AI Deskilling: We Warned You.
Smart machines, dumb users?
We have all heard of AI brain rot, AI psychosis, and AI slop. If you spend any time online, it’s quite obvious that the combination of social media and AI isn’t exactly healthy for your neurons. What isn’t talked about as often is the mental impact of using AI at work, despite it being potentially more damaging. Thankfully, this issue is now beginning to receive headline coverage. But most publications fall short of explaining why using AI at work can be so harmful and totally neglect to mention that we were warned about all of these issues from the start. Welcome to the world of AI deskilling.
Business Insider recently published one of these articles. The piece profiles Josh Anderson, a highly experienced software consultant, who shared his experience developing a new app, Road Trip Ninja. He conducted a little experiment and tried to get AI to write the entire codebase. Initially, things went great, but as the code ballooned past 100,000 lines, and interactions with the chatbot grew from minutes to hours, Anderson became increasingly frustrated when progress slowed to a halt.
Of course, this was just an experiment; Anderson could have stepped in at any time and coded the app himself, even if sorting out such a huge block of AI-generated code with very few in-code comments is insanely difficult. But Anderson’s experience highlighted a glaring problem. You see, even Anthropic has found that using generative AI coding tools dramatically reduces a coder’s skills in debugging and code comprehension. So, with the direction the software industry is heading, could a coder actually step in and finish what the AI couldn’t? This article explains that Anderson’s experience “raised questions about the real impact of AI on skill retention and development” and that it “highlights a broader concern among workplace researchers: the risk of deskilling in an environment increasingly reliant on AI.”
Given that this is an industry-wide issue, the article didn’t just look at Josh’s experiment. It also highlighted that developers admitted to finding tasks considerably more challenging during Claude’s recent outage, which rendered their AI assistant useless and indicated a “dangerous dependency.”
This issue has a variety of names and similar explanations that the article quickly glances over.
For example, Josh Nosta calls it the “AI rebound effect.” He describes it as when an AI-driven increase in productivity hides a decrease in skill levels. As he put it:
“When automation handles the details, situational awareness dulls. And in that context, we scan less, anticipate less, and make fewer micro-adjustments. Simply put, the mental models we rely on to navigate complex situations shrink because the system is doing what we once did ourselves. Over time, this isn’t just about pausing a skill; it may be more akin to erosion. And when the technology steps away, the skill doesn’t simply return to baseline. It can come back lower.”
In other words, skill and expertise are muscles that need to be trained to be maintained or they will be lost. So, automating these decisions with AI can lead to us losing critical skills.
