Vibe Coding: A New Tool in the Digital Transformation Toolbox
The landscape of digital development is shifting beneath our feet. With the emergence of "vibe coding" – the practice of using natural language to direct AI in creating functional code and prototypes – we're witnessing a fundamental change in how digital experiences come to life. At Two Point O, we're embracing this evolution while maintaining our commitment to delivering enterprise-grade digital solutions that drive real business value.
The Promise of Vibe Coding
Vibe coding represents a paradigm shift in how we bridge the gap between business vision and technical implementation. Traditionally, our process involves extensive mapping sessions, wire-framing, and concept designs – all aimed at capturing the intent behind business ideas and creating common ground between stakeholders, analysts, and developers. Vibe coding offers an intriguing shortcut: the ability to articulate desires in natural language and see them transformed into working prototypes almost instantly.
We're genuinely excited about this development. One of our clients has already adopted this strategy, and we're keen to see how we can evolve this tactic together with our clients to accelerate the development process. The ability to express conceptual desires more naturally and prototype faster helps bridge that crucial gap between concept and implementation.
For UI/UX professionals, this could be transformative. After all, a working web-based prototype is inherently superior to a static prototype created with tools like Figma. It's worth noting that this shift could be disruptive for established design tools – though how platforms like Figma will adapt to this new paradigm remains to be seen. We appreciate Figma as a collaborative design platform, but the industry may be approaching an inflection point.
edit, 07th of May
My ink was barely dry when Figma announced their "Figma Make" feature today, directly embracing the vibe coding concept we discussed. This premium feature uses Anthropic's Claude 3.7 Sonnet to transform design descriptions into working code. The timing validates our analysis that design tools would adapt to this paradigm shift. Notably, Figma is positioning this as an enhancement for enterprise clients rather than replacing designers, aligning with our perspective that AI will augment human expertise, not replace it.
source CNBC: Figma introduces ‘vibe-coding’ AI software design feature
The Reality Check: Limitations and Considerations
However, we must be clear-eyed about the limitations.
GenAI for development is truly transformative for how we work today and in the near future. But vibe coding is part of that ecosystem, not the silver bullet that will bypass the engineering part of building digital products altogether. For strategic platforms, it will be hard to get all dimensions right without proper architectural thinking and engineering expertise.
Vibe coding only produces user-friendly, accessible, and conversion-optimised results based on the knowledge embedded in the AI and the skill of the prompter. The underlying insights of UX experts, UI designers, analysts, and domain specialists remain crucial. While you can partially compensate through sophisticated prompting, you can't ask questions about things you don't know exist. Even with the best prompts, achieving professional-grade visual experiences that adhere to all necessary standards requires expert oversight.
The Hidden Complexities
One of the most significant pitfalls we've observed is the illusion of near-completion that vibe coding can create. While some digital products might indeed be "good enough" with vibe-coded solutions (especially if deployment is also automated through AI), serious challenges emerge with maintenance and scaling.
Consider a practical example we've encountered: locale management in internationalised applications. AI might generate code with language strings copied throughout the codebase hundreds of times. While an AI can keep these synchronised during generation, this approach becomes problematic for enterprise platforms facing compliance requirements, security audits, and evolutionary maintenance. When a platform needs to scale or adapt to new requirements, such architectural shortcuts can create significant technical debt.
Moreover, enterprise platforms must balance numerous non-functional requirements like performance optimisation, cost efficiency, scalability, and security. These often involve complex trade-offs that require experienced judgment. While even seasoned professionals can make imperfect decisions, we mitigate risks through collaborative review processes and architectural discussions – something that's challenging to replicate in a pure vibe coding environment.
Unchecked AI-generated code can massively amplify technical debt; the hidden problems that make software brittle and costly to maintain.
The Business Impact of Getting It Wrong
The stakes are high when AI-generated code fails to meet enterprise standards. Consider these potential business impacts:
- 1
Security vulnerabilities could expose sensitive customer data, leading to regulatory fines and reputational damage
- 2
Performance issues might result in lost sales and decreased customer satisfaction
- 3
Maintenance nightmares can dramatically increase operational costs and slow down feature development
- 4
Scalability limitations could prevent businesses from capitalizing on growth opportunities
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Integration challenges might block critical business processes or partner connections
These aren't theoretical concerns – they translate directly into financial risk and competitive disadvantage.
Strategic Integration of AI
We've been investing in AI integration for over two years, not just at the tactical level (like providing our engineers with AI coding assistants), but strategically throughout our development lifecycle. We're working to answer fundamental questions:
How should features be documented to optimally feed GenAI systems?
How can we standardise our target architectures to build deep AI knowledge?
What quality metrics ensure AI-assisted development meets our standards?
How do we maintain our commitment to delivering business value while leveraging AI efficiency?
Our stance is clear: GenAI for development is here to stay and is genuinely transformative. AI-driven development is a valuable part of this ecosystem, but it's not a complete replacement for thoughtful engineering. For strategic platforms, getting all dimensions right requires a hybrid approach that combines AI capabilities with human expertise.
Finding the Balance
We see vibe coding as a powerful addition to our conceptual toolbox – one that sits alongside, not instead of, our established practices. It excels at:
Rapid prototyping for stakeholder feedback
Exploring multiple design directions quickly
Creating proof-of-concepts for new features
Accelerating the initial development phases
Improving communication between business and technical teams
However, for enterprise-grade solutions, we maintain our commitment to:
Architectural review and planning
Security and compliance oversight
Performance optimisation
Scalability planning
Maintenance and lifecycle management
Real-World Practitioner Insights
Experienced vibe coding practitioners have discovered valuable patterns that align with our approach. One developer who spent six months building multiple products solo using AI tools shared these key lessons:
"Your job is to lead the machine. AI isn't 'coding for you.' It's co-piloting. You're still the captain," the developer notes, emphasising that human leadership remains essential.
Their experience reveals practices that separate successful AI-augmented development from frustrating dead ends:
Plan meticulously before prompting: Create proper product documentation first, which serves as your compass when AI loses context
Scope AI interactions narrowly: Use AI for specific components rather than entire applications, with clear boundaries and instructions
Implement rigorous version control: AI tools will inevitably break critical functionality, making Git essential
Schedule regular refactoring session: Technical debt accumulates at "AI speed" - faster than traditional development
Maintain clean architecture: As the developer warns, "You can vibe-code fast, but you can't scale spaghetti"
Moving Forward
We're well on our way to maximising GenAI in our process without compromising our values and beliefs about quality software. We see it as a way to increase both quality and value, not just decrease costs. As we often say, "garbage in is garbage out" – our role as AI-augmented software engineers is to be the gatekeepers who ensure quality goes in and business value comes out.
The future of digital development will likely be a thoughtful blend of AI assistance and human expertise. Vibe coding opens exciting new possibilities for rapid prototyping and improved stakeholder communication, but it's the combination with experienced engineering, thoughtful architecture, and strategic thinking that will deliver the enterprise-grade solutions our clients need to succeed in an increasingly digital world.
At Two Point O, we're committed to staying at the forefront of this evolution, continuously adapting our proven software processes to incorporate AI capabilities while maintaining the high standards our clients expect. The goal isn't to replace human expertise but to amplify it, delivering more business value than ever before.
