From Coding to Spec-Writing

Navigating AI's Impact on Developers

Will AI Make Writing Code Obsolete?

Over the years, software development has evolved dramatically. Initially dominated by linear, structured waterfall methodologies, software development transitioned significantly with Agile's iterative, flexible approach. But recently, the arrival of Generative AI tools like large language models (LLMs) has accelerated this evolution even further, embedding intelligence into every stage of the development lifecycle. So, the big question emerges: Will writing code soon become an obsolete skill?

The reality is more nuanced than that.

A Quick Look at Today's Development Cycles

Historically, software development relied heavily on structured processes:

• Waterfall: Requirements → Design → Coding → Debugging → Testing → Maintenance

• Agile: Iterative cycles repeated as needed: Requirements → Design → Development → Testing → Review → Deployment

Additionally, development methodologies like Test-Driven Development (TDD), where developers write tests before implementing functionality, and Behaviour-Driven Development (BDD), which uses natural-language scenarios to describe software behaviours, have also become prominent, enhancing clarity, collaboration, and quality.

While Agile and related methodologies remain effective, Generative AI is dramatically reshaping even these flexible practices by automating tasks previously requiring extensive manual intervention.

How AI Tools Are Transforming Development

Generative AI tools significantly reduce the manual effort required for routine coding tasks by:

• Rapid Prototyping: Generating boilerplate and scaffolding quickly.

• Automating Repetitive Tasks: CRUD operations, data pipelines, and integrations.

• Enhanced Debugging and Testing: AI-driven error detection, test case generation, and optimisation.

• Automated Project Management: AI-assisted scheduling, resource allocation, and timelines.

• Documentation and Refactoring: Automatically generating documentation and recommending code improvements.

• Security and Compliance: Real-time detection and mitigation of vulnerabilities.

• UX/UI Design: Automating interface creation and enhancing user experiences.

• DevOps Automation: Streamlining CI/CD processes with predictive capabilities.

These advancements in AI tools lay the groundwork for even more sophisticated, integrated coding support through next-generation AI coding assistants.

AI Assistants in 2025: Not Just Helpers, but Partners

AI coding assistants don’t just complete snippets; they now handle comprehensive end-to-end project implementation:

• Windsurf IDE: Deep contextual coding support through "Cascade Technology".

• GitHub Copilot: Suggests entire blocks of code, summarises pull requests, and automates documentation.

• Cline (formerly Claude Dev): Natural language execution of complex coding tasks integrated directly into IDEs like VS Code.

• Cursor: Advanced AI-driven coding environment optimising productivity with intelligent code generation and real-time assistance.

• JetBrains AI Assistant: Seamless integration across JetBrains tools, providing intelligent coding, automated testing, and more.

• Claude Code: Last, but by no means least, an agentic coding tool that lives in your terminal.

With AI handling more routine coding tasks, developers can increasingly focus on strategic, higher-order responsibilities.

Moving from Writing Code to Writing Specifications

The biggest shift isn't the disappearance of code, but the changing focus from writing it manually to crafting precise specifications that AI translates into code. Here's how workflows are changing:

• Traditional Workflow: Requirements → Design → Manual Implementation → Manual Debugging and Testing → Deployment

• AI-Driven Workflow: Detailed Specifications → High-Level Design → Prompt Engineering & AI Code Generation → AI-Assisted Debugging → Deployment & Iterative Refinement

Alternative Workflow: Spec-to-Validation Cycle

An alternative yet equally important workflow involves:

Detailed Specifications → AI-generated Code → Automated Validation against Specs → Manual Validation & Refinement → Final Deployment

In this cycle, validation becomes crucial to ensure the AI-generated output aligns perfectly with the original specifications.

In this new paradigm, code becomes an artefact derived from well-defined specifications rather than something handcrafted line-by-line.

Emerging Roles for Developers

As coding tasks evolve, developer roles adapt accordingly. Expect to see new specialist roles emerging:

• Specification Engineer: Experts in creating precise and robust software specifications.

• Prompt Engineer: Specialists in crafting effective interactions with LLMs.

• AI Integration Architect: Professionals ensuring seamless integration between AI-generated outputs and existing systems.

• AI Validation Specialist: Professionals specialising in verifying that AI-generated outputs align perfectly with original specifications.

With developers increasingly focusing on higher-level responsibilities, another significant trend emerges - the orchestration of multiple specialised AI agents.

The Rise of AI Orchestration and Multi-Agent Systems

One of the most significant advancements by 2025 is the orchestration of multiple specialised AI agents, collaborating seamlessly to enhance productivity and innovation. This shift from single-model dependencies to coordinated ecosystems of AI agents represents a major leap in efficiency and capability.

What Skills Should I Learn For This AI-Driven Future?

The rise of AI doesn't eliminate the need for skilled developers; rather, it transforms what skills are essential:

Technical Skills:

• Mastery of AI-powered coding tools and environments

• Expertise in specification writing, prompt engineering, and AI orchestration.

• Deep understanding of system architecture, security, compliance, and governance in AI-driven environments.

Non-Technical Skills:

• Strategic thinking for designing scalable architectures.

• Exceptional communication skills for precise specifications and effective collaboration.

• Critical thinking skills for validating AI outputs and refining development strategies.

So, Is Coding Really Becoming Obsolete?

Not entirely and not immediately. Although AI automates routine coding, manual programming remains critical for highly specialised tasks, such as:

• Performance-intensive systems

• Complex algorithm design

• Cutting-edge research and innovation

• Bespoke security solutions

• Regulated Industries and Defence software/tools

Plus new or niche programming languages and frameworks initially lack comprehensive support by Generative AI models, necessitating manual coding and ongoing development. Developers must still manage scenarios involving rapidly evolving technologies, domain-specific languages, or specialised frameworks that AI has yet to master fully.

Ultimately, the integration of generative AI tools does not signal the end of coding. Instead, it represents a strategic shift, enabling developers to transition from manual coding tasks to higher-value roles in innovation, specification design, validation, and strategic thinking. Developers and organisations that embrace this shift proactively will be best positioned to leverage AI’s full potential.