Vibe Coding: Productivity Hack or Time Bomb?

AI isn’t going to steal your job—but someone who knows how to use it might. 

That’s a good way to describe AI’s current role in business processes. Because, while it’s not here to replace human talent, it is changing the way work gets done. 

This shift is especially noticeable among developers, where AI adoption is growing rapidly. In fact, Stack Overflow estimates that 80% of enterprise software engineers are using AI to write code. 

One of the emerging practices in this space is called “vibe coding”, a trend gaining traction with both technical and non-technical users. 

But what exactly is it? How does it work? And more importantly—how is it shaping the software industry?  

Let’s check this out in today’s episode. 

[Quick reminder: next week, the Inclusion Cloud team will be at the Irving Convention Center for the Convergence AI Dallas 2025! We hope to see you there to tell you how we can help transform your business operations with agentic AI solutions. If you want to know more, check out our LinkedIn event]

What Is “Vibe-Coding”? 

Like a lot of trends, this one started in online communities (think Reddit) when AI coding assistants began gaining traction with developers. In this case, we’re talking about a more relaxed, intuitive way of interacting with AI—where people, even those without a technical background, just kind of “throw vibes” at it. 

You give the AI a rough idea or loose direction of what you want, and it takes it from there—filling in the blanks and building something useful. It’s a great example of how AI is reshaping the talent landscape by empowering both technical and non-technical folks to do more, faster. 

Vibe coding in real life 

Let’s make it real with a quick example. Say you’re in marketing and need a simple survey form for a landing page. You don’t know how to code, but you do know what you want it to do. 

This way, you tell an AI: 

️ “Create a form with name, email, and a short message box. Make it look modern.” 

And it gives you this: 

<form style="max-width: 400px; margin: auto; font-family: sans-serif;"> 
  <label>Name</label><br> 
  <input type="text" style="width: 100%; padding: 8px;"><br><br> 
  <label>Email</label><br> 
  <input type="email" style="width: 100%; padding: 8px;"><br><br> 
  <label>Message</label><br> 
  <textarea style="width: 100%; padding: 8px;"></textarea><br><br> 
  <button style="background: hotpink; color: white; padding: 10px 20px; border: none;">Send</button> 
</form>         

You didn’t think about HTML structure, CSS rules, or browser compatibility. You just described the vibe—and the AI did the rest. 

The pros and Cons behind Vibe-Coding 

✅ Pros 

• Fast prototyping – Build working demos in minutes. 

• Accessible to non-devs – Natural language prompts lower the barrier. 

• Speeds up feedback loops – Great for Agile and Lean teams. 

• Reduces dev bottlenecks – Frees up engineers for high-value tasks. 

⚠️ Cons 

• Risk of spaghetti code – Output may be hard to read and prone to security flaws. 

• Hard to maintain – Debugging “AI guesswork” can be painful later. 

• Technical debt – Quick fixes now can mean big rework later. 

• Not production-ready – Often needs expert review and refinement. 

The productivity gap in the AI era 

Contrary to what many people might think, experienced engineers are becoming even more valuable in the age of AI. Just like we say an AI model is only as good as the data it’s trained on, AI-generated code is only as good as the developer reviewing it. 

Sure, some repetitive or lower-impact entry-level tasks might get automated—but the pipeline isn’t disappearing, it’s evolving. In fact, not all AI-assisted coding is vibe coding.  

Many developers use it in their workflows to handle repetitive coding tasks once they have the technical specs or a defined architecture. They still review, refactor, and align the output to broader goals. 

The seniority difference here is that a junior dev just asks the code to the AI and then copy and paste it without reviewing, prompting until something seems to work. An experienced and certified one, on the other hand, has a deeper understanding of the code and knows how to read it and optimize it. 

What’s really changing is how we measure productivity. In today’s AI-driven world, the legendary “10x developers” need more than just solid software fundamentals—they also need strong AI fluency.  

That means knowing how to prompt effectively, review critically, and iterate with purpose—treating AI as a collaborative partner. On the flip side, developers who rely too heavily on AI without understanding the code behind it will likely struggle under pressure, even if their results appear to work at first glance. 

It’s not about replacing people—it’s about finding talent that can adapt to new technologies. At Inclusion Cloud, we can help with that. Let’s connect and talk about building the right foundation for your AI systems.