How much of the code your developers “write” do they actually understand? The way AI-assisted coding is evolving, the honest answer for many developers is probably less and less. And that should concern us.
Andrej Karpathy recently put a name to this growing trend: “vibe coding.” On X, he described how AI tools like Cursor Composer and Sonnet have made coding feel more like directing than actually developing. No digging through files. No searching for the right function. No manual debugging. Just telling the AI what you want, hitting “Accept All,” and watching the code build itself.
Sounds convenient. Maybe even inevitable. But let’s pause for a second. When did coding become a passive activity?
At some point, writing software shifted from being an intentional, problem-solving process to something more… automatic. Like filling in a form, clicking “Next” a few times, and hoping the result is what you wanted. The craziest part is that this isn’t happening gradually. It’s happening faster than any of us realized previously.
Developers have always looked for ways to speed up the process. But there’s a real difference between using AI to enhance productivity and outsourcing thinking entirely. The latter has consequences. Some obvious, some not so much. Because if you don’t really know your own code, then who does?
Blog Summary:
This shift in coding might be convenient, but it comes with serious risks for businesses and developers alike. In this blog you will learn:
- Why blindly accepting AI-generated code can create long-term liabilities.
- How over-reliance on AI makes debugging more difficult.
- The hidden risks of AI patching problems.
- Why AI-generated code often gives the illusion of productivity.
- How vulnerabilities can go unnoticed when developers don’t understand their code.
- Why AI can’t replace experienced developers.
- The unintended consequences of AI on hiring and retaining top talent.
- How companies can use AI responsibly.
Table of Contents:
- Blog Summary:
- A Dangerous Precedent
- Debugging in the Dark
- AI-Generated Workarounds
- Illusion of Productivity vs. Real Productivity
- Security Risks
- Can AIs Replace Developers Today?
- Why Businesses Should be Cautious of Vibe Coding
- The Business Case for Custom Software Development
- The Right Way to Build Software
A Dangerous Precedent
At first glance, letting AI handle the bulk of coding might seem like a productivity win. Developers move faster. The code gets written. Features ship. What’s not to love? But speed without understanding is a liability, not an advantage.
The “Accept All” mentality, where developers blindly integrate AI-generated code without reviewing it, isn’t just a shortcut. It’s a shift in mindset. It normalizes not knowing what your software is actually doing under the hood.
If you’re not reviewing AI-generated code, how do you catch subtle errors? If you don’t understand how a function works, how do you optimize it? If something breaks in production, do you fix it? or just feed the error message back into the AI and hope for the best?
If no one fully understands the system, ownership disappears. And that’s where things get risky. Especially for businesses that rely on software as a core part of their operations. AI-generated code doesn’t come with guarantees. It doesn’t promise security, efficiency, or scalability. And once that codebase gets complex enough, even your most senior engineers might struggle to untangle what the AI has built.
Debugging in the Dark
Every developer knows that bugs are inevitable. But debugging isn’t only about fixing errors. It’s important to understand why they happened in the first place. And that’s exactly where AI-generated code starts to fall apart.
When you’re working with code you wrote yourself, debugging is a logical process. You retrace your steps, analyze the logic, and identify what went wrong. But what happens when the code is a black box of AI-generated functions that you never actually read?
The problem is that you’re not actually learning anything about your system. You’re not building intuition for how your code behaves. And you’re not preventing future issues. You’re just patching leaks as they pop up.
What’s worse is that AI doesn’t really understand bugs. It predicts solutions based on patterns, not deep comprehension. It can introduce subtle logic flaws, the kind that don’t throw an error immediately but silently corrupt data, create inefficiencies, or open security gaps.
AI-Generated Workarounds
Some bugs are stubborn. They don’t go away on the first fix. Or the second. That’s when the real debugging starts. Or at least, that’s how it used to be. Now, developers can just keep prompting AI until something compiles, run the code, and call it a day.
The problem is that AI doesn’t solve issues. It patches them. Instead of uncovering the root cause, it generates code that seems to work. Maybe it does. Maybe it just shifts the problem somewhere less visible. Either way, there’s no real understanding of what went wrong or why the new solution actually “fixed” it.
A workaround isn’t a solution. It’s a bandage over something no one took the time to diagnose. And when enough of those pile up, you get a fragile system, full of unknowns, waiting to collapse at the worst possible moment.
Illusion of Productivity vs. Real Productivity
AI-assisted coding makes everything feel faster. The code appears instantly. Features roll out quicker. Development cycles shrink. But speed alone doesn’t mean progress.
There’s a difference between writing code and building software. AI makes it easy to generate more lines, but that doesn’t mean those lines are scalable, well-structured, or even necessary. More code doesn’t always mean better results. Sometimes it just means more complexity, more dependencies, and more hidden issues waiting to surface. And simplicity is usually better for business.
Productivity is about how much of that code actually solves problems, scales with the business, and doesn’t have to be rewritten later. A team that leans too heavily on AI might seem efficient at first, but if they’re constantly revisiting past work and fixing AI-generated messes, they’re not moving forward, they’re just running in circles.
Security Risks
If no one’s reviewing AI-generated code properly, bad code doesn’t just slip through, it gets deployed. The real risk isn’t just that AI might introduce vulnerabilities. It’s that developers relying on AI might not even recognize them. Security flaws don’t always announce themselves with errors. They can sit quietly in the codebase, unnoticed until an exploit happens.
AI also has a habit of pulling solutions from public repositories, which raises another issue: supply chain attacks. If AI suggests a library with known vulnerabilities or questionable dependencies, and no one questions it, businesses could unknowingly expose themselves.
Security helps prevent problems before they happen. And that only works when developers take an active role in what gets shipped, not when they “Accept All” and hope for the best.
Can AIs Replace Developers Today?
AI can generate code, suggest fixes, and automate repetitive tasks. That much is clear. But there’s a difference between assisting development and replacing developers entirely. And businesses that fail to see that distinction are in for a rude awakening.
Software development involves making strategic decisions, structuring systems for scalability, and ensuring long-term maintainability. Without experienced engineers guiding the process, AI-generated code can quickly become a liability instead of an asset. As we covered in our blog, AI still struggles with complex problem-solving, adapting to changing requirements, and debugging effectively.
AI is a powerful tool, but that’s all it is: a tool. The difference between a functional codebase and a maintainable one isn’t how much AI was involved, but whether experienced developers were there to shape, review, and refine the final product.
Why Businesses Should be Cautious of Vibe Coding
AI is changing the way code gets written but it’s also changing the way developers feel about their work. The more companies shift towards AI-assisted development, the more they risk losing one of their most valuable assets: top tech talent.
Developers thrive on problem-solving, innovation, and ownership of their work. But if AI is making most of the decisions, what’s left for them? A workplace that relies too much on AI-generated code risks turning software development into a repetitive approval process. Let me be brutally honest: no skilled engineer wants to spend their days blindly accepting AI suggestions and debugging unpredictable outputs.
This has real consequences for hiring and retention. The best engineers want to work on meaningful, challenging projects where their expertise is valued. If AI takes over too much of the thinking, companies may find themselves with a talent pool that’s less engaged, less motivated, and ultimately looking for opportunities elsewhere.
The Business Case for Custom Software Development
If AI-generated code is so efficient, why do companies still invest in custom software development? Because business software is supposed to bring solutions that fit specific needs, scale effectively, and remain maintainable over time. AI, for all its speed, can’t guarantee any of that.
Custom software development is about making sure that what you want today, still works tomorrow, next year, and as your business grows. That requires strategic planning, clean architecture, and expert decision-making. AI can assist in the process, yes, but it can’t replace the experience.
For businesses that rely on technology to gain a competitive edge, offloading development to AI is just limiting. The companies that succeed aren’t the ones taking shortcuts. They’re the ones building software that’s designed to last.
The Right Way to Build Software
Software should do more than simply function. But getting there takes more than just code. It takes the right people to make the right decisions.
Our expert teams at CodingIT focus on scalability, security, and long-term maintainability from day zero. We don’t believe in shortcuts. We believe in custom software built for real-world business needs, not just what looks right in the moment.
AI is a tool, and like any tool, its value depends on how it’s used. With the right team guiding the process, AI can enhance development without compromising quality. But without expert oversight, it’s just another source of technical debt waiting to happen.
If you’re looking for software that’s built to scale, teams that prioritize quality, and a partner that understands what your business needs, CodingIT is the right choice. Let’s build something that lasts. Contact us today.