Writing Code is Cheap Now: How AI is Transforming Software Development

AI coding agents have fundamentally changed software development economics. The act of writing code—once expensive and time-consuming—now costs almost nothing. This shift forces us to rethink everything about how we build software.

The Economics Have Flipped

Code generation used to be the bottleneck. A few hundred lines of clean, tested code took most developers a full day or more. Our entire industry organized around this constraint—extensive planning, careful estimation, and feature prioritization based on development costs.

Now, AI agents can generate thousands of lines of working code in minutes. The expensive part isn’t the typing anymore.

Good Code Still Has a Cost

While generating code is nearly free, delivering good code remains expensive. Here’s what “good code” means:

• Simple and minimal - does only what’s needed, understandable by humans and machines
• Well-tested - comprehensive test coverage that actually validates behavior
• Secure - follows security best practices and handles edge cases
• Performant - meets speed and resource requirements
• Maintainable - the design affords future changes without major rewrites
• Documented - clear explanations of why decisions were made

AI can help with all of these, but achieving them requires active effort and expertise. The difference is that this effort now focuses on guidance and review rather than typing.

New Engineering Practices Emerge

This transformation demands new habits:

Embrace throwaway prototypes. When code generation is cheap, you can build three different approaches in a day and pick the best one. Previously, you’d agonize over architecture because rewriting was expensive.

Shift from writing to reviewing. Your time moves from thinking + typing to thinking + reviewing. The thinking part—domain knowledge, edge cases, integration constraints—hasn’t gotten cheaper.

Question old instincts. When your gut says “don’t build that, it’s not worth the time,” try prompting an agent anyway. Your intuitions about what’s worth building may be outdated.

Focus on specifications. Clear requirements become more valuable than ever. Fuzzy specs lead to confident but wrong implementations.

The Real Bottlenecks

With code generation solved, new constraints emerge:

• Understanding what to build - requirements gathering and domain modeling
• System design - architecture decisions that enable future changes
• Quality assurance - ensuring generated code meets standards
• Integration complexity - making components work together reliably

These were always important, but now they’re the primary cost centers.

Implications for Developers

Junior developers who primarily transcribed requirements into code face the biggest disruption. The valuable skills now are:

• Domain expertise - deep understanding of business problems
• System thinking - designing for maintainability and change
• Quality judgment - recognizing good code from generated options
• Communication - translating business needs into clear specifications

The Path Forward

We’re still learning how to work effectively with AI coding agents. The technology is advancing faster than our practices. The challenge isn’t technical—it’s developing new personal and organizational habits that respond to these new capabilities.

The future belongs to developers who can direct AI agents toward valuable outcomes, not those who can type code fastest. Writing code is cheap now. Knowing what code to write, and ensuring it’s good code, remains as valuable as ever.