The Illusion of Progress
This is Part 2 in the series on AI Isn't Replacing Us, We're Opting Out
In the rapidly evolving landscape of software development, AI coding tools have emerged as the new gold standard for productivity. These tools promise to transform how we work—generating code at unprecedented speeds, completing our thoughts before we finish typing them, and eliminating the tedious aspects of programming that few developers truly enjoy.
"10x your productivity."
"Code faster with AI."
"Never write boilerplate again."
These marketing promises are seductive, and they're not entirely untrue. They appeal to our desire for efficiency and our dislike of repetitive tasks. When I watch code materialize almost magically after typing a brief comment, it's hard not to feel the satisfaction. Tasks that once took an hour now take minutes. Complex implementations that would have required diving into documentation can be summoned with a simple prompt.
But beneath the veneer of this efficiency lies a more complex reality. What if this apparent leap forward is actually leading us backward in ways that we haven't fully recognized? What looks like advancement may actually be a regression in disguise. The ability to rapidly generate code doesn't necessarily make us better software engineers—it may be making us more dependent and less capable.
The Speed Mirage
The most immediate benefit of AI coding tools is speed. They generate code faster than humans can type it, offering instant solutions to problems that might otherwise require research and longer implementation times. This acceleration feels like progress because we equate faster output with improved productivity.
However, speed of code generation is a deceptively shallow metric of engineering progress. While AI can rapidly produce syntactically correct code that appears to solve the problem at hand, this speed bypasses the deliberative thinking process that has been central to software engineering.
Consider what happens during the process of engineering that AI now frequently eliminates:
- We mentally model the problem space
- We consider alternative approaches and their tradeoffs
- We build intuition about the edge cases and potential failures
- We develop a deeper understanding that will serve us when we need to debug or maintain the code later
When AI shortcuts this process, we may get working code faster, but miss the gains that build lasting expertise. It's an old saying but "sometimes you need to go slow to go fast".
The Satisfaction Trap
Perhaps the most subtle danger lies in how good using an AI tool feels. There is an undeniable rush of satisfaction when you describe what you want and watch code appear seconds later. When problems occur, simply craft another prompt instructing the AI to fix the problem. The friction of development seems to melt away, and this feeling reinforces continued and expanding reliance on these tools.
This positive feedback loop creates a powerful illusion of professional advancement. We feel more productive, more efficient, and even more skilled when leveraging these tools. But this satisfaction may be masking a gradual erosion of our capabilities and independence.
The Competence Paradox
The ultimate illusion in all of this is that AI tools are making us better engineers. The reality is they may be creating what I would call a "competence paradox"—the tools that make us feel more capable in the short term may be making us less capable in the long run.
What's often overlooked is how expertise in software engineering actually develops. The journey from novice to expert programmer has traditionally involved common patterns of skill acquisition:
Struggle and discovery: Wrestling with problems just beyond our current abilities.
Pattern recognition: Gradually identifying common structures across different problem domains.
Knowledge integration: Building a rich mental model of how systems fit together.
Intuition mastery: Developing the ability to see solutions almost instinctively.
This developmental path isn't unique to programming—it mirrors how humans master complex domains across disciplines, from chess to medicine to music. And critically, it depends on a process that AI tools are specifically designed to short-circuit: productive struggle.
When we delegate the implementation details to AI—even while maintaining high-level direction—we're systematically removing the very experiences that have traditionally built expertise. The hours spent debugging mysterious errors, the false starts that teach us which approaches don't work and why, the gradual building of intuition through hands-on practice—these formative experiences are increasingly being bypassed. The hardest part of this shift is that it might not be noticeable day-to-day, but it's effects compound over time as our unused skills begin to atrophy.
Finding Balance
None of this is to suggest we should abandon AI coding tools entirely. They represent a significant innovation with genuine benefits when used thoughtfully. Consider the parallel to mathematics education. When calculators became ubiquitous, they revolutionized what was possible in applied mathematics and engineering. Yet educators recognized a crucial distinction: students who first mastered fundamental concepts and then used calculators as accelerators developed deeper mathematical intuition than those who relied on calculators from the outset.
The calculator itself was never the problem—it's a powerful tool when used to extend capabilities rather than replace them. So too with AI coding tools. When they amplify the skills we've deliberately cultivated, they can elevate our work. When they substitute for skills we've never developed, they create a deceptive form of progress that may ultimately limit our professional growth.
By thoughtfully navigating this balance, we can leverage the genuine efficiencies of AI tools while ensuring they enhance rather than replace the deep cognitive work that builds true expertise. The goal isn't to resist innovation, but to integrate it in ways that amplify our capabilities instead of diminishing them over time.
Dispelling the Illusion
The illusion that software development progress can be measured by the speed of code creation fundamentally misunderstands the craft. True advancement in our field has never been about velocity of output or lines committed per hour—these shallow metrics fail to capture what actually drives transformative software solutions. Progress manifests through a developer's deepening understanding of systems, increasingly refined judgment about architectural tradeoffs, and strengthened ability to reason about complex problems from first principles. These capabilities represent the difference between code that merely functions and code that elegantly solves problems while anticipating future needs.
This isn't merely philosophical—it's pragmatic. When we evaluate senior engineers today, we don't celebrate those who simply produce code the fastest; we value those who demonstrate technical wisdom, who can articulate system boundaries with clarity, who make sound trade-offs balancing immediate needs against long-term maintainability, and who can distill complex domain challenges into coherent abstractions—qualities developed primarily through active engagement with challenging problems and thoughtful reflection on our experiences.
In Part 3 of this series, we'll trace the evolution of developer dependencies—from documentation lookups to StackOverflow copy-pasting to today's AI-generated solutions—examining how this progression has steadily shifted cognitive work away from developers. We'll explore how these seemingly practical adaptations have accelerated into a troubling pattern that affects developers across all experience levels and threatens to fundamentally transform the skills that define our profession.