Photo by Nahrizul Kadri on Unsplash
“Vibe coding” or AI-assisted coding has become popular in the past few months. It simply means prompting the AI to generate your codebase. If a bug occurs, instead of digging into the code, you tell AI to fix the bug. Some are even claiming it could allow anyone, without any software development training at all, to easily build software.
Now, AI-assisted coding is fantastic for non-technical founders with basic coding skills, indie developers, and small teams building CRUD apps, MVPs, simple mobile apps or automating repetitive tasks. It accelerates iteration cycles, allowing more people to test ideas with minimal development effort. In tech and startup community circles like HN, Product Hunt etc., there’s often a sample bias, where everyone is in the startup space, using AI tools or building AI agents.
However, once you move into enterprise applications, especially those dealing with critical sectors like banking, healthcare, cybersecurity and government systems, the stakes are entirely different. Enterprises have regulatory requirements, compliance concerns, and internal security policies that make them extremely cautious. They simply cannot afford to let any third-party AI company touch their codebase, as it poses risks to security, compliance, and long-term maintainability.
First, there are serious concerns about protecting intellectual property with AI-assisted coding. If you suggest that their code will be sent to a cloud-based LLM, good luck navigating that conversation. They are deeply protective of their proprietary codebases, and sending any part of it to an external LLM(hosted by a third-party) is a non-starter.
Second, AI frequently produces bug-ridden code, and an oversight by an "experienced" developer is constantly needed to effectively use AI. AI coding assistants work best as enhancements for experienced developers, not as a replacement. Subtle logic bugs, poor optimization, and architectural inconsistencies can add up and lead to performance degradation, scalability challenges, and hard-to-debug failures that compound over time.
Third, a huge and under-discussed issue. The security risks that AI can possibly introduce into the code are significant. AI models trained on public codebases often inherit bad security practices, which means they are likely to produce insecure code. While this might be tolerable for a fun weekend project, in an enterprise setting, these risks are unacceptable, potentially leading to security vulnerabilities.
Fourth, unchecked AI usage will accumulate massive technical debt. If you let AI generate code without understanding what each line is doing, the technical debt will accumulate at an alarming rate. When things go awry, you won't know where to start looking to fix them. In any firm, the ability to debug and refactor existing code is a huge part of a software engineer’s responsibility, more so than writing code from scratch. If you’re blindly accepting AI suggestions, your codebase will become an opaque box, resulting in code that is hard to debug and an architecture that will not scale.
In my software engineering career, I have been taught to write "every line of code with a purpose." You should know why you wrote that line, what it is doing, and how it will affect other parts of the code. AI will not replace strong engineering fundamentals, but it will make good engineers more productive while amplifying the mistakes of bad ones.
If you're a software engineer, it's crucial to be aware of AI's limitations and associated risks. Cautiously explore its potential through side projects or weekend experiments to stay up-to-date with emerging AI technologies. Again, there is absolutely no replacement for strong software engineering fundamentals. If your goal is to progress further in this profession, mastering the ability to understand, navigate, and debug complex code will always be more valuable than relying on AI tools.
AI-assisted coding is here to stay, but its role in professional software development will depend on the context. Startups and solo founders can reap significant benefits, while enterprises, understandably, will remain skeptical, especially when it comes to security, compliance, and long-term maintainability.
Hi, I’m Ash. With 10 years of experience in software engineering, I’m passionate about using technology to improve efficiency and create real value.
I also built Brisqi, a personal Kanban app designed to help people stay organized and in control.
Outside of work, I spend time hiking in the mountains or snowboarding — nature is where I recharge and find much of my inspiration.
Top comments (0)