it, you ship it, it works. Goal achieved.
Until you need to modify it.
Here’s a real pattern I’ve seen play out countless times:
Someone needs to configure a tool for a CTF challenge; let’s say sqlmap or a custom enumeration script. They ask an AI: “Give me the sqlmap command to test for SQL injection on this target.”
AI spits out:
sqlmap -u "http://target.com/page?id=1" --batch --dbs --threads=10 --risk=3 --level=5
They run it. It works. They get the database names. Challenge solved.
Fast forward a week. Different challenge. Different target. They try to adapt the command. It doesn’t work. They don’t know why because they never learned what --risk=3 actually does, why --level=5 matters, or when --batch is appropriate vs. dangerous.
Now they’re stuck reverse-engineering a command they “wrote” but don’t actually understand.
The same thing happens in real-world work:
- AI generates a monitoring configuration that works perfectly… until you need to add a custom alert
- AI writes a deployment script that runs great… until you need to handle a new environment variable
- AI creates a SIEM rule that catches threats… until you need to tune it for a different log source
Here’s the trap: AI tools optimize for sounding right, not necessarily being right.
I’ve seen it happen:
- Ask an LLM for a PowerShell command, get something that looks correct but fails in practice
- Request an explanation of a protocol, receive a plausible but subtly wrong answer
- Generate a Python script that runs but contains logic errors you’d only catch if you understood what it was supposed to do
The problem isn’t that AI is useless; it’s incredibly powerful when used correctly. The problem is that it’s too easy to mistake a confident answer for a correct one, especially when you don’t have the foundational knowledge to evaluate what you’re being told.
Even worse, AI makes it possible to complete entire projects, labs, scripts, CTF challenges without ever stopping to think through the underlying mechanics. You can “succeed” without learning anything.
And that’s dangerous, because in security, surface-level understanding isn’t just insufficient; it’s actively risky. You can’t defend what you don’t understand. You can’t troubleshoot what you never learned. You can’t adapt solutions when your foundation is built on vibe coding and blind trust.
The Cost: Technical Debt in Your Own Knowledge
Here’s what happens when you optimize for quick solutions; whether through surface-level Googling or vibe coding instead of deep understanding:
1. You can’t troubleshoot when things break in new ways
The fix you copied works for someone else’s environment, not yours. The config AI generated works for the demo case, not your specific edge case. When it fails, you have no mental model to diagnose why.
2. You build on shaky foundations
Each surface solution stacks on top of the last. Eventually, you hit a problem that requires understanding three layers deep and you don’t have any of them.
3. You mistake familiarity for competence
You’ve seen the solution before. You’ve used the tool before. AI even “explained” it to you. But when asked to adapt it or explain it from first principles, you can’t. You’ve memorized the steps without internalizing the logic.
4. You plateau faster
Real growth comes from struggle, experimentation, and building intuition. When you short-circuit that process with instant answers whether from Google or AI you stop developing the problem-solving muscle that separates good practitioners from great ones.
The Alternative: Build Intuition First, Then Validate
When I rebuilt my blog site, I took a different approach.
I started from the bottom. I read the Hugo documentation, not to copy commands, but to understand how Hugo actually works. How does it generate static sites? What’s the role of themes, content files, and config? How does GitHub Pages serve the final output?
I took notes. I broke things intentionally to see what would happen. I experimented with different configurations. I built a mental model of the entire system.
And then only then when I hit specific issues, I used Google and AI to validate my understanding or find optimizations I hadn’t considered.
The difference was night and day.
Now, when something breaks, I don’t panic and start Googling blindly. I have a framework to think through the problem: Is this a Hugo issue? A deployment issue? A theme conflict? I can troubleshoot systematically because I understand the fundamentals.
This same principle applies everywhere in security:
- Before Googling “how to set up AD lab”: Understand what Active Directory is, how authentication works, why domain controllers exist.
- Before asking AI to write a script: Understand what the script needs to do, what logic it requires, what edge cases exist.
- Before copy-pasting a CTF writeup solution: Understand the category of vulnerability, the general attack approach, why this specific technique works.
Build the intuition first. Struggle with the concept. Experiment in a lab. Make it click in your own head.
Then use search and AI to:
- Validate your approach
- Find more efficient methods
- Check syntax you’re unsure about
- Discover tools you didn’t know existed
In this model, Google and AI become what they should be: tools for accelerating understanding you’re already building, not replacements for that understanding.
When Google and AI Are Useful
I’m not saying never Google. I’m not saying avoid AI entirely.
Both are incredibly powerful when used correctly:
- Quick reference checks: “What’s the syntax for this PowerShell cmdlet again?”
- Error message debugging: “This specific error, what are common causes?”
- Validation: “I think this works because X, am I right?”
- Exploration: “What tools exist in this space that I should know about?”
- Efficiency: “I know how to do this manually, but is there a faster method?”
The key difference: you’re using search and AI to supplement understanding you already have or are actively building, not as a shortcut around building that understanding in the first place.
Looking Back
The version of me that copy-pasted blog commands had a working site but no real knowledge.
The version that rebuilt from fundamentals had both.
That’s the difference between appearing to learn and actually learning.
In security, that gap matters. You can’t fake your way through a real incident response. You can’t copy-paste your way out of a novel attack. You can’t vibe code your way to intuition when the tool breaks and you need to understand what’s actually happening under the hood.
The shortcuts feel productive in the moment. But they leave you stuck at a level you could have surpassed if you’d been willing to struggle through the fundamentals first.
So next time you’re tempted to “just Google it” or “just ask AI”, PAUSE.
Ask yourself: Am I using this to validate understanding, or am I using it to avoid building understanding in the first place?
The answer will determine whether you’re actually learning, or just going through the motions.
Until next time, build more, vibe less.