Will Faster Paths to Expertise Create Experience Debt?

In my previous post, I explored how AI might change the way expertise develops.

I still think there is real opportunity there.

AI may help people practice more often, see more examples, receive feedback faster, and encounter situations they may not normally experience until much later in their careers.

That is exciting.

But the more I think about it, the more I find myself asking another question.

If AI helps people develop faster, how do we know whether the expertise they develop is deep enough?

What Is Experience Debt?

Experience debt is the gap between what someone appears able to do and the depth of experience supporting that ability.

It is not always visible.

Someone may produce good work with AI support.

They may move faster.

They may seem more capable than expected.

But underneath that performance, they may not have built the same foundation that traditionally came from years of practice, mistakes, feedback, and difficult situations.

That does not mean their capability is fake.

It means the capability may be less tested than it appears.

Why This Risk Is Hard to See

Organizations are usually good at noticing visible performance.

They can see:

• work getting done

• outputs being produced

• time being saved

• processes moving faster

Those are important.

But experience is harder to see.

It is harder to measure whether someone has developed resilience, pattern recognition, and sound judgment across messy situations.

That usually only becomes visible when something unusual happens.

A new problem.

An edge case.

A situation that does not fit the pattern.

That is when the depth of experience starts to matter.

Two Different Paths to Expertise

Imagine two people.

One person develops expertise slowly over several years.

They do the routine work.

They make mistakes.

They see different situations.

They deal with difficult cases.

They receive feedback.

Over time, their expertise becomes grounded in lived experience.

Another person develops much faster with AI support.

They see more examples.

They practice with simulations.

They receive guidance and feedback quickly.

They may reach a useful level of performance much earlier.

That is not a bad thing.

But the two people may not have built the same kind of expertise.

The first person has experience they can draw from when things change.

The second person may have learned faster, but their experience may be more dependent on structured examples, guided practice, and AI support.

At first, the difference may not matter.

Both people may perform well in normal situations.

The difference may show up later, when the situation becomes unfamiliar.

Faster Is Not Always the Same as Deeper

I am not arguing against AI-assisted learning.

Actually, I think it may become one of the most useful ways to help people develop judgment faster.

But faster does not automatically mean equivalent.

A simulated situation is not the same as a real one.

A suggested answer is not the same as making the decision yourself.

Seeing an example is not the same as living through the consequence.

AI can help create better pathways to experience.

But we should be careful not to confuse exposure with depth.

The Hidden Part of Expertise

The visible part of expertise is performance.

• Can this person complete the task?

• Can they produce the output?

• Can they use the tool?

• Can they follow the process?

The hidden part is harder to see.

• Can they adapt when the situation changes?

• Can they notice when something feels wrong?

• Can they make a decision when there is no clear answer?

• Can they recover from mistakes?

• Can they handle uncertainty?

That hidden part is often built through experience.

If AI helps with the visible work but reduces the experiences that build the hidden part, the risk may not show up immediately.

It may accumulate quietly.

That is why I think of it as debt.

What I Am Still Thinking About

I do not think the answer is to avoid AI.

AI can help people learn faster, practice more often, and gain exposure to situations they might not otherwise encounter.

The question I keep coming back to is whether faster exposure produces the same kind of expertise as lived experience.

Perhaps it does.

Perhaps it does not.

What makes this difficult is that the difference may not show up immediately.

It may only become visible when someone encounters a situation they have never seen before.

That is why I have been thinking about experience debt.

Not as a reason to slow down AI adoption, but as a question worth paying attention to as we rethink how expertise develops.