Who Becomes an Expert When the Junior Work Disappears?
Automation can improve today’s output while quietly weakening the path to tomorrow’s judgment
Part five of seven in the Artificial Intelligence in Education series, beginning with AI Did Not Break Education. It Exposed It. Source cutoff: Oct 30, 2026.
Every organization using advanced artificial intelligence eventually reaches the same reassuring conclusion: a human expert will remain responsible for checking the important work.
That answer creates another question. Where will the future expert come from?
Experts begin as novices who do limited work, observe experienced people, make mistakes, receive feedback, encounter exceptions, build relationships, and take on more responsibility. Much of that learning has been embedded in work that organizations now hope to automate.
Routine junior work should not be preserved merely because earlier generations endured it. Some of it is tedious or badly designed. The danger appears when an organization removes the production task and forgets the learning that occurred inside it.
I call that risk the Missing Junior Loop.
The short answer
A job is a production role, a set of relationships, and often a learning sequence. Routine work can supply repetition, feedback, exceptions, context, and increasing responsibility.
Artificial intelligence can raise novice performance while leaving the development of independent judgment unresolved. Assisted output, independent competence, and future expertise are different outcomes.
Colleges and employers should remove obsolete chores and rebuild developmental functions through simulation, rotation, coaching, supervised work, and real responsibility.
A job is more than a collection of tasks
Organizations often describe jobs through outputs: answer the customer, review the contract, test the code, reconcile the account, draft the brief. A job is not simply a collection of tasks. It is a role in a production system, a set of relationships, a source of identity and trust, and often an entry into a profession.
A novice experiences the role as a sequence of encounters. Repetition makes the basic steps familiar. Then an exception appears. The novice asks why the standard answer does not apply, watches someone more experienced, and learns which details change the decision.
Feedback arrives through a supervisor, customer, failed test, rejected draft, or the consequence of the work itself. Over time, the novice builds patterns and counterexamples. The person learns who knows what, which rules have exceptions, and when uncertainty must be escalated.
Many junior jobs provide poor supervision and years of repetition without progression. The point is not that every old pathway worked. Removing work changes more than headcount and cost when development was hidden inside the role.
Artificial intelligence can help novices now
Brynjolfsson, Li, and Raymond studied 5,172 customer-support agents at one company. Access to a generative artificial-intelligence assistant increased productivity on average, with the largest gains among less-skilled and less-experienced workers.[1]
That is a real benefit. A novice who receives useful guidance at the moment of need may resolve problems accurately and learn faster than someone waiting for an overloaded supervisor.
It also creates a measurement question. Did the worker become more capable, or did the worker and tool become more productive together? Both outcomes matter, but they are not the same.
The apprenticeship problem is already visible
Entry-level work has not disappeared across the economy. But pressure is visible in several important pathways.
A Stanford Digital Economy Lab analysis of payroll data reported weaker employment among workers ages 22 to 25 in the most artificial-intelligence-exposed occupations, with the divergence appearing more in hiring than in separations.[2] A Revelio Labs analysis of profile-based call-center estimates also attributes recent contraction mainly to a falling entry rate rather than a surge in exits.[3]
Anthropic reports that junior workers’ share of its technical hires fell from 27 percent to 9 percent and their share of the technical workforce from 21 percent to 14 percent while the company grew rapidly.[4] These measures come from different settings. Together they show how an entry rung can narrow without a wave of layoffs.
These signals are enough to make apprenticeship a design problem now.
The Missing Junior Loop
The mechanism is straightforward:
An organization automates routine work because artificial intelligence can produce it more quickly or cheaply.
Experienced people remain responsible for verification, exceptions, relationships, and consequential judgment.
Novices receive fewer repetitions, less observation, and less contact with the exceptions through which judgment develops.
The organization later needs experts but has weakened the pathway that produced them.
Artificial intelligence may also improve the pathway. It can generate practice, expose reasoning, provide timely feedback, and allow novices to attempt harder work earlier. New roles can appear. The question is whether the redesigned system produces independent capability and increasing responsibility, not whether every old task survives.
Organizations should track the expertise pipeline: entry, varied cases, feedback, relationships, progression, time to competence, mentoring capacity, promotion, and the people who never obtain a first role.
A falling junior share can coexist with rising junior headcount. Time in a job can coexist with little development. Count the flow of expertise, not only the stock of employees.
Expertise still changes the value of the tool
An Anthropic analysis of roughly 400,000 coding-agent sessions found that greater domain expertise was associated with more effective delegation and higher task success.[5] The relationship makes intuitive sense. Experts define better objectives, supply better context, recognize failure, and know when to stop.
Easy access to an agent does not erase the return to expertise. It can increase the value of expertise by giving knowledgeable people more leverage.
This creates the verifier paradox. Organizations want experts to supervise artificial intelligence while automation changes the work through which novices become experts.
Preserve the function, not the chore
Before automating a junior task, ask what it contributed to development. Did it provide repetition, expose recurring failure modes, require communication with a client, teach source judgment, or create a relationship through which feedback and trust could grow?
Once the function is visible, you can redesign or replace the historical task. Repetition may move into simulation. Rotations can expose varied cases. A residency or co-op can provide supervised live work. A synthetic case can prepare someone for a rare event. A real project can add consequence and responsibility.
Judge the replacement by transfer. Can the learner handle a new case, work without the scaffold when required, detect an error, communicate uncertainty, and accept responsibility?
A replacement-learning agreement should name the function being removed and the route replacing it: varied cases, practice, feedback, relationships, real responsibility, supervision, independent demonstration, progression, an owner, and a budget.
Automation promises lower production cost. Deep development remains expensive. If the learning route isn't funded, it isn't a route.
Simulation helps, but reality resists
Artificial intelligence can generate cases in quantities no instructor could prepare manually. It can vary facts, play a client or patient, reveal one clue at a time, and adapt difficulty. That can expand access to practice where authentic opportunities are scarce.
A simulation is built from assumptions about what matters. It may omit the awkward human detail, organizational constraint, or material consequence that makes the real case educational. A simulated client does not depend on the learner. A generated codebase does not contain years of institutional history.
Poorly supervised workplace experience can teach less than a well-designed simulation. The test is the capability. Where relationship, consequence, ambiguity, or accountability is part of the capability, supervised real work remains necessary.
Apprenticeship is a shared obligation
Registered apprenticeship provides one useful reference. A recent U.S. evaluation found better employment and earnings outcomes for participants in the programs studied than for comparison groups.[6] The model combines paid work, related instruction, mentoring, and progression.
The larger principle is that education and productive work can be linked through an explicit ladder. Universities can provide foundations, simulation, reflection, and assessment. Employers can provide authentic cases, tools, supervision, and responsibility. Professional bodies can define standards and protect the public.
A university cannot promise readiness while ignoring the workplace where expertise develops. An employer cannot remove the learning ladder and expect schools to deliver finished experts with current organizational judgment.
Reskilling current workers and creating entry for new workers are different obligations. A credible system must serve both.
Capability needs better signals
If the entry-level job changes, the signal used to enter it may need to change. A work sample, portfolio, simulation, oral defense, structured interview, supervised project, or apprenticeship record can show something a degree title does not.
Each can also be coached, gamed, biased, inaccessible, or costly. The goal is not to replace one proxy with a pile of new ones. A useful signal connects evidence to capability: what the candidate did, under what conditions, with which tools, how the result was checked, what happened when the case changed, and who observed the work.
A design test for the new ladder
Before automating junior work, an organization should ask:
What production value does the work provide, and which repetitions, exceptions, relationships, and feedback carry its learning value?
What combination of simulation, coaching, rotation, and supervised live work will replace them, and who owns the time and cost?
How will novices demonstrate independent performance, transfer, verification, and escalation?
What evidence will show whether the new pathway works better than the old one?
Expertise is a flow
Artificial intelligence may increase current output and widen access to expert practices. Those gains should be pursued.
The system will fail if every design assumes that experienced people will remain available while no design explains how new people become experienced.
The practical goal is straightforward: remove obsolete chores, keep the learning, and build better ladders before the old ones weaken.
Part six, Preparing Graduates for AI-Native Organizations, follows graduates into the organizations they will help redesign. What capabilities matter when artificial intelligence is embedded in workflows, teams, products, and decisions?
Questions, corrections, or disagreements are welcome. You can reach me directly at dave@aworkingmodel.com.
Sources
1. Brynjolfsson, Li, and Raymond, “Generative AI at Work.” 2025. Field evidence from 5,172 customer-support agents at one company; the study measured performance with assistance rather than long-term independent expertise.
2. Stanford Digital Economy Lab, “Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence.” Revised 2026. Payroll-based early indicators; results are sensitive to controls, pre-trends, and data source.
3. Wilkie-Rogers, “After a Decade of Growth, Global Call Center Employment Is Shrinking.” Revelio Labs, Oct 6, 2026. Profile-based weighted estimates that may be revised and do not establish firm-level causation.
4. Wilmers, Catipon, Massenkoff, and McCrory, “How Employment Is Changing at Anthropic.” Oct 16, 2026. First-party evidence from one fast-growing frontier company; shares, headcount, hiring, and causal labor effects are different measures.
5. Anthropic, “Agentic Coding and Persistent Returns to Expertise.” 2026. Vendor analysis of one coding product using classifier-derived measures; it does not track long-term learning.
6. Mathematica, “The Impact of Registered and Unregistered Apprenticeship.” 2026. Comparison-group evaluation of apprenticeship programs; it does not isolate which program element produced the outcomes or establish direct transfer to every knowledge profession.