AI Power Coding: The Future of Multi-Skilled Software Teams?

AI is transforming how people interact with code, giving rise to distinct approaches that range from fully assisted development to casual experimentation. While Pro-Code, AI-Assisted Coding, and No-Code have clear definitions, other emerging approaches— such as Vibe Coding —add a new dimension. In this article, we define the full coding spectrum, highlight the key differences between these methods, and propose a new term which describes how we’re working on our software development projects at Rendr.

The Coding Spectrum in the Age of AI

Pro-Code (Traditional Development)

Pro-coders are experienced developers who write, debug, and optimize code from scratch. They may use AI tools for efficiency but retain full control over their work.

Key Traits:

• Deep technical expertise
• Writes, reviews, and optimizes code manually
• Uses AI tools selectively (e.g., Copilot, Cursor) for efficiency

AI-Assisted Coding (Developer Productivity Boost)

AI-assisted coders are professional developers leveraging AI to 10x their output. AI helps with suggestions, bug fixes, and refactoring but does not replace traditional coding skills.

Until mid 2024, all of our devs fell into the Pro-Coder category; however, we are now fully leveraging AI to strive for that 10x with the AI Assisted Coding method.

Key Traits:

• AI enhances but does not replace expertise
• Faster coding through AI-generated suggestions
• Uses AI for debugging and refactoring

No-Code (Drag-and-Drop Development)

No-code platforms allow non-developers to build applications through visual interfaces, eliminating the need for traditional programming.

Key Traits:

• No programming knowledge required
• Drag-and-drop components (Bubble, Webflow, Glide)
• Limited flexibility and scalability

Vibe Coding (Exploratory AI-Driven Development)

Vibe Coding is an emerging trend where users interact with AI tools like Cursor with no structured approach, embracing spontaneity to see where the process takes them.

Key Traits:

• No product brief, design, or structured goals
• Keeps hitting "Accept" until the output looks right
• Minimal concern for development environments, staging, or best practices
• Likely lacks GitHub, CI/CD setup, or proper database management
• Mostly experimental, not necessarily aiming for a production-ready product

Power Coding (Using AI for Learning and Development)

At Rendr, we have been leveraging AI to engage our non-technical resources in more technical way. Our plan is to onboard most of the team into junior dev roles where they will largely use AI (at the time of writing, primarily Cursor) to complete simple technical projects.

With supervision from senior devs, we’re finding that this is the sweet spot to optimise our resources and leverage AI to the max. Longer-term, we have our junior devs on learning and development plans to transform them into fully fledged developers.

The idea is that we upgrade the team into a multi-skilled powerhouse: our devs are already doing a bit of design, a bit of project management here and there; so when we skill up our non-tech resources, everyone will be able to do a bit of everything.

As with all things, there might be downside as well as upside, but we believe the positives will prevail; there’s too much upside for it not to work:

• Fully utilised team.
• More in-depth knowledge of the cross functional skills required to build.
• Team members get a chance to add new skills to their arsenal.
• Less chance of people getting bored in a role.
• Much higher employee engagement and satisfaction.

We’ve Named It AI Power Coding

As we’ve stated publicly, we’re not big fans of the term Vibe Coding—it’s too unstructured for the way we work. As an agency, our clients have business plans, tight budgets, and clear goals, so we need a professional approach. That’s why we’ve coined our own term: AI Power Coding.

This is our structured, AI-driven approach to turning non-technical team members into junior developers while optimizing resource allocation. AI Power Coding isn’t just about building with AI; it’s about learning, questioning, and improving. Instead of just clicking “Accept All,” our junior devs actively engage with AI-generated code, refine it, and develop a deeper understanding of software development.

With oversight from senior developers, this approach bridges the gap between AI-assisted coding and traditional software development—allowing us to build better products while future-proofing our team. The result? A fully utilized, multi-skilled team that delivers exceptional outcomes for clients.

But instead of just using “Accept All”, our junior devs will be learning about the development process as a whole because, at the end of the day, the best use case for AI as it stands is as an AI-coding assistant, allowing pro-coders to 10x their output.

Key Traits:

• Works from a structured product brief, goals, and requirements
• Uses AI to learn coding, not just generate code blindly
• Understands technical aspects (env vars, staging, commits, error resolution)
• Questions AI’s output, optimizes code for efficiency
• Debugs issues by reading and fixing the code, rather than retrying AI responses indefinitely
• Builds scalable applications with a structured component and API architecture
• Conscious of best practices, aiming for maintainability and production readiness

Why AI Power Coding is the Future

AI Power Coding represents a bridge between traditional coding and no-code, allowing users to build sophisticated applications while learning to code professionally. It democratizes software development, making it accessible to non-developers who are serious about understanding how code works, not just generating it.

As AI continues to evolve, this approach will likely shape the next generation of self-taught developers, who start with AI and transition into pro-coders over time. AI Power Coders are building real, scalable applications, not just playing around.