01. The Price of Cheap Code: Exponential Growth of Technical Debt
In the AI era, the marginal cost of code generation is approaching zero. You can instantly obtain complex algorithmic optimizations, reducing complexity from to . However, hidden behind this efficiency is a massive trap: the rate of technical debt accumulation has accelerated to an alarming degree.
When writing code becomes effortless, developers tend to accept a series of "good enough" local solutions. This "local perfection," born from a lack of global perspective, is leading to "global chaos". This is not merely an efficiency issue; it is a process of increasing entropy. Without rigorous design, every line of AI-generated code increases the system's complexity and maintenance costs.
02. The Essence of Software Engineering: Complexity Management and Hierarchical Evolution
The core value of software engineering lies in managing four dimensions of complexity: Business, Technical, Organizational, and Evolutionary.
Business Complexity: Understanding real-world rules and modeling various boundary conditions.
Technical Complexity: Making decisions among numerous options to balance performance, maintainability, and scalability.
Organizational Complexity: Coordinating collaboration across multiple teams to ensure system consistency.
Evolutionary Complexity: Ensuring the system's ability to evolve continuously as requirements change.
This complexity management dictates that we cannot simplify software development to mere "typing"; it must be viewed as an information transformation process from the abstract to the concrete. To address challenges across these dimensions, we must establish a rigorous logical chain across the five core layers of software development: the Problem Domain, Solution, Implementation, Testing & Validation, and Deployment & Operations layers. Currently, AI's strengths are concentrated only at the bottom Implementation layer. Without high-level strategic guidance, high-speed output at the bottom will only accelerate system collapse.
03. Defining "Design First": The "Success Boundary" within AI's Capability Limits
Amidst the AI wave, I have defined a "Design First" paradigm in my new book, Intelligent Software Development through the Full Lifecycle Driven by DeepSeek: Within the limits of AI's capabilities, human developers define a "success boundary" for code generation quality through Architectural Intent and Domain Modeling. Its core philosophy is to achieve a hierarchical mapping from the problem domain to the solution.
To understand the unique value of this paradigm, we can compare it against currently popular development styles:
- Design First vs. Vibe Coding:
Vibe Coding relies on vague intuition and constant trial and error. It essentially ignores "hierarchical mapping" by skipping the abstraction process from problem domain to solution, directly colliding with the implementation layer through natural language. This results in the system becoming an unpredictable "black box". In contrast, Design First requires humans to complete low-entropy logical deductions before AI intervention, ensuring the generation process stays on a controlled track.
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Design First vs. SDD (Spec-Driven Development):
While SDD (such as Amazon’s Kiro) emphasizes standardized specification documents (e.g.,
requirements.md,design.md) as AI input, Design First elevates this into an engineering philosophy. It focuses not only on the "specification" as an artifact but also on utilizing Domain-Driven Design (DDD) and other methodologies to eliminate ambiguity in business concepts, establishing a Ubiquitous Language to provide a high-quality collaborative framework for AI.
04. Practical Blueprint: A "Defensive Design" System for the AI Era
Based on the practical framework in Intelligent Software Development through the Full Lifecycle Driven by DeepSeek, a complete "Defensive Design" system should cover the entire lifecycle, from architectural selection to security defense:
Architectural Design and Visualization (Solution Layer):
In the ITAM (IT Asset Management System) case study, the book proposes prioritizing Hexagonal Architecture. By designing Driving Adapters and Persistence Adapters, the core application logic is isolated from external technical details. This highly decoupled design provides AI with clear, independent generation targets.Granular Domain Modeling (Detailed Design Layer):
Utilize DDD domain models to standardize entity relationships. Introduce Immutability principles to optimize asset classes and incorporate Design Patterns (e.g., the Strategy pattern for depreciation strategies, and Factory or Builder patterns for object creation). These patterns are not just code organization methods; they are high-level constraint instructions passed from humans to AI.Multi-layer Quality Assurance and Security Review:
One cannot hope that AI will spontaneously generate perfect code. A multi-layer validation system including JUnit unit tests, BDD (Behavior-Driven Development) tests, and end-to-end integration tests must be established. Simultaneously, utilize AI-assisted STRIDE threat modeling and security reviews to identify and close security vulnerabilities before code is deployed to Kubernetes or built through GitHub Actions CI/CD pipelines.
In summary, through deep practical comparison of different paradigms, we find distinct value coordinates: In the quality dimension, Design-First demonstrates an incomparable advantage in medium-to-large projects, providing the most complete architectural documentation and the clearest design decision chains. Regarding maintainability, because logical boundaries are predefined, the cognitive load for newcomers is minimized, and long-term maintenance costs are reduced to the lowest level. From a development efficiency perspective, Vibe Coding has a speed advantage in the initial prototyping stage, but subsequent modification costs often grow exponentially. The initial investment for SDD and Design-First is similar, but the Total Cost of Ownership (TCO), including maintenance, is lowest with Design-First. Ultimately, the three have formed their own applicable territories: Vibe Coding for small-scale prototype exploration, SDD for medium-sized standardized projects, and Design-First as the essential moat for building large-scale complex systems and supporting long-term evolution.
Conclusion: The Rise of the Architect
As the marginal cost of code generation approaches zero, human strategic design capability becomes the only scarce resource in software development. AI ends the era of the "typist," while architectural logicists will regain control within the "Babel of code" by defining the "success boundary".
Checkbox for Readers: Before handing a task to AI, please confirm:
[ ] Hierarchical Mapping: Have I completed the logical deduction from "business pain point" to "technical solution," rather than jumping straight to code?
[ ] Success Boundary: Does the prompt I provided include clear architectural constraints (e.g., Hexagonal Architecture isolation)?
[ ] Domain Precision: Have I utilized DDD to establish clear domain boundaries and eliminate ambiguity in business concepts?
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