I'm obsessed with optimization. If there is a tool that shaves minutes off a deployment pipeline or automates a tedious migration, we are using it. Naturally, AI tools like ChatGPT and GitHub Copilot have become permanent fixtures in our IDEs. They feel like superpowers, generating boilerplate, debugging stack traces, and writing regex in seconds.
But recently, we’ve started to notice a disturbing trend. While we are definitely moving faster, we’ve started questioning if we are actually becoming more productive.
A recent deep-dive into the cognitive science of LLMs confirmed a suspicion I’ve had for months: AI might be making our development process too easy for our own good.
Here is why the "path of least resistance" might be silently degrading your codebase, and what the data says about the trade-off between speed and quality.
The "Cognitive Load" Trap
I stumbled upon a study titled "Cognitive ease at a cost" that perfectly explains the developer experience in 2025.
The researchers ran an experiment pitting traditional Web Search (Google) against LLMs (ChatGPT) to see how humans handle complex problem-solving. The results were validating for anyone who has ever felt "AI fatigue":
- The AI users felt great: They experienced significantly lower "Cognitive Load". The task felt easier, less stressful, and less mentally taxing.
- The Friction Disappeared: They didn't have to sift through documentation or filter out bad StackOverflow threads (what researchers call "Extraneous Load").
In a sprint planning meeting, this sounds like a win. Less mental strain means we can burn down more story points, right?
Why "Friction" is Actually Essential
Here is where the study gets scary for software engineers. While the AI group felt less stressed, their actual output was objectively worse.
The researchers found that despite the task feeling easier, the AI users demonstrated "lower-quality reasoning and argumentation" compared to the group that had to struggle through Google Search.
Why? Because they skipped the "Germane Load".
In plain English: Learning requires struggle.
When you hunt through three different documentation pages to understand how a specific API works, your brain is actively constructing a "schema", a mental map of how that system works. This is "Germane Cognitive Load," and it’s essential for deep learning.
The study showed that because the LLM handed over the answer on a silver platter, the users didn't engage in the deep processing necessary to actually understand the topic. They got the what, but they missed the why.
The Codebase Consequence
So, what does this mean for me as a developer?
It means that the "efficiency" I feel when using AI might be an illusion. If I copy-paste a solution for a complex architectural problem without the "friction" of understanding the documentation, I'm introducing technical debt. I'm solving the ticket, but I'm not learning the system.
The study proves that while LLMs reduce the mental effort of gathering information, they compromise the depth of inquiry. As developers, that depth is exactly what separates a junior engineer from a senior architect.
Final Thoughts: How I'm Changing My Workflow
I'm not deleting my OpenAI API keys. The speed benefits are too real to ignore. However, as developers, we should change the way we use these tools to ensure we don't fall into the "Cognitive Ease" trap.
Here are the key takeaways from this:
- AI for Syntax, Not Architecture: We use AI to handle the "Extraneous Load", the boring stuff like regex, boilerplate, and syntax memory. That is valid optimization.
- Voluntary Friction: When solving a core logic problem, we don't accept the first AI answer. We force ourselves to verify the solution against official documentation. We voluntarily add the friction back in to ensure we are increasing our "Germane Load".
- The "Explain Why" Rule: We don't just ask for the code. We ask the AI to explain why this approach is better than the alternative. This forces us to engage with the reasoning, not just the result.
The Verdict
AI tools are incredible for removing the "busy work" of coding. But when it comes to the deep thinking required to build great software, the data is clear: Easy doesn't mean good.
The next time Copilot suggests a 50-line function that solves your problem instantly, take a second to ask yourself: Do I actually understand this, or did I just skip the part where I learn?