Beyond Technical Debt: How AI-Assisted Development Creates Comprehension Debt in Resource-Constrained Indie Teams

TL;DR

This paper introduces the CIGDI framework for indie game development, highlighting how AI tools can both aid and create comprehension debt, which impacts team fragility and dependency.

Contribution

It presents a novel seven-stage AI-assisted development framework tailored for resource-constrained indie teams and identifies the concept of comprehension debt as a new form of technical debt.

Findings

AI democratized knowledge access and reduced cognitive load.

Comprehension debt leads to system fragility and AI dependency.

Framework supports iterative, human-in-the-loop decision making.

Abstract

Junior indie game developers in distributed, part-time teams lack production frameworks suited to their specific context, as traditional methodologies are often inaccessible. This study introduces the CIGDI (Co-Intelligence Game Development Ideation) Framework, an alternative approach for integrating AI tools to address persistent challenges of technical debt, coordination, and burnout. The framework emerged from a three-month reflective practice and autoethnographic study of a three-person distributed team developing the 2D narrative game "The Worm's Memoirs". Based on analysis of development data (N=157 Jira tasks, N=333 GitHub commits, N=13+ Miro boards, N=8 reflection sessions), CIGDI is proposed as a seven-stage iterative process structured around human-in-the-loop decision points (Priority Criteria and Timeboxing). While AI support democratized knowledge access and reduced cognitive load, our analysis identified a significant challenge: "comprehension debt." We define this as a novel form of technical debt where AI helps teams build systems more sophisticated than their independent skill level can create or maintain. This paradox (possessing functional systems the team incompletely understands) creates fragility and AI dependency, distinct from traditional code quality debt. This work contributes a practical production framework for resource-constrained teams and identifies critical questions about whether AI assistance constitutes a learning ladder or a dependency trap for developer skill.