Engineers, AI, and the Cognitive Tradeoff
Every generation creates tools to offload effort. In the 1960s, when electronic calculators entered classrooms, educators worried students would lose the ability to do math on their own. Why? Because the brain needs the reps. Understanding can’t be outsourced.
But this wasn’t a new fear. Centuries earlier, even the abacus sparked concern. When we hand off the mechanics of thinking to a tool, what happens to the mind behind it?
The pattern is familiar. The abacus. The calculator. The spell checker. Each added efficiency and sparked worry.
Today, engineers face a new version of the same dilemma, only now the tool doesn’t just assist, it completes your thought. Helpful? Absolutely. But the less we engage our brains, the more we risk losing the very skills we’re trying to enhance.
Same Pattern, New Player
AI is everywhere in modern engineering. From GitHub Copilot to LLM assistants in your IDE, it’s never been easier to write code fast. These tools reduce friction, prevent common mistakes, and suggest solutions in milliseconds, accelerating workflows like never before.
But as we integrate AI deeper into our workflows, developers are asking a more reflective question: What are we gaining and what are we giving up?
The Rise of the AI-Accelerated Engineer
No one is arguing that AI doesn’t improve speed. Tools like Copilot, Cursor, and ChatGPT can:
- Autocomplete repetitive code
- Suggest full function implementations
- Help with unfamiliar frameworks or libraries
- Generate tests, documentation, and refactorings
- Speed up prototyping and reduce friction in problem-solving
For many engineers, especially early in their careers, this can be transformative. AI helps close skill gaps, reduces time spent searching Stack Overflow, and allows devs to focus more on solving higher-level problems… at least in theory.
It also enables small teams to do more. Startups and consultancies can prototype faster. Mid-sized teams can reduce time-to-deploy. In an environment where deadlines are tight and expectations are high, AI feels like a secret weapon.
What’s the Tradeoff?
Despite these advantages, many engineers, especially experienced ones, have started noticing a troubling side effect: reliance. A growing number of developers, and now even cognitive science researchers, are raising a critical question:
“What happens to our brains when we stop thinking and start depending?”
The Slow Atrophy of Mental Muscles
A recent study helps explain the science behind that creeping feeling of overreliance. Titled “Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing,” it found that participants who used LLMs consistently underperformed at neural, linguistic, and behavioral levels compared to those who worked without assistance. Using EEG scans, the researchers saw that AI-assisted users showed less brain activity, weaker memory recall, and lower ownership over their work.
In a fourth session where the AI tools were taken away, participants who had been relying on them couldn’t just snap back into focus, their brains were under-engaged, even when the task required more.
While the study was focused on writing, its implications for engineering are clear.
The takeaway? Relying on LLMs can reduce cognitive engagement over time, leading to what the researchers call “cognitive debt.” Like financial debt, this kind of dependency can be manageable in small amounts, but problematic if it compounds unchecked.
The Dev Perspective: More Than a Hunch
These concerns aren’t just academic, they’re emerging in real developer conversations.
In an internal chat, between two Ardan Labs engineers, one shared:
“I’ve noticed this with other devs just from using code completion. I don’t use any code completion. Insane, right? Not really, when it comes to knowing the SDKs like the back of my hand.”
Another added:
“I second you on that, knowing the stuff is hard to beat.”
“I watch other devs pause their typing and rally their brains while they’re waiting for their editor to give them the answer… I find that autocompletion, to almost any degree, distracts me more than helps me.”
The concern here isn’t about whether AI tools are useful, they clearly are. The question is whether they’re substituting for learning and recall, rather than supporting them.
When autocomplete fills in the name of a function you forgot, are you just moving faster or are you giving up the opportunity to learn it?
When an LLM writes a chunk of code for you, are you saving time or skipping the exercise of problem-solving that makes you better?
The Psychological Shift
Relying on AI changes how engineers think about the problem. When a tool is constantly suggesting solutions, there’s less need to remember syntax, less incentive to explore docs, and fewer opportunities to wrestle with ambiguity, the kind of challenge that builds skill.
Over time, this reliance creates a kind of cognitive debt, an accumulation of missed chances to strengthen mental pathways. Like taking shortcuts in math and never fully learning algebra, you get the right answers until the questions get difficult.
The Tortoise & the Hare
It’s tempting to equate productivity with speed: the faster we write code, the more effective we are. But that’s a narrow view of software engineering. Much of what we value in senior engineers (judgment, pattern recognition, architectural intuition) comes from years of focused effort and deep practice.
If that effort is increasingly offloaded to AI tools, what happens to the skill curve?
What happens when engineers who have grown up with autocomplete try to debug complex issues, architect large systems, or write performant low-level code?
More importantly: What happens when the tool is wrong, and the brain isn’t equipped to notice?
Mindful Engineering in an AI World
This isn’t an argument against AI assistance. At Ardan Labs, we ourselves use and appreciate the power of modern developer tools, including LLMs and AI pair programming. But we also recognize that tools are only as powerful as the minds using them.
As Bill Kennedy put it on the Ardan Labs Podcast (Ep.131): “Understand that you’re the intelligence behind the tooling, it’s not the other way around.”
If we want to remain thoughtful, capable engineers, we must:
- Treat AI as a support system, not a substitute for understanding.
- Invest in mastering the fundamentals of our craft.
- Create time for deep work, problem-solving without a safety net.
- Reflect on where we’re growing and where we’re just going faster.
Final Thoughts
As engineers, our job isn’t just to write code. It’s to understand problems, design systems, and build things that last. If AI helps us do that better, we should embrace it. But if it tempts us to stop thinking, stop learning, and stop caring, we should slow down and reflect.
Cognitive debt is real, and while it may not show up in a compiler or a code review, its effects are long-term. Whether you’re writing documentation, planning out tasks, or designing scalable systems, the brain you train today is the one you’ll need tomorrow.
The next time your IDE pauses to suggest a snippet, take a second to ask yourself:
Do I already know this? Could I figure it out? Should I try?
Mastery isn’t just about speed. It’s about awareness, discipline, and the choice to learn when it’s easier not to.
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