The Jevons Paradox in Software Engineering: How AI Coding Assistants Reshape Productivity
Imagine this: You've just installed the latest AI coding assistant. The marketing promised to cut your coding time in half. Six months later, you're writing more code than ever before, tackling increasingly complex problems, and somehow still working the same hours. What happened?
Welcome to Jevons Paradox in the age of AI-assisted software development.
The Curious Case of Efficiency That Doesn't Save Time
In 1865, a British economist named William Stanley Jevons noticed something counterintuitive about coal consumption. When more efficient steam engines were introduced, logic suggested coal use would decrease. Instead, it skyrocketed. The more efficiently coal could be used, the more applications people found for it.
Fast forward to 2025: Your AI coding assistant is the modern-day steam engine, and your time and mental energy are the coal.
My Journey With AI Coding Assistants: A Personal Story
When I first integrated an AI coding assistant into my workflow last year, I had visions of shorter workdays and more time for strategic thinking. The reality? I found myself saying "now I can finally tackle that refactoring project I've been putting off" and "let's add those extra test cases we've been skipping."
Sound familiar?
The Numbers Don't Lie: The Productivity Paradox in Action
Recent industry surveys reveal a fascinating pattern:
| Metric | Without AI Assistant | With AI Assistant | Change |
|---|---|---|---|
| Lines of code written/week | 1,200 | 1,560 | +30% |
| Tickets closed/sprint | 8 | 10 | +25% |
| Languages/frameworks used regularly | 2-3 | 4-5 | +67% |
Below Metrics are subjective. I loved writing more code | |||
| Average hours worked/week | 40 to 50 | 70+ | 50%+ |
Why We Keep Consuming Our Efficiency Gains
The Expanding Possibility Frontier
As our tools improve, our concept of what's possible expands with them. It's human nature. When we suddenly have the capacity to do more, we don't pocket the difference—we expand our ambitions.
This cycle isn't unique to software development, but our field experiences it more intensely than most because of how quickly our tools evolve.
The Four Types of Productivity Consumers
In my observation, there are four ways engineers typically "spend" their AI-driven productivity gains:
- The Depth Diver – Uses efficiency to create more robust solutions with better error handling, edge case management, and performance optimization
- The Breadth Explorer – Leverages AI to work across more languages, frameworks, and systems than previously possible
- The Quality Enhancer – Invests saved time in better documentation, more comprehensive tests, and cleaner code
- The Volume Maximizer – Simply produces more features, closes more tickets, and ships more code
Which one are you? Most of us are a blend, shifting between these archetypes depending on project requirements and personal interests.
The Great Capability Expansion
What makes AI coding assistants particularly powerful is how they expand what individual developers can accomplish:
This expansion means junior developers can contribute to complex systems earlier in their careers, while senior developers can focus more on architecture and innovation.
Reimagining Productivity in the AI Era
From "Doing Things Faster" to "Doing Better Things"
The most successful teams I've observed aren't just using AI to speed up existing processes—they're rethinking what processes should exist in the first place.
Consider this reimagined development workflow:
The key shift: humans focus on the parts of the process where creativity, judgment, and contextual understanding matter most.
How to Thrive in the Age of AI-Assisted Development
1. Embrace Strategic Inefficiency
Not everything should be optimized for speed. Sometimes, diving deep into a problem without AI assistance builds fundamental understanding that pays dividends later.
2. Set Clear Boundaries
Establish team norms around when and how to use AI assistants. Some projects benefit from exploration and creative generation; others need careful, methodical human reasoning.
3. Measure What Matters
If you're still measuring productivity by lines of code or tickets closed, you're missing the true impact of AI assistance. Consider metrics like:
- Time to validated solution (not just working code)
- Reduction in production incidents
- User-reported satisfaction with features
- Knowledge dissemination across the team
4. Continuously Reskill
The skills that make developers valuable are evolving rapidly. The future belongs to those who can:
- Clearly articulate problems for AI to solve
- Evaluate and refine AI-generated solutions
- Understand and communicate system-level concerns
- Apply deep domain knowledge to technical decisions
Looking Ahead: The Co-Evolution of Engineers and Their Tools
As our relationship with AI coding assistants deepens, we're not just changing our tools—our tools are changing us. The software engineers of 2030 will approach problems differently than those of 2020, just as today's engineers think differently than those of the pre-internet era.
The most exciting part of this journey isn't just what we'll build—it's who we'll become as builders.
What's your experience with AI coding assistants? Are you saving time, doing more, or both? Share your thoughts in the comments below!
