AI tools are commoditizing the act of writing code (software development). The durable skill and key differentiator is now software engineering: architecting systems, creating great user experiences, and applying taste. Building something people want to use is the new challenge.

The cost of generating code with AI is trivial, shifting the primary expense to its maintenance, validation, and deployment. This inverts the traditional software engineering model where human code production was the main bottleneck, making code's complexity a liability.

With AI making code generation cheap, the limiting factors for development velocity are now defining what to build (product) and ensuring its quality (review). Engineers will increasingly focus on high-level systems architecture rather than typing code.

AI coding tools let solo developers 'vibe code' impressive prototypes quickly, creating a false belief that they are production-ready. These projects often lack the robust architecture needed to scale, requiring expensive rewrites by 'God level' developers to fix the resulting spaghetti code.

As AI automates code generation, the ability to write code quickly becomes less of a differentiator for engineers. Instead, a deep understanding of computer science fundamentals and system design is now more critical, as these are architectural skills AI cannot yet replicate.

Unlike normal technical debt, 'agentic technical debt' compounds rapidly. Without persistent, written architectural constraints, AI coding tools re-derive foundational decisions in each session, causing the codebase to drift incoherently. The solution is to document architectural principles before building to give the AI context and prevent entropy.

With AI making code generation nearly free, the new competitive moat isn't speed of execution but the quality of judgment, architecture, and intent behind the product. Mindless, 10-minute builds are easily replicated and lack defensibility, leading to what Aishwarya Srinivasan calls "AI slop."

AI coding tools dramatically accelerate development, but this speed amplifies technical debt creation exponentially. A small team can now generate a massive, fragile codebase with inconsistent patterns and sparse documentation, creating maintenance burdens previously seen only in large, legacy organizations.

As AI makes the act of writing code a commodity, the primary challenge is no longer execution but discovery. The most valuable work becomes prototyping and exploring to determine *what* should be built, increasing the strategic importance of the design function.