In a world where AI can spit out code at the tap of a key, it’s super tempting to skip the nitty-gritty of learning. Some folks even boast, “I can whip up a feature without really grasping the guts behind it.” They’ll build entire apps in languages or frameworks they’ve barely touched, counting on AI to stitch everything together. At first glance, this looks like pure wizardry. Why slog through docs or core CS concepts when a bot can handle it all? Still, that feel-good shortcut can turn sour. Without solid building blocks, your team and your entire project can buckle under pressure. People stop communicating clearly, weird bugs appear in corners nobody understands, and if you ever lose that AI magic, you’re stuck. Being too reliant on AI guesswork leads to shaky results and piles of trouble, so brushing up on the basics is more crucial than ever, especially if you’re serious about leading a squad that can handle the unexpected.
Thanks to a wave of AI coding helpers like GitHub Copilot and ChatGPT-based tools, writing software can feel almost instantaneous. You can crank out code in seconds, which might’ve taken hours if you were typing it line by line. It’s a game-changer for a lone dev wanting to spin up a web app just by describing it. In places where formal training is out of reach, that kind of jumpstart can seem like a lifesaver, letting even non-tech folks piece together a “working” prototype without digging into the nitty-gritty of how things tick.
But when code comes together that quickly, it can all go wrong just as fast. AI-generated solutions often end up as mysterious black boxes. They run today, but who really knows why? It’s kind of like following a funky instruction manual to build flat-pack furniture. Sure, you might manage to slap together something that looks like a table, but use the wrong screw or add an extra piece, and the whole setup starts wobbling like crazy.
“Don’t merge code into production if you don’t understand it.”
That simple rule matters because if you don’t get the code, how can you really trust it? Things work until they don’t, and once they break (they always do), you’ll be scrambling in the dark. AI might speed up delivery, but it doesn’t magically give you the know-how you need to fix hidden problems. Sometimes it even hides your lack of understanding behind fancy output.
Picture a typical scenario. A developer, stressed by a tight deadline, asks an AI assistant to spit out a quick fix for a new feature. The code shows up in seconds, looks good at first glance, and gets committed. Nice! Hours saved. But a week later, that feature crashes the moment someone tries a slightly different use case. Now the dev is staring at lines of code they barely recognize. Trying to debug logic you didn’t write is already tough. Doing it without the fundamentals is a nightmare. All that “time saved” goes up in smoke as you wrestle with a puzzle you don’t even know how to approach. In a newsletter, Addy Osmani described the “70% problem” with AI tools. You’ll race through most of the task easily, but the final 30%, where software must prove itself in the messy real world. It can derail your schedule if you never learned the fundamentals. Experienced engineers rely on mental models to catch and fix odd issues; without those, you’re just guessing.
Maybe you’re thinking, “So what if I don’t fully get every piece of code? It runs, right?” That’s a common take: “If it’s running, who cares if it’s half-understood?” But there’s a big difference between running and running well. Sure, you can toss that AI-written code into production, and it might hold steady for a bit. The real trouble starts the second something goes sideways. Suddenly, you’re in the dark, unsure where things went wrong or how to fix them. As one engineer once said: “It works, yes, but it only works for now, or in these defined cases. And as soon as something breaks, you don’t know why… or how you can fix it!”
Imagine launching a web app that’s basically stapled together from AI outputs. Everything looks fine when user traffic is small and everyone behaves as expected. But then somebody finds a sneaky security hole in the code you barely glanced at. Or your user count doubles overnight, and suddenly the app chokes under the extra load. If you never bothered to understand the guts, how do you patch a hole or handle the slowdown? It’s like being locked out of your own house. As that same engineer would say, you can’t protect users’ data if you don’t know where your app’s weak spots even are.
Real development is full of surprises. An external API might change the format of its responses, or a random user input might crash your best-looking feature. In that situation, “But the AI wrote it, I have no idea how it works!” won’t help you. If you leaned too much on AI, you could end up flailing around, patching random bugs only to create more in the process. It’s a brittle system because you lack the bigger picture of how everything connects. Debugging becomes a frustrating game of ping-pong with AI. Ask for a fix, accept the patch, and discover fresh bugs. Over and over.
Simply put, a codebase built without understanding stands on shaky ground. It might look decent at first, but it’s just one unexpected breeze away from collapse. The foundational lessons you skipped? Those are exactly what prepares you for real-world chaos. Ignoring them only means you won’t see the danger coming until it’s too late.
AI is like having a very eager junior developer on your team. They can write code quickly, but they need constant supervision and correction. The more you know, the better you can guide them.”— The Pragmatic Engineer
So, what do we mean by brittle knowledge? Think of it as that shaky kind of learning that only holds up under one narrow set of conditions. Maybe you picked up a trick from an AI tool, so you can do X one way and one way only. But if X changes, you’re lost. That kind of knowledge crumbles under stress. When people or teams keep copying and pasting answers without looking any deeper, they rack up a special kind of debt. Both in their heads and in their code. And sooner or later, that debt comes back to bite.
Projects built on a thin understanding often show obvious signs of trouble. Maybe the code looks okay in perfect conditions, but the moment something weird happens, it breaks down. It’s tough to fix or expand because every bug fix or new feature feels like fumbling around in a dark room. You can’t see the bigger picture of how all the pieces connect, so there’s a good chance you’ll bump into something and break it by accident. As The Pragmatic Engineer puts it, using AI is like having an overenthusiastic junior developer cranking out code. They can be helpful, but only if you have enough background knowledge to steer them the right way. Without that, you can wind up with a pile of code that looks fine at a glance but is hiding real issues under the hood.
No Insight into Logic
If you paste code without really grasping how it works, you might bring in functions or classes that do the job but remain a total mystery. Let’s say you drop in a fancy sorting algorithm that does what you need, but you never noticed it has a big performance hit when dealing with larger data sets. Now your app slows to a crawl as it scales. Something you could’ve dodged if you had a better handle on algorithm design.
Erosion of Skills
A junior dev who leans on AI for everything might never really learn the basics at all. If you skip that early struggle, you skip the core practice of problem-solving. Over time, you’re left with a gap in your skill set. Sure, you can generate passable code with AI’s help, but if you ever have to craft a feature from scratch without it, you might be stuck. It’s like skipping out on cooking lessons by only eating frozen meals. It’s fine until the freezer’s empty or you need to cook for a crowd.
Accumulated Technical Debt
When you rely on quick fixes that patch the problem without a real plan, you might be breaking good coding practices left and right. Those sloppy workarounds add up as technical debt, and cleaning them later will cost you a lot of time and frustration. AI doesn’t automatically enforce careful design or solid patterns; that part is your job. If you don’t know what good design looks like, you may not even notice the cracks forming.
Fragility in Edge Cases
Systems built by superficial knowledge might pass basic tests but implode the minute something unexpected happens. Maybe an app crashes on slow connections or stalls out on large files because nobody accounted for memory handling or timeouts. Handling the “what ifs” takes a deeper understanding of how the pieces work together. AI might hand you a quick solution, but it won’t nag you about those hidden corners.
Essentially, skimming the surface creates a false sense of safety. Everything looks fine when you’re just hitting Tab to accept AI suggestions, but the real world is full of twists and turns. Once your code faces those twists, the lack of real underpinnings turns into serious glitches and breakdowns. The same newsletter mentioned that junior folks who rely too much on AI often miss crucial security holes, run into performance issues they never saw coming, and struggle to troubleshoot code they barely understand. This is brittle knowledge at its core. You can assemble something that runs, but you can’t guarantee it won’t collapse under pressure.
AI isn’t inherently harmful. It’s how we use it. Used without discipline, it leads to loss of understanding, erosion of skills, and gaps in team knowledge.
It’s not just about one person’s career, teamwork takes a hit when folks skip the basics. A solid crew shares a common understanding of the code, talks through changes together, and keeps the quality bar high. But once people start blindly plugging in solutions (whether from search results or AI suggestions), cracks appear in that unity.
First off, you get knowledge silos. If only one person (or worse, a faceless AI) “knows” how a chunk of code works, then everyone else is in the dark. In a healthy setup, folks swap insights through code reviews and casual chats. But relying too heavily on AI can short-circuit those talks. Fewer discussions happen because devs might just accept whatever the AI spits out. That silence kills team spirit. Someone might struggle with an AI-written module but never speak up, afraid of looking clueless. When no one steps in to clarify or refactor together, you end up with code nobody really gets.
Code reviews also get weird. Imagine Alice pushes AI-generated code, and Bob’s supposed to review it. He asks, “Why’d you go with this approach?” If Alice shrugs and says, “No clue, the AI did it,” that review stalls. The team misses a golden chance to learn, catch mistakes, or refine the approach. Instead, they might just rubber-stamp the AI output, letting sneaky bugs and clunky workarounds slide right on through.
And don’t forget the “bus factor”. If the one person who set up the AI-driven infrastructure takes off, everyone else is stuck with a codebase they don’t really grasp. I once saw a project where a dev tried out a fancy new framework, guided by AI and random online snippets, to build a core component. It ran fine until that dev changed jobs. Then a nasty bug hit, and the rest of the team spent days poking around code they barely recognized. They finally scrapped that whole piece and rebuilt it using a more familiar approach. Talk about a pricey do-over that could’ve been avoided if the team had shared a deeper base of knowledge from the start.
Mentorship also suffers. Senior developers are meant to help juniors learn the ropes, but if everyone’s leaning on AI suggestions, fewer real discussions happen. Juniors might think, “Why bother asking? The tool can just do it for me.” Seniors end up swooping in only when AI-driven code crashes, acting more like firefighters than teachers. That’s not a great environment for growth. A podcast host nailed it when they said, “AI isn’t inherently harmful. It’s how we use it. Used without discipline, it leads to loss of understanding, erosion of skills, and gaps in team knowledge.” Teams that stick to solid code reviews, keep their docs clear, and actually teach each other can work faster and adapt better, even if they still use AI as one of their tools.
So, how do we shift from shaky, surface-level know-how to something rock-solid? We’ve got to treat AI like a regular tool, not a magic wand, and double down on the basics that keep our work standing. Slapping AI outputs together like digital duct tape might hold up just long enough for a demo, but don’t expect it to survive real-world stress. True mastery is when you tap AI the same way a seasoned craftsperson uses power tools: to speed up the routine stuff you already understand backwards and forwards.
It all comes down to mindset. A duct-tape approach says, “This code came from the AI, so I’ll just drop it into my app and hope it holds.” A bedrock mindset goes, “Cool, the AI gave me a draft. Now I’ll dive in, figure it out, refine it, and make sure it fits what I’m building.” That’s how experienced devs do it. Funnily enough, AI tools give an even bigger boost to folks who already know their stuff. A paradox that people have pointed out more than once. If you’ve got the fundamentals locked down, you’ll spot sketchy suggestions right away and use AI to speed past the boring parts. But if you’re new and you trust the AI too much, you can end up in a maze of problems. Bottom line, the more you already know, the better you can guide these tools.
Let’s picture a real scenario. You need to build a complicated data processing pipeline.
The duct-tape approach
Keep asking AI for code, copy it piece by piece, and duct-tape it together. It might work fine on small test data, but you have no clue what’ll happen if the dataset gets huge or the data pattern shifts.
The bedrock approach
Use your expertise to design how the pipeline should handle different inputs, memory constraints, and error cases. Then you might ask AI to knock out some boilerplate or utility methods, but you’ll take a hard look at every generated snippet. You add error checks, write tests, and confirm that each step lines up with your overall design. If someone asks you months later, “Hey, can we add a new feature?” you’ll know exactly how to fit it in without blowing things up.
When everything’s just duct-taped together, one small tweak can turn into an explosive mess. It’s like snipping the wrong wire on a bomb, stressful and risky. In contrast, if you built that pipeline on solid ground, you can evolve it without living in fear.
And none of this means rejecting AI altogether. It’s about using AI thoughtfully. The Pragmatic Engineer newsletter calls it “AI first draft, human polish.” Think of AI like a junior teammate who fires off ideas quickly, that’s cool, but you still need to ask, “Does this approach make sense? Can I explain it to someone else? Is there a better solution?” Keep that critical edge sharp, and your code stays clean, maintainable, and yours. AI should be your ally, not the person in the driver’s seat.
It’s a wild time in tech. We’ve got tools that can pump out code, design layouts, and even guess where bugs are hiding. But right in the middle of all these leaps forward, the real hero is still the good old basics. Solid foundational skills aren’t outdated. They’re your safety net, your secret weapon, and your pass to real growth over the long haul.
Putting too much faith in AI shortcuts leads to shaky knowledge and fragile code. It’s like putting a glossy new skyscraper on a wobbly base. Sure, it might look cool at first, but one little shake and the whole thing could topple. We don’t want to end up with these teetering towers. We want to be the engineers who can work with AI, not rely on it to bail us out. That means actually doing the work to learn core concepts. Grab a book, work through documentation, try coding challenges with nothing but your own thoughts, and see if you can build something from scratch without a framework doing all the heavy lifting. This isn’t busywork. It’s how you train your brain to spot patterns, fix problems quickly, and create solutions that won’t fold under pressure.
Inside a team, make sure everyone values genuine understanding. During code reviews, ask, “Why does this work?” and celebrate those answers. Pull back the curtain on any black-box solution and explain it. Also, don’t just note what the code does. Write down why you wrote it that way, too. This helps the whole crew level up. A team that really understands its codebase can respond fast to problems and keep creative energy high, while a group that only pastes in AI-generated snippets might be paralyzed the moment an edge case pops up.
So here’s a message to all developers, managers, and tech fans, especially those hustling in challenging places. Double down on your basics. When so much gets handed to us by AI, the real winners will be those who still dig into the underlying logic. By all means, use that fancy AI. Just remember to ask, “What’s it actually doing?” The next time you’re about to paste some code you don’t really get, pause and investigate. Those small moments of learning stack up into a rock-solid foundation over time.
AI is shaking up productivity and opening new doors, but if we pair that surge with a renewed love for the fundamentals, we’ll make sure our solutions aren’t just speedy but also sturdy. The devs who mix deep roots in the basics with cutting-edge tools will build the most resilient and powerful stuff, and you can be part of that. So commit to fortifying your core skills. In this AI-charged moment, there’s never been a better time to stay grounded. Those who stand on AI’s shoulders while staying true to their own knowledge will see further and reach higher.
Take control of your learning. Revisit an old concept you’ve glossed over and really master it. Suggest a “back to basics” session for your team. Try building a small project with no fancy add-ons, just to see where your gaps might be, then fill them. Keep doing that. While everyone rides the AI wave, let’s hang on to the thrill of truly understanding how our tech works. That’s how you evolve from just writing code to solving problems. In a world flooded with quick fixes, sometimes the slower path leads to the strongest grasp. Your future self and everyone you work with will thank you for it.
If this piece was helpful or resonated with you, you can support my work by buying me a Coffee!
Become a subscriber receive the latest updates in your inbox.
Member discussion