Companies stopped hiring juniors. Who becomes senior now?

Good morning. Thanks for opening Still Monday again. Keep voting, replying, and passing it around. If somebody on your team hires people, develops people, or complains that nobody entering the workforce knows anything anymore, today’s issue may save them some time.

Companies are discovering a remarkably efficient way to avoid training inexperienced people:

Don’t hire them.

There is one slight problem with this strategy.

Experienced people have to come from somewhere.

Let’s get to work.

Entry-level employees have always been economically inconvenient.

They ask questions.

They make mistakes.

They need somebody experienced to review their work.

Sometimes they spend forty minutes solving something a senior employee could finish in seven.

Companies tolerated this because everyone understood the arrangement.

The company received inexpensive labor.

The young employee received something much more valuable: experience.

That bargain is beginning to change.

Researchers at Stanford’s Digital Economy Lab have been tracking employment using payroll records covering millions of U.S. workers. Their latest analysis found that employment among 22-to-25-year-olds in occupations with high exposure to AI is roughly 19% below where it would be if it had kept pace with employment among similarly aged workers in less-exposed occupations. The researchers stress that this is descriptive evidence, not proof that AI caused the entire gap. Stanford Digital Economy Lab

The mechanism, however, is interesting.

The weakness appears to be coming primarily from reduced hiring of young workers, rather than companies firing large numbers of existing ones. More experienced workers in those same exposed occupations do not show the same employment gap. Stanford Digital Economy Lab

That makes sense.

If software can draft the first version, summarize documents, perform basic analysis, answer routine customer requests or write simple code, the easiest productivity win is often removing work previously given to juniors.

But those tasks had a second purpose nobody put on the process map.

They trained juniors.

The first financial model you build is inefficient.

So is the tenth.

By the hundredth, you see things the spreadsheet cannot tell you.

That is how tacit knowledge develops.

Stanford’s researchers found something similar. Employment weakness among younger workers is concentrated in occupations relying heavily on codified knowledge—formal information that can be documented and taught—while experienced workers whose value relies more heavily on tacit knowledge acquired through repeated practice have held up better. Stanford Digital Economy Lab

Which creates a fascinating business problem.

Companies can automate the beginner work.

They cannot automatically manufacture the experience that beginner work used to create.

Patrick Harker, the former president of the Philadelphia Fed and now a Wharton professor, recently described entry-level positions as a kind of hidden apprenticeship system. Companies were paying for relatively inefficient junior labor while quietly creating the future supply of experienced workers. Fortune

Remove the inefficient labor and the spreadsheet improves immediately.

Remove the apprenticeship and the problem appears five years later.

You need a senior analyst.

Nobody developed one.

You need an experienced manager.

Everyone was hired experienced somewhere else.

You need somebody who understands how the customer, product and organization actually work together.

The AI knows the documentation.

The person who would have learned everything else never got hired.

This is not an argument against automation.

It is an argument for understanding everything a task does before eliminating it.

Some junior work produces output.

Some produces learning.

Sometimes it does both.

Owners who automate the first without replacing the second may create an extremely efficient talent shortage.

Stanford says the employment gap for 22-to-25-year-olds in highly AI-exposed occupations widened from about 15% in its earlier data to roughly 19% by June 2026. The researchers do not claim the evidence establishes AI as the sole cause. Stanford Digital Economy Lab

The canary is not dead.

It would, however, like management to look over here.

Half of 70 senior executives surveyed by BCG said they were already seeing signs of AI-related de-skilling inside their organizations. More than 60% believed de-skilling could become a material threat within three to five years. Judgment, problem framing and decision-making were among the capabilities they viewed as both highly important and particularly vulnerable. BCG Global

AI can answer the question.

Somebody should still know whether it was the right question.

NFIB’s August survey found 35% of small-business owners had job openings they could not fill, well above the historical average. NFIB

The economy has accomplished something impressive:

Companies cannot find experienced workers.

Young workers cannot get experience.

Please enjoy the synergy.

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When automation removes a basic task, identify the learning that task used to create.

Then replace it intentionally.

Have junior employees observe a real decision, attempt the work themselves, compare their judgment with the senior person’s, and review what they missed.

The goal is not protecting boring work forever.

It is protecting the path from I know the theory to I know what happens in reality.

That path needs repetitions.

A junior employee who requires supervision is visible cost.

A senior vacancy nobody can fill is visible pain.

Those two numbers belong in the same calculation.

Businesses routinely budget for equipment depreciation. They understand that productive assets need maintenance and replacement.

Experience has depreciation too.

People retire.

People leave.

People get promoted.

If the next layer never develops, eventually the company begins buying experience from the outside at market price.

The discount junior employee suddenly looks less expensive.

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COMPANY: We only hire experienced candidates.

CANDIDATE: How do people get experience?

COMPANY: At another company.

FIVE YEARS LATER

COMPANY: Why is experienced talent so expensive?

RECRUITER: Great question.

Stanford’s data show the weakest employment pattern in jobs where AI tends to automate human tasks. In occupations where AI is more complementary to human work, employment outcomes have generally been stronger. Stanford Digital Economy Lab

That distinction may end up mattering more than whether a job is simply labeled “AI exposed.”

A tool that replaces your repetitions can reduce your opportunity to learn.

A tool that makes your repetitions better may do the opposite.

Vote below.

And send this issue to the manager who says “young people need more experience” immediately before rejecting every candidate who doesn’t already have five years of it.

Maybe we can close the loop.

Technically Tuesday. Emotionally entry-level Monday.

— The Still Monday Desk