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Expertise Decay

The Expertise You're Losing Is the Expertise You'd Need to Notice

By Nora Kaplan— May 28, 2026

Feature image for article: The Expertise You're Losing Is the Expertise You'd Need to Notice

Expertise inside an organization is a practice, maintained through repetition, apprenticeship, and evaluation against standards that live in people's heads. Agentic AI disrupts all three simultaneously, and the decay compounds in a way that's genuinely hard to see: the capacity to detect expertise loss erodes on the same curve as the expertise itself. When experienced developers using AI tools believed they were 20% faster but measured 19% slower, the error pointed in exactly the direction that would prevent correction.

Expertise Decay

The Expertise You're Losing Is the Expertise You'd Need to Notice

By Nora Kaplan— May 28, 2026

Expertise inside an organization is a practice, maintained through repetition, apprenticeship, and evaluation against standards that live in people's heads. Agentic AI disrupts all three simultaneously, and the decay compounds in a way that's genuinely hard to see: the capacity to detect expertise loss erodes on the same curve as the expertise itself. When experienced developers using AI tools believed they were 20% faster but measured 19% slower, the error pointed in exactly the direction that would prevent correction.

Window and Mechanism

What the Maturity Gap Is Actually For

Only 9% of enterprises report meaningful AI autonomy in production. That's widely treated as a sign organizations are stuck. I think it means they're in the last period where certain things are still possible. The analysts still run the reports. Someone still knows what a good output looks like from years of producing them by hand. That knowledge has a half-life. What gets built during the overlap determines whether autonomy is trustworthy—or just operational.

Window and Mechanism

When Smooth Work Feels Like Good Work

Experienced developers predicted AI made them 24% faster. Actual result: 19% slower. After completing the work, with their own timing data in hand, they still believed AI had helped. That 39-point gap between perceived and actual performance points to something structural about how humans read fluency as accuracy. For organizations building AI-augmented workflows right now, it's also a design problem with concrete solutions.

Window and Mechanism

What the Maturity Gap Is Actually For

Only 9% of enterprises report meaningful AI autonomy in production. That's widely treated as a sign organizations are stuck. I think it means they're in the last period where certain things are still possible. The analysts still run the reports. Someone still knows what a good output looks like from years of producing them by hand. That knowledge has a half-life. What gets built during the overlap determines whether autonomy is trustworthy—or just operational.

Window and Mechanism

When Smooth Work Feels Like Good Work

Experienced developers predicted AI made them 24% faster. Actual result: 19% slower. After completing the work, with their own timing data in hand, they still believed AI had helped. That 39-point gap between perceived and actual performance points to something structural about how humans read fluency as accuracy. For organizations building AI-augmented workflows right now, it's also a design problem with concrete solutions.

Window and Mechanism

What the Maturity Gap Is Actually For

Only 9% of enterprises report meaningful AI autonomy in production. That's widely treated as a sign organizations are stuck. I think it means they're in the last period where certain things are still possible. The analysts still run the reports. Someone still knows what a good output looks like from years of producing them by hand. That knowledge has a half-life. What gets built during the overlap determines whether autonomy is trustworthy—or just operational.

Window and Mechanism

When Smooth Work Feels Like Good Work

Experienced developers predicted AI made them 24% faster. Actual result: 19% slower. After completing the work, with their own timing data in hand, they still believed AI had helped. That 39-point gap between perceived and actual performance points to something structural about how humans read fluency as accuracy. For organizations building AI-augmented workflows right now, it's also a design problem with concrete solutions.

Baseline Keeper

The Test Nobody Wants to Take — A Conversation With the CLO Measuring What AI Leaves Behind

Baseline Keeper

The Test Nobody Wants to Take — A Conversation With the CLO Measuring What AI Leaves Behind

Measurement Recursion

You Can't Grade What You've Forgotten How to Do

Knowing whether an AI agent performed well requires someone who can do the work without it. That someone is becoming scarce. Three months of AI-assisted clinical practice was enough to measurably degrade doctors' independent diagnostic accuracy.

A handful of organizations have caught on. They're preserving pockets of unassisted workflow, not for productivity, but to maintain the human judgment that evaluation depends on. Most organizations see this as redundancy. It's actually calibration infrastructure, and the window to build it is shrinking while the spreadsheet says everything looks fine.

Measurement Recursion

You Can't Grade What You've Forgotten How to Do

Knowing whether an AI agent performed well requires someone who can do the work without it. That someone is becoming scarce. Three months of AI-assisted clinical practice was enough to measurably degrade doctors' independent diagnostic accuracy.

A handful of organizations have caught on. They're preserving pockets of unassisted workflow, not for productivity, but to maintain the human judgment that evaluation depends on. Most organizations see this as redundancy. It's actually calibration infrastructure, and the window to build it is shrinking while the spreadsheet says everything looks fine.

TAKE NOTE

Rapid erosion: Doctors relying on AI diagnostic tools showed measurable independent skill decline after just three months, suggesting the evaluation baseline degrades faster than most assume

Invisible compounding: An estimated 78% of AI errors go undetected, and that miss rate worsens as the humans doing the reviewing lose the expertise to catch problems

Misplaced confidence: The METR trial found developers were actually 19% slower with AI coding tools while believing they were 20% faster, a gap no dashboard would surface

Deliberate friction: Some early adopters now run reliance drills and maintain unassisted workflows specifically to preserve evaluation capacity, treating human skill as measurement infrastructure

Leadership vacuum: Only 6% of leaders report meaningful progress designing how human judgment and AI assistance should structurally coexist, leaving the calibration choice unmade by default