The cost of over-dependence on AI

One look at the internet in 2025, and it feels fuller than ever: people can now use AI to write, design, and code without years of training.  But that AI dependence has flooded the web with low-quality content.

We risk outsourcing the hard parts – thinking, noticing, deciding, and executing – the very skills we spent decades building in school and work. 

LLMs can think on command.  Agents and LAMs (Large Action Models) can act on our behalf.  That’s power.  When used irresponsibly, it becomes a shortcut with an invoice of consequences that come due later.

When Tools Hollow Us Out

Tools save time.  Over time, they also reshape us. 

GPS killed our mental maps.  Calculators dulled our natural arithmetic.  Social media weakened our debate muscles – instead of arguing with dissenting views, we just block them.  AI follows a similar pattern – just broader and faster, with deeper consequences.

Once we stop bearing cognitive load, our ability to carry it shrinks – like muscles that atrophy when we stop exercising them.

What Atrophies First

AI magnifies both sides: in the hands of someone who knows the terrain, it’s a force multiplier. In the hands of someone who doesn’t, it becomes an answer key you don’t know how to grade.

• Research literacy: The grind of framing a question, mapping sources, and cross-checking claims.  Offload all of it to a model, and we forget how to triangulate.
• Sense-making: Turning noise into a narrative.  AI can condense text; only we can decide what matters.  That judgment is a muscle.  Stop using it, and it fades.
• Boundary-setting: Knowing when to stop, what’s “good enough”, and what merits escalation.  Agents don’t have instincts.  We do – until we stop using them.
• Error detection: The subtle squint that says, “this looks off”.  Lose that, and we’ll ship confident nonsense.
• Memory and context: Craftwork knowledge – edge cases, exceptions, weird precedent – lives in heads, habits, and experience.  If no one practices it, it leaks out of the team.
• Communication clarity: If every draft starts as AI slurry, our voices get diluted.  We become generic.  Our thinking follows suit.

The Impact of Atrophy

Tacit knowledge decays first:
The parts we can’t write down cleanly – good taste, nuance, institutional memory – require repetition under pressure.  Skip the reps, lose the edge.

Systems drift toward fragility:
If critical skills sit in a model’s hidden weights instead of people, a single outage or policy change becomes a critical business risk.

Accountability blurs:
“But the AI said so!” is never a defense.  Regulators and courts still attribute responsibility to humans.  If we lose the ability to verify outputs, we risk financial, legal, and reputational consequences.

What Stays Human – For Now, and On Purpose

Framing the problem; deciding the tradeoffs; assigning responsibility; handling outliers and edge cases; and communicating with clients and stakeholders in our own voices.

These are not “nice to have” skills.  They are the core of our competence.  If we outsource these, we don’t have teams – we have dependents on someone else’s infrastructure.

In designing systems and processes, focus on leveraging AI so that it adds value, but not at the cost of de-skilling individuals. This is our approach for integrating AI into the Lupl platform.

A Simple Operating Stance

• Use AI to accelerate thinking, not replace it.
• Keep manual drills in the loop.
• Make verification a habit, not an afterthought.
• Grow craft faster than we grow throughput.

Do this, and we can realize the benefits of AI models without eroding our skills.  Skip it, and we’ll win speed today while mortgaging judgement tomorrow.