The Return to Hand-Written Code: Analyzing AI Adoption and Retraction in Software Engineering

The Tension Between AI Velocity and System Understanding

While Large Language Model (LLM) coding assistants have become ubiquitous, a segment of the software engineering community is actively limiting or reverting their use of AI to prioritize mental models and code quality. The primary driver for this shift is the realization that the speed of code generation does not always correlate with the speed of problem-solving or the stability of the final product.

The Cognitive Cost of AI-Generated Code

For many developers, the process of deriving a solution and building a mental model is the most critical part of software engineering. Relying on AI to generate the implementation can decouple the developer from the logic, leading to several systemic issues:

• Loss of Flow State: Automated suggestions can interrupt the cognitive process of streaming a vision from the mind into code.
• Masking Architectural Problems: When AI makes a difficult task feel easy through rapid generation, developers may overlook underlying system flaws that would have been obvious if the code were written manually.
• Erosion of Skill: Hand-rolling code is increasingly seen as a necessary practice for maintaining syntax proficiency and technical interview readiness.

"having derived the code and built a mental model is something like 90% of the work, the code artefact being 10%... once I have this model / vision in my mind, I’m streaming it from my mind into reality via code and having code suggestions pop up breaks that flow state for me."

AI-Driven Technical Debt and Feature Bloat

In startup environments, the ease of generating code with AI can lead to "feature bloat" and a lack of architectural discipline. The ability to iterate rapidly during early development cycles can create a codebase that is too complex to manage or too unstable for production.

Some organizations are considering rewriting core functionality without AI to ensure the system remains simple, understandable, and slower to change. This approach treats AI as a tool for rapid prototyping but views it as a liability for core architectural stability.

Strategic and Economic Constraints on AI Usage

Certain industries and company sizes dictate a more conservative approach to AI adoption:

• Deep Tech and High-Stakes Environments: In specialized fields, full understanding of every line of code is a prerequisite for security and reliability. In some cases, clients may view the presence of AI-generated code as a deal-breaker.
• Corporate Retraction: Some reports suggest that large entities, such as Ford, IBM, and the Commonwealth Bank of Australia, have scaled back AI-related hiring or initiatives, though the motivations range from economic restructuring to quality control.
• The "Vibe Coding" Crash: There are anecdotal reports of divisions (such as within AWS's AI division) that adopted a "vibe coding" approach—prioritizing rapid, AI-driven iteration over rigorous engineering—only to face dismantling or failure when the resulting software lacked stability.

The Counter-Argument: AI as an Evolutionary Tool

Conversely, many engineers argue that banning AI is equivalent to banning IDEs, compilers, or Stack Overflow. They posit that AI is simply the next step in shifting complexity away from the developer, similar to how garbage collection replaced manual memory management.

From this perspective, the productivity gains are undeniable, and the current friction is a result of a lack of mature patterns and design principles. Proponents argue that the best use cases for AI are not in core logic but on the periphery: security checks, performance audits, and test generation.

Summary of AI Usage Patterns

Approach	Primary Motivation	Typical Use Case
Full Adoption	Maximum velocity and token-maxing KPIs	Rapid prototyping, commoditized UI, boilerplate
Selective Use	Balancing speed with deep understanding	Review, test generation, periphery checks
Manual Core	Architectural stability and client trust	Core technology, deep-tech systems, complex spatial reasoning
Complete Avoidance	Risk mitigation and skill preservation	High-security systems, interview preparation