So-called “vibe coding” is both exciting and a little unnerving—it’s a shift away from the painstaking, line-by-line grind of traditional coding towards something more fluid and conversational.
The idea behind vibe coding is that you can collaborate with an AI, describe what you want, and watch as it constructs a working application. It promises a future where the barrier between a great idea and a functional product is thinner than ever.
However, as we stand on this new frontier, we have to ask ourselves: are we building the future, or are we taking a risky shortcut?
The seductive power of vibe coding
The appeal of vibe coding is understandable; to have an idea and see it come to life almost immediately. For years, we’ve been bogged down in the necessary but often tedious details of syntax, boilerplate, and configuration. Vibe coding promises to sweep that all away and democratise software development.
Think of a junior developer, just starting out. Instead of hitting a wall of complex documentation for a new framework, they can now ask an AI to scaffold a project for them and learn from the example it provides
Now think of a small startup. They can build a minimum viable product and get it in front of investors or customers at a speed that was once the preserve of huge, well-staffed teams.
This isn’t just an incremental improvement; I’ve heard developers talking about seeing their productivity increase by “orders of magnitude.” They describe getting into a state of creative flow, where the AI partner removes the friction that would normally jolt them out of their problem-solving mindset.
When you’re not wrestling with the ‘how,’ you can pour all your energy into the ‘what’ and the ‘why’. This is where true innovation happens. However, it’s worth noting that while more developers are jumping on the bandwagon, they are starting to become more sceptical.
Cracks in the digital foundation
For every story of incredible productivity, there’s a quiet, growing concern about the quality and integrity of what we’re building. This is where the dream of speed meets the harsh reality of long-term ownership.
The most immediate worry is the “house of cards” codebase. An AI might generate something that looks flawless and passes a basic test.
Imagine vibe coding and asking an AI to build a checkout flow for an e-commerce site. It will likely build a perfect ‘happy path’ where the customer enters their details correctly and the payment goes through. But did it account for race conditions, where two customers try to buy the last item in stock at the exact same millisecond? Did it consider the complexities of international currency conversion and floating-point arithmetic, which could lead to tiny rounding errors that cost the business thousands over time? Often, it doesn’t. You’re left with a system that is brittle, riddled with edge-case bugs that only appear in production.
This leads to a debugging nightmare. When you write code, you build a mental map of its logic. When an AI writes it, that map is a complete black box. Trying to fix a bug in a complex, AI-generated function is like being asked to repair a car engine you’ve never seen before, with no manual. You can see what’s going on and the wrong thing that’s happening, but the internal logic is a mystery you have to painstakingly reverse-engineer. That initial time you saved is gone, and then some.
Then we have security, which, frankly, is terrifying. AI models are trained on vast datasets of public code, which includes both good and horrifyingly insecure practices. An AI could easily suggest a code snippet that’s vulnerable to SQL injection because it’s seen that pattern thousands of times online. The risk of developers becoming too trusting is immense.
A developer, prompted by an AI, might be told to run pip install colourama to add some flair to a command-line tool. But a simple typo could lead them to install colorama instead, a malicious package in a ‘typosquatting’ attack designed to steal credentials. The AI provides the suggestion, but the human bears the consequence.
From vibe coder to AI conductor
Railing against this technology is futile—it’s here to stay. Rather than reject vibe coding, we need to shift our roles to being the person who directs, reviews, and refines the output of our AI partners. We must become AI conductors.
Here are some principles for this new role:
- Treat the AI as a copilot, not an autopilot: A good pilot always knows how to fly the plane manually. They use the autopilot to reduce workload, not to replace their own skill. In the same way, we must maintain our core coding fundamentals. We should be able to write the code ourselves if we have to. Use the AI for speed, but rely on your own judgement for direction and safety.
- Rigorous review is non-negotiable: No AI-generated code, especially anything complex or critical, should go into a production branch without a thorough human review. This is the new baseline. In fact, this could be the dawn of “AI-assisted pair programming,” where the AI generates the first draft and a human developer acts as the senior partner, interrogating the code, questioning its logic, and demanding improvements.
- Master strategic prompting: The quality of the output depends entirely on the quality of the input. A lazy prompt like “make me a login page” will get you lazy, generic, and likely insecure code. A strategic prompt is a skill in itself. For example: “Using Python and Flask, create a secure login route. Ensure you use Flask-WTF for form handling, sanitise all user inputs to prevent XSS, hash passwords using bcrypt with a salt, and implement robust server-side validation with clear, user-friendly error messages.”
- Cultivate healthy scepticism: We must encourage our teams to challenge the AI’s output. Ask why it chose a particular library or algorithm. Is there a more performant, more secure, or more maintainable solution? This critical thinking is what separates an engineer from a simple instruction-follower. It’s the most valuable skill we possess, and we must not let it atrophy.
Vibe coding heralds a new beginning for developers
For me, the rise of vibe coding isn’t the beginning of the end for developers. It’s the end of the beginning. We’re moving beyond an era where our primary value was in our ability to memorise syntax and are entering one where our value lies in our taste, our architectural vision, our security paranoia, and our unwavering focus on quality.
The developer of the future is a conductor. They may not play every instrument in the orchestra, but they know how each one should sound, and they are responsible for bringing them all together to create a beautiful, functional, and robust symphony.
(Photo by Ashley Whitlatch)
See also: Why developers are leaving jobs that can’t support AI
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.
Explore other upcoming enterprise technology events and webinars powered by TechForge here.
