The transformation of software development through AI presents an intriguing application of Jevons Paradox: as development becomes more efficient, it is likely that we won’t see a reduction in demand for developers, but rather an increase. What was a previously (economically) unfeasible project, now becomes viable, and so organizations can explore innovative solutions that were once too resource-intensive to pursue.
“Cheaper software means people are going to want more of it.”
While AI excels at rapid prototyping and routine coding tasks, it currently encounters significant limitations in system completion and complex architecture decisions. Dustin Ewers calls this the “70% problem”: AI gets you most of the way, but someone must handle the rest — testing, deployment, maintenance, and fixing compounding errors. This suggests a transformation of the developer’s role rather than its obsolescence — technological augmentation rather than replacement.
The core value of software development lies not in code production, but in the deep understanding of business processes, system design, and problem-solving — domains where human judgment remains paramount.
Even in scenarios where AI capabilities expand dramatically, the opportunity cost dynamics suggest continued demand for human developers focusing on high-value activities while AI handles routine tasks. This is comparative advantage at work: even if AI outperforms humans broadly, computational scarcity means AI resources will be allocated to highest-value tasks, leaving meaningful work for humans.
“The AI revolution is similar to the introduction of compilers.”
As we navigate this transition, the question isn’t whether developers will remain relevant, but rather how the profession will evolve to leverage AI’s capabilities while developing new areas of expertise. Ewers puts it plainly: “the best days of our industry lie ahead.”
Quotes from Dustin Ewers — Ignore the Grifters, which is well worth reading in full.