This originally began as one lengthy post but I’ve broken it into multiple posts that are more digestible. This is part 4.
By “skill atrophy” I mean “skill rot”, “becoming rusty”, “dulling your blade” or any of the other colorful metaphors people use. It’s analogous to a runner getting out of practice and losing speed, or a litigator who hasn’t seen the courtroom in a long time and doesn’t have quite the same edge.
Cell Phone Contacts
A more banal comparison that those of us of a certain age might know:
Those of us that grew up without cellphones probably used to have many phone numbers memorized. Since cellphones (“dumb” phones, included!) have contact lists that reference numbers by name instead of digits, our brains have no reason to memorize new phone numbers.
I still remember most phone numbers from my childhood, but only have a handful of phone numbers memorized now (close family members).
In development, it might look like forgetting syntax more frequently, or finding it harder to refactor, or even as severe as getting executive dysfunction when confronting a programming problem.
Self-confidence and critial thinking in the age of “AI”
In this 2025 Carnegie Mellon study The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers
Specifically, higher confidence in GenAI is associated with less critical thinking, while higher self-confidence is associated with more critical thinking. Qualitatively, GenAI shifts the nature of critical thinking toward information verification, response integration, and task stewardship.
The study is careful to acknowledge:
Our analysis does not establish causation. However, based on our evidence, it is possible that fostering workers’ domain expertise and associated self-confidence may result in improved critical thinking when using GenAI. Task confidence significantly influences how users engage with AI tools, particularly in the context of human-AI “collaboration” (not withstanding objections to that term).
LLM-driven development is still a very novel concept, so we really don’t have any longitudinal studies objectively showing skill atrophy.
Opportunities for growth
Personally, I enjoy programming. The depth of knowledge I have about my particular slice of the coding world is something that is consistently useful at my job, especially with bug hunting and resolution. I would be disappointed to lose that edge or become reliant on a third party service to be able to perform my job.
I have yet to be convinced that using an LLM to generate a solution will save me a significant amount of time, when you consider the “total cost of ownership” for that solution – factoring in review time, corrections, and implementation time. It feels more jarring and stunted than approaching it more conventionally.
Given that, I see every opportunity to program as an opportunity for me to hone my skills a bit more, to become a bit sharper, and to learn new things.