We are likely witnessing a quiet crisis in our technical teams. While we celebrate the productivity gains of AI tools, a more insidious process probably unfolds beneath - the surface—the systematic erosion of our fundamental technical capabilities. This is not pure speculation or technophobic fear-mongering; it is the reality playing out.

A Scenario

The pattern is alarmingly consistent. A team adopts AI tools to enhance productivity. Initial results are impressive: faster code generation, quicker troubleshooting, more efficient documentation. Management celebrates the metrics. But something else is happening simultaneously—something few are measuring or even noticing until it's too late.

Technical professionals are gradually losing their ability to think critically, solve novel problems, and understand the systems they're supposedly managing. Tasks that were once routine become insurmountable challenges. Debugging skills deteriorate. Architectural thinking weakens. The ability to reason fades.

This is skill atrophy in action—the progressive weakening of cognitive and technical muscles through lack of use. And it might already be happening at an alarming rate. Engineers increasingly rely on AI to generate infrastructure code they don't fully understand. They use the LLM version of copy-paste solutions without grasping the underlying principles. When anomalies occur—as they inevitably do in complex systems—these engineers - at some point - lack the foundational knowledge to diagnose and resolve them. They become helpless without their AI assistants, frantically prompting for solutions rather than reasoning through problems.

Culture erosion

The consequences extend beyond individual performance. Teams develop a dangerous form of collective amnesia. Institutional knowledge that once resided in human minds now exists only in prompt histories and AI interactions. Critical context is lost. Subtle domain expertise that took years to develop atrophies in shorter timespans. The organization becomes brittle, vulnerable to novel challenges that fall outside the AI's training data.

What makes this crisis particularly dangerous is its invisibility. Unlike a sudden system failure or security breach, skill atrophy happens gradually, masked by the very productivity gains that executives celebrate. By the time the problem becomes obvious—when a critical system fails and no one knows how to fix it without AI assistance—it's often too late. The skills have already withered beyond quick recovery.

The demographic patterns make this trend even more concerning. Younger team members show significantly higher dependency on AI tools and correspondingly lower critical thinking scores. They've never experienced a professional environment where thinking deeply about technical problems was the only option. For them, AI isn't a tool—might be a cognitive prosthetic they've never learned to function without. I don't think the technology is ubiquitous enough to warrant such dependencies already. Apart from all the other risks.

This creates a perfect storm for technical organizations: experienced professionals whose skills are gradually eroding, combined with newer team members who never developed those skills in the first place. The could be a hollowing out of technical competence masked by a facade of AI-driven productivity.

The mechanisms driving this decline are clear. When we outsource our thinking to AI, we engage in cognitive offloading—the delegation of mental tasks to external tools. This starts with complex tasks but inevitably creeps into increasingly basic functions. Each time we defer to AI rather than engaging our own problem-solving faculties, we miss an opportunity for the deliberate practice that builds and maintains expertise.

More insidiously, AI systems fundamentally alter how we process information. Their immediate, synthesized responses short-circuit the cognitive dissonance necessary for deep learning. Their confident presentation discourages the suspension of judgment essential for critical thinking. Their tendency to align with existing beliefs reinforces rather than challenges our perspectives. In short, they make thinking too easy—and in doing so, they make us worse at it.

This is not an argument against AI tools. It's a wake-up call about how we're integrating them into our technical organizations. The current approach—unrestricted usage with success measured solely by short-term productivity metrics—risks creating a technical workforce increasingly incapable of independent thought and problem-solving. We are trading long-term capability for short-term convenience, and the bill will eventually come due.

What's needed is a fundamental shift in how we think about AI integration in technical teams. We need to recognize that human expertise and AI capabilities are not interchangeable. Human understanding of complex systems—with all its intuition, creativity, and adaptability—remains essential, especially when those systems behave in unexpected ways. We need deliberate strategies to preserve and develop human expertise alongside AI augmentation. Most importantly, it means changing how we measure success. If productivity is our only metric, AI dependency will continue to increase, and skill atrophy will accelerate. We need new metrics that value human understanding, resilience, and independent problem-solving capability—even if they sometimes come at the cost of short-term efficiency.

The stakes could not be higher. We are rapidly approaching what some researchers have called a "second singularity"—not when AI surpasses human intelligence, but when repeated outsourcing of decisions to machines leads to irreversible skill loss among human professionals. Once we cross that threshold, there may be no going back. Critical technical knowledge will be lost, not because it was forgotten, but because we stopped practicing it long enough for it to atrophy beyond recovery.

The time to act is now, before this quiet crisis becomes a catastrophic one.

The choice is ours: Will we become a technically competent society augmented by Expert Systems, or merely the increasingly helpless operators of systems we no longer understand? The answer will determine not just the fate of individual careers, but the resilience and sustainability of our entire technical infrastructure.

The time for complacency is over. What will we do today to preserve the technical competence of your teams?

Sources

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• Macnamara, B. N., Berber, I., Çavuşoğlu, M. C., Krupinski, E. A., Nallapareddy, N., Nelson, N. E., Smith, P. J., Wilson-Delfosse, A. L., & Ray, S. (2024). Does using artificial intelligence assistance accelerate skill decay and hinder skill development without performers' awareness? Cognitive Research: Principles and Implications.
• Natali, C., Marconi, L., Dias Duran, L. D., Miglioretti, M., & Cabitza, F. (2025). AI-induced deskilling in medicine: a mixed method literature review for setting a new research agenda. SSRN Electronic Journal.
• Osmani, A. (2024). Avoiding Skill Atrophy in the Age of AI. Elevate (Substack).
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