Who trains your next senior once agents do the junior work. · VANDAM

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# Who trains your next senior once agents do the junior work. · VANDAM

> AI is shrinking entry-level hiring. What the data shows, why it empties the pipeline to senior roles, and how to rebuild apprenticeship around agents.

URL: https://vandam.ai/notes/who-trains-the-next-senior  
Contact: hendrik@vibelab.be

# Who trains your next senior once agents do the junior work.

Four studies from the past year point the same way: AI is thinning the entry-level rung, mostly by slowing hiring. The judgment employers now look for was learned on that rung, so I set out what I would change as an employer.

Hendrik De Winne 24 October 2026 6 min read

Two economists at [Harvard](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5425555), Seyed Hosseini and Guy Lichtinger, followed the résumés of 65 million American workers at more than 280,000 firms to see what happens to a company’s junior staff once it starts building with generative AI. They spotted adoption through a specific kind of job ad, one that hires a “GenAI integrator.” From early 2023, junior employment at those firms fell about **9%** against non-adopting firms within six quarters. Senior employment kept its old trend.

The detail I keep returning to sits in their job posting data. Within the same occupation, the tasks generative AI handles well are increasingly removed from junior job postings. The ad for the first rung is being rewritten one task at a time. And the drop came from slower hiring. Juniors already inside those firms were, if anything, leaving less often than before. The role simply stopped being posted.

## Four datasets point the same way

One study is a signal. Over the past year, three more have landed on the same pattern from different data.

- **9%**
  Share of technical hires at junior level at [Anthropic](https://www.anthropic.com/research/employment-at-anthropic), down from 27%.
- **14%**
  Drop in the monthly job finding rate for 22 to 25 year olds in the most exposed occupations, against 2022, per [Anthropic](https://www.anthropic.com/research/labor-market-impacts).
- **19%**
  How far employment of 22 to 25 year olds in AI-exposed jobs sits below its kept-pace path, per [Stanford](https://digitaleconomy.stanford.edu/publications/canaries-in-the-coal-mine/).

The first number comes from inside the company that makes Claude. In its October report on its own workforce, which I read closely in [an earlier note](https://vandam.ai/notes/inside-anthropic), [Anthropic](https://www.anthropic.com/research/employment-at-anthropic) reports that the share of juniors in its technical departments slipped from **21%** to **14%** since early 2025. The proportion of junior technical hires “fell even faster, from 27% to 9%.” By April, engineering and research had deemphasized hiring for lower band technical roles.

The second comes from Maxim Massenkoff and Peter McCrory, economists at Anthropic, in [Labor market impacts of AI](https://www.anthropic.com/research/labor-market-impacts), published in March. Using US survey data, they found no rise in unemployment for exposed workers. What they did find was that young workers became less likely to be hired into the most exposed jobs: a **14%** drop in the job finding rate compared with 2022, with “no such decrease for workers older than 25.” They are honest that the result is “just barely statistically significant,” and that the young people who were not hired may simply have stayed in the jobs they had.

The third is the largest. Erik Brynjolfsson, director of the [Stanford Digital Economy Lab](https://digitaleconomy.stanford.edu/publications/canaries-in-the-coal-mine/), and his Stanford coauthors Bharat Chandar and Ruyu Chen work from ADP payroll records covering millions of US workers. In their August 2026 update, employment of 22 to 25 year olds in AI-exposed occupations stands **19%** below where it would be had it kept pace with less-exposed peers. Experienced workers show no comparable gap. The divergence has “widened steadily” since they first published it a year earlier, and it runs mainly through reduced hiring. They call their findings early, descriptive indicators, and they flag that the pattern weakens when they control for education. I take that caution seriously. Four teams, four datasets and one direction is still hard to wave away.

## The seniors everyone wants were made on that rung

Look at what employers now say they want. Anthropic’s hiring managers “increasingly emphasize applicants’ autonomy and judgment relative to specific technical expertise.” The Stanford team finds the same split in the wider market: young workers lose ground in occupations built on codified knowledge, while occupations that rely on tacit knowledge see faster employment growth for experienced workers. The market pays for the things that are hard to write down.

Those things were learned on the bottom rung. Hosseini and Lichtinger describe junior work as “intellectually mundane tasks,” routine but cognitively demanding, such as debugging code or reviewing legal documents. A junior did the dull version of the task next to someone who knew what good looked like, got corrected, and slowly absorbed why. That loop is where judgment came from. It was never written into a job description, because the work itself carried it.

[Enrique Ide](https://arxiv.org/abs/2507.16078), an assistant professor of economics at IESE Business School, built a model of exactly this loop. In it, novices acquire tacit knowledge by working alongside experts, and growth comes from the best experts passing their practice on to several novices at once. His conclusion is sharper than a headcount story.

> I show that improvements in entry-level automation increase output upon adoption but can reduce growth and welfare, even without reducing entry-level employment.
>
> Enrique Ide, Assistant Professor of Economics, IESE Business School, June 2026, [Source](https://arxiv.org/abs/2507.16078)

The damage in his model comes from moving novices away from the most productive experts. So keeping the same number of juniors only helps if they still learn from the strongest people and still get to touch the real work.

## The bill arrives in five years, at HR

Every individual decision here makes sense. A team lead with a good agent setup does not need a new graduate this year, and the business case for skipping the hire writes itself: one salary saved, no onboarding, no mistakes to fix. Nobody books the cost, because it lands years later in a different budget. Around 2031, the company looks for people ready to run a close, lead a client or own a system, and finds a gap where its 2026 intake should have been.

The obvious answer is to hire those people from the market. That works for one company. When every company reasons the same way, the market has no one left to sell, because nobody trained them. For a single employer it is a pipeline question. Across an industry it becomes a commons that everyone draws on and nobody refills.

The strongest objection deserves a fair hearing. Many juniors cost more than they produced in their first year, and some of that early work really was filler that an agent should do. The research also cuts the other way: experimental studies find generative AI raises productivity most for less experienced workers, as Hosseini and Lichtinger point out. I agree with both points, and they lead somewhere useful. A junior with good agents is worth more than a junior without them. That is an argument for redesigning the role, and a weak argument for deleting it.

## Four changes I would make as an employer

**Keep the entry path on purpose.** Put a number in the workforce plan: how many entry-level people you take on each year per senior, reviewed once a year like any other capacity. If the number goes to zero, that should be a decision someone signed, with the five-year consequence written next to it.

**Rebuild the apprenticeship around supervising agents.** The junior runs the agent on the work they would once have done by hand: the reconciliation, the first draft of the contract review, the bug triage. Their job is to check the output, find what is wrong and explain why. A senior then reviews the junior’s review. The junior stays close to the object-level work, which is where judgment forms, and the senior’s correction lands on reasoning instead of typing. Following Ide, I would pair juniors with the strongest people on the team, even though those are the people with the least spare time.

**Measure judgment directly.** If hiring managers now score judgment at the door, track it after the door too. Give juniors agent output with errors you already know about and record how many they catch. Log the calls they escalate and check later whether escalating was right. Within a few months you have a picture of who is ready for more, based on evidence a manager can defend.

**Count teaching as work.** The pairing costs senior hours. If those hours are invisible in objectives and reviews, they disappear in the first busy quarter. I would write a teaching load into senior roles and treat it like any other deliverable. This is the part of training I care about most in the [100xworker Program](https://100xworker.com), because it is the part companies skip when they focus on tools.

A first step costs an afternoon. Pull your hiring data for the last three years, count entry-level hires per senior in each team, and check whether the ratio fell. If it did, find out whether anyone decided that, or whether it happened one skipped requisition at a time.

## More notes

29 October 2026 6 min read

## Who owns what your AI learns.

Y Combinator now asks for startups that help companies own their intelligence. Before a mid-sized company owns a model, it can own the four things every model works with: context, evals, rules and connections.

26 October 2026 9 min read

## Delete the step first and give the agent whatever survives.

Musk’s five-step method puts automation last, and most AI programs start there because an agent is the one step a vendor can sell. I walk accounts payable through the steps in his order and show why an agent that clears exceptions fast hides the steps that should go.

## Your agents need a critic and so does the plan they carry out.

Models grade their own work too kindly, and the labs now separate the agent that makes from the agent that checks, the way finance separates maker and checker. I set out a four-layer review design with a minutes budget, and why the brief itself needs a critic with no stake in it.