AI-Native Development Methodology

AI and Developers

The perspective of developers regarding AI is complex. While AI has facilitated unprecedented increases in productivity and information acquisition, concurrently, the fundamental role of the developer is undergoing transformation. Some prognosticate that AI, upon evolving into AGI, will supplant the majority of developers, whereas others disparage AI as an overhyped instrument, contending that the intrinsic value of developers remains unaltered. Ostensibly, these attitudes appear antithetical; however, they exhibit a striking resemblance in their tendency to evaluate AI based on existing paradigms of thought.

Nevertheless, the crucial aspect here is not to judge or evaluate AI. The true crux lies in how the development mindset must adapt given the premise of AI, and how the roles of developers and organizations must be reconfigured in response to this transformation. To address this, the AI development methodology has been systematized through the following three-stage analysis.

AI-Dependent Development

AI-dependent development represents a stage where AI is regarded as the primary arbiter of judgment, and its outputs are uncritically accepted. Developers at this stage perceive AI as an entity that organizes and implements their ideas, and furthermore, they accept it as a substitute capable of assuming judgment and responsibility.

Superficially, this approach appears to dramatically enhance productivity. However, in reality, it rapidly accumulates code that is inexplicable and for which no one can be held accountable. When issues arise with such code, no one can confidently determine why a particular structure was adopted or which choice was correct.

At this stage, AI only generates unreliable results, and developers are relegated to the role of consuming these outputs. Consequently, both AI and developers are utilized exhaustively, with no genuine capabilities accumulating anywhere.

AI-Utilizing Development

AI-utilizing development is a stage observed among more proficient developers. In this stage, developers perceive AI not as a substitute for judgment, but as a powerful tool for enhancing productivity. Developers at this stage retain their role as the primary agents of design and final judgment, while delegating repetitive and high-cost tasks such as code generation, refactoring, debugging, and document summarization to AI. This approach significantly reduces the proportion of repetitive tasks for developers and can dramatically boost productivity while maintaining reliability.

However, this method possesses a structural limitation in that the context of design and judgment do not circulate. Because the direction of development and the process of judgment remain solely within the developer's individual reasoning and are not shared with AI, AI fails to accumulate the developer's changes and growth. As a result, the context of design and the faculty of judgment become isolated within the developer.

Consequently, a learning cycle between the developer and AI does not form, and the ability to utilize AI does not expand beyond the developer's existing capabilities. Growth remains attributable to individuals, and over time, the competence gap between developers widens. Furthermore, such competencies exhibit the limitation of being difficult to accumulate or reproduce at an organizational level.

AI-Native Development

AI-native development transcends the stages of trusting AI or using it merely as a tool, signifying a structure where AI is incorporated into the development framework and judgment processes, and where AI and humans interact and mutually grow. At this stage, AI functions as an interactive component, sharing the developer's judgments and context.

The essence of this structure is that AI and developers engage in a bidirectional, rather than unidirectional, cycle. Developers continuously share their design intentions and the rationale behind their judgments with AI, and AI, in turn, accumulates the developer's thought processes and the organization's context. As a result, AI evolves beyond being a mere tool that processes requests, progressing towards presenting customized options for the organization and assisting and correcting the developer's judgments.

AI-native development does not depend on the proficiency or adroitness of specific individuals. Even if team members change, as long as the AI context is maintained, similar questions can be posed regarding the same problem, and better decisions can be made based on past choices and their rationales. Development capabilities are not attributed to individuals but become cumulative and reproducible across the entire organization.

Ultimately, what AI-native development aspires to is not a particular architecture or development methodology, but rather the development process itself, wherein the judgments and learning of AI and humans are continuously reinforced. Within this structure, developers and AI do not supersede each other. Instead, they share the same problem space, evolving into a relationship where they collectively create superior judgments and more robust structures.