Digital Economy Dispatch #246 -- The Promises and Pitfalls of Vibe Coding

I've worked with technology for over three decades, writing countless lines of code in various languages for diverse systems. Some projects were slow, painstaking endeavours for mission-critical situations. However, most were developed rapidly to test ideas, gather user feedback, or simply deliver new features. Because of this, I believed I had a strong grasp of the benefits and limitations of rapid application development.

The arrival of GenAI, though, has dramatically changed how quickly we can create highly functional, software-intensive systems. To understand what this means in practice, I recently spent six hours building a comprehensive AI Strategy Assessment tool using only conversational prompts with Claude AI. This experience was eye-opening. The bottom line is that everything I thought I knew about prototyping and early-stage development has undergone a fundamental shift.

What truly excites me isn't just "vibe coding"—the emerging practice of building applications through natural language conversation with AI. Instead, it's reflecting on what this capability means for leaders and decision-makers. The key question is “How far can we now get rapidly test ideas, engage users, and validate concepts without the traditional overhead of formal development processes?”.

The Moment Everything Changed

It started with a simple request: "Create an AI assessment tool". Within thirty minutes, I had a functional web application that evaluated organizational AI readiness across ten strategic dimensions. By the end of the day, I had a professional-grade assessment platform that generated executive-quality reports with strategic recommendations, investment guidelines, and implementation roadmaps.

The speed wasn't just impressive – it was paradigm-shifting. In traditional development, this would have required days of requirements gathering, UI/UX design, backend development, and testing. Here, I was iterating in real-time, watching ideas transform into functional reality through conversation.

It’s quite seductive. A few lines of text in a prompt to Claude and out comes a stream of code that you can deploy locally to a website, or distribute in the cloud. What could be easier?

But as I discovered, this remarkable capability comes with equally remarkable limitations and concerns that every leader looking to adopt AI-assisted development needs to understand.

The Magic of Conversational Development

What struck me first was how naturally the process flowed. Instead of writing technical specifications, I was describing business requirements: "I need an assessment that helps executives understand their AI strategic maturity." Claude didn't just build a generic survey – with a little nudging it created contextually appropriate questions about AI governance, competitive positioning, and investment strategy.

The AI tool understood business nuance in ways that surprised me. When I asked for "executive-quality output," it generated reports with proper section headers, strategic insights, and professional formatting suitable for inclusion in presentations. The assessment questions weren't just functional – they demonstrated an understanding of AI strategy frameworks and business implications.

This is where vibe coding reveals its first major promise: it bridges the gap between business vision and technical implementation. As someone who spends his time bridging these two perspectives, I was watching business requirements transform directly into functional solutions without the typical translation layers that introduce delays and misunderstandings.

When Reality Hit

The honeymoon period lasted about four hours. As I pushed for more sophisticated features – enhanced reporting, complex scoring algorithms, professional PDF generation – the elegant simplicity began to crack.

The first challenge is simply gaining some control of this very powerful tool. For example, if you ask a GenAI tool like Claude to “update the generated report”, it is likely to make some decisions that have important consequences for the quality and accuracy of what you produce. In my case, it invented a set of “industry benchmarks” to use for grading the assessment without telling me. On closer inspection, I asked where they originated, and Claude admitted they were invented based on “a best guess”. Subsequently, I was much more careful in what I asked it to do, often telling it what I didn’t want, and then checking to see if it had kept to this.

On top of that, Claude made frequent errors as it over complicated the solution. Looking at what was generated, the issue appears to be poor code organization. What started as clean, functional JavaScript gradually became an unwieldy mess of nested functions and duplicated logic. Claude was excellent at adding features but struggled with refactoring existing code for maintainability. Each enhancement was built upon previous code rather than optimizing the overall architecture. This makes understanding what is generated a bit of a nightmare.

Then came the debugging challenge. When I introduced a complex reporting function that broke the application, Claude couldn't effectively diagnose the problem. We were back to traditional troubleshooting – examining console errors, testing individual functions, and methodically eliminating issues. The conversational magic disappeared the moment we needed genuine technical problem-solving.

Most revealing was what happened when I asked Claude to "make this more professional." It could enhance visual design and add features, but the underlying architectural decisions remained fundamentally unchanged. The application worked, but it wasn't built with the scalability, security, or maintainability considerations that enterprise applications require. It certainly couldn’t replicate all the experience you gain from fielding solutions that work and are robust.

The Strategic Implications

For me, this experience has highlighted something crucial about AI-assisted development that goes beyond technical capabilities. Vibe coding excels at translating business requirements into functional prototypes with unprecedented speed, but it operates within significant constraints that define its strategic value. Understanding these boundaries is critical to know if, when, and how to use it.

For rapid experimentation and user engagement, these constraints matter less than the speed advantage. When I needed to test whether users would find value in a comprehensive AI assessment, having a functional prototype in hours rather than months was hugely important. The ability to gather real user feedback, iterate on requirements, and validate core assumptions before committing significant resources represents a genuine competitive advantage.

But for applications that need to handle sensitive data, integrate with enterprise systems, or scale to large numbers of users, the limitations become critical risks. The code that Claude generates works for demonstration and testing, but lacks the security implementations, error handling, and performance optimizations that production applications require. I would need to conduct a series of detailed reviews before attempting to use this in any meaningful situation.

What This Means for Innovation

From this and other experiences, I've come to see approaches such as vibe coding as a powerful tool for a specific phase of innovation – the crucial early stage where ideas need to become tangible enough to test and refine. Traditional development processes often kill promising concepts before they can prove their value, simply because the investment required to build functional prototypes exceeds the confidence level in unvalidated ideas.

Vibe coding changes this perspective. When you can move from concept to functional prototype in hours, the risk-reward equation shifts dramatically. Ideas that wouldn't justify weeks of development effort suddenly become viable for rapid testing and validation.

This has profound implications for how organizations approach innovation. Instead of an over-emphasis on elaborate requirements documents and comprehensive project planning, teams can start with functional prototypes that stakeholders can actually experience and critique. The feedback loop becomes immediate rather than theoretical. But it must be used with care.

The Human Element

What surprised me most was how the conversational development process affected my own thinking about the product and the process. As with other rapid prototyping practices, because you can see immediate results from each request, you find yourself iterating on requirements in real-time rather than trying to specify everything upfront. This led to discoveries about what the assessment tool I was creating really needed to accomplish that wouldn't have emerged through traditional planning processes.

But this speed was also a 2-edged sword. It also meant that I didn’t always think through what I needed and asked for. It is so easy to just type the next thing that comes into your head that there are times you really need to force yourself to step back and take a deep breath. What is needed? Why? What value will it bring? Asking such questions remains critical.

In this way, the AI tool became a collaborative partner in ways I hadn't expected. When I asked for "more professional recommendations," Claude didn't just change formatting – it enhanced the strategic depth of the content, adding investment requirements, timeline considerations, and implementation frameworks that I hadn't explicitly requested but clearly needed. It contributed in a creative way to the project.

This collaborative dynamic suggests that vibe coding's value extends beyond just speed. It creates a different kind of design process where business stakeholders can participate directly in solution development rather than waiting for technical teams to interpret and implement their requirements.

The Reality Check

Despite these advantages, I still consider my background in software engineering to be a significant advantage in creating solutions in this way. My experience reinforced why traditional development expertise remains essential. By the end of the project, I had a powerful demonstration tool that could engage executives and validate the assessment concept. But I also had a codebase that no professional developer would want to maintain and functionality that couldn't scale beyond its current scope. The rest, as they say, is engineering.

The application serves its purpose perfectly – testing market demand, gathering user feedback, and proving the value proposition. But if this assessment tool becomes successful enough to warrant broader deployment, it will need to be rebuilt using proper development practices, security implementations, and scalable architecture.

This isn't a failure of vibe coding – it's a recognition of its proper role in the development lifecycle. The conversational approach excels at rapid prototyping and concept validation, but creating production-ready applications remains the domain of traditional engineering expertise.

Looking Forward

As I reflect on this experience, I see vibe coding as part of a broader shift toward more accessible technology development. Just as spreadsheets democratized data analysis and presentation tools democratized design, AI-assisted development is democratizing the ability to create functional prototypes and test digital concepts.

For leaders responsible for innovation and digital transformation, this represents a significant opportunity. The ability to rapidly prototype solutions, test user engagement, and validate concepts before committing significant development resources can accelerate innovation cycles and reduce the risk of building solutions that nobody actually wants.

But success requires understanding both the capabilities and limitations. Vibe coding is powerful for exploration and validation, but it's not a replacement for professional development when applications need to scale, integrate, or handle sensitive operations. An efficient approach includes making time for solutions to be created in a considered way, appropriate for the context and audience.

The organizations that will benefit most are those that can leverage AI-assisted development for what it does best – rapid experimentation and early-stage validation – while maintaining the engineering capabilities necessary to transform successful prototypes into production-ready solutions.

As I continue exploring these capabilities, I'm convinced we're at the beginning of a fundamental shift in how digital products are conceived, tested, and refined. The question isn't whether AI-assisted development will change innovation processes, but how quickly leaders will adapt their approaches to leverage these new capabilities effectively, blending them with more traditional software engineering practices. And perhaps just as importantly, gaining insights into what this does to the people responsible for developing and delivering new products and services.