AI's impact on coding is unfolding in stages. Phase 1 was autocomplete (Copilot). We're now in Phase 2, defined by interactive agents where developers orchestrate tasks with prompts. Phase 3 will be true automation, where agents independently handle complete, albeit simpler, development workflows without direct human guidance.

AI coding has advanced so rapidly that tools like Claude Code are now responsible for their own development. This signals a fundamental shift in the software engineering profession, requiring programmers to master a new, higher level of abstraction to remain effective.

AI acts as a massive force multiplier for software development. By using AI agents for coding and code review, with humans providing high-level direction and final approval, a two-person team can achieve the output of a much larger engineering organization.

Designers have historically been limited by their reliance on engineers. AI-powered coding tools eliminate this bottleneck, enabling designers with strong taste to "vibe code" and build functional applications themselves. This creates a new, highly effective archetype of a design-led builder.

Traditionally, building software required deep knowledge of many complex layers and team handoffs. AI agents change this paradigm. A creator can now provide a vague idea and receive a 60-70% complete, working artifact, dramatically shortening the iteration cycle from months to minutes and bypassing initial complexities.

The role of a senior developer is evolving. They now focus on defining outcomes by writing tests that a piece of code must accomplish. The AI then generates the actual implementation, allowing small teams to build complex systems in a fraction of the traditional time.

Experienced programmers are urged to stop dismissing AI coding tools. The experience is described as "revolutionary," and even a one-hour trial on a toy project will reveal that it's the clear next evolution of programming, not a gimmick.

AI-assisted development, or "vibe coding," is re-engaging executives who coded earlier in their careers. It removes the time-consuming friction of going from idea to MVP, allowing them to quickly build personal tools and reconnect with the craft of software creation, even with demanding schedules.

The focus on AI writing code is narrow, as coding represents only 10-20% of the total software development effort. The most significant productivity gains will come from AI automating other critical, time-consuming stages like testing, security, and deployment, fundamentally reshaping the entire lifecycle.