The Work Was Doing Two Jobs

A junior claims adjuster spends her first year handling fairly ordinary claims. Some of that probably teaches her something. Some of it probably doesn’t. She sees recurring patterns, gets decisions corrected, watches unusual cases get escalated and gradually takes on harder work. She may become excellent. She may plateau. She may leave before anyone finds out. Nobody designed this as an optimal curriculum. The claims needed to be processed, and she needed experience, and the same activity happened to serve both purposes.

That arrangement exists across professional work. Junior lawyers review documents and contracts. Analysts build models and check numbers. Developers fix bugs and write ordinary code. Accountants reconcile transactions. We tend to describe all of this as junior work, but it has always had two possible outputs: the work itself, and whatever capability the person develops while doing it.

AI changes the economics of the first output. If an AI system can process the ordinary claim faster and cheaper, the company has less reason to give that claim to a junior just to get the work done. There may still be regulatory, liability or audit reasons to keep a person involved, and those will vary by industry. AI may also make the junior dramatically more productive rather than remove the role at all. But wherever the human contribution to routine production genuinely declines, something changes: the relationship between the work a junior produces and the experience a junior accumulates is no longer automatic. The learning value of the work starts to become visible as a separate reason for doing it.

In the first three essays in this series, I’ve been following what happens as AI removes more routine work. The Average Is Lying to You was about how the mix of human work gets harder. The Side Door Is the Whole Building Now was about what happens when exception processes become a much larger part of the human process. And in The Dollar Threshold Is Static. Risk Isn’t., I looked at what happens to authority when the remaining decisions require more judgment. Put those together and you get a problem that isn’t really about whether junior jobs survive. It’s about how the people doing the harder work become capable of doing it.

The obvious response is to preserve some of the old work so people can learn, and I’m not convinced that’s necessary. Doing a thousand things isn’t the same as learning from a thousand things. Repetition without good feedback can just make someone very experienced at repeating the same mistakes. What matters is what happens inside those repetitions: variation, feedback, increasing difficulty, correction, consequences and the chance to compare your judgment with someone better than you. Once you think about it that way, AI could actually improve professional development rather than destroy it.

A junior adjuster doesn’t necessarily need a thousand randomly arriving claims. She could work on cases selected because they expose an important distinction: two claims that look almost identical but have different outcomes, a routine-looking claim containing one subtle warning sign, a case where experienced adjusters disagreed, a decision she makes herself before seeing what the AI or an expert concluded. Aviation has been doing a version of this for decades. We don’t insist that pilots encounter every dangerous situation for the first time with passengers behind them. Simulation is one part of a much broader training system, but it demonstrates something useful: experience can sometimes be designed rather than simply accumulated by chance. Professional work may move in that direction too.

But better training doesn’t solve the whole problem. In some ways, it makes the real problem easier to see: who pays? When a junior was producing economically useful work while developing, production and development were bundled together. Whatever training happened wasn’t free, but some of its cost was supported by the value of the work being produced. If AI takes away enough of that production value, development becomes easier to see for what it is: an investment. And part of the return on that investment can walk out the door.

A company can spend years developing a claims adjuster, lawyer, engineer or analyst only to watch that person take the resulting expertise somewhere else. Companies have always had this problem. AI doesn’t create it. What AI may remove is some of the economic activity that helped offset its cost.

When junior production had value, companies could employ people at the bottom of the pyramid knowing some would leave. If the productive value of those roles falls while the cost of developing expertise remains, the calculation changes. Maybe companies train fewer people. Maybe they train fewer people much better. Maybe AI makes juniors productive enough on harder work that the economics remain attractive. And maybe the market corrects. If enough insurers decide they would rather hire experienced claims professionals than develop them, experienced people become harder to find and more expensive, and eventually developing your own starts to look attractive again.

Except expertise has an awkward property: it takes time. You can raise the salary for an experienced adjuster tomorrow. You can’t create one tomorrow.

That is where this starts to look like a scarcity problem.

In Migrating Scarcity, I argued that when technology makes something abundant, scarcity doesn’t disappear, it relocates to whatever the newly abundant thing still depends on. That’s the mechanism underneath the book: relieving one constraint exposes another. I was mostly looking at technology, organizations and economic value when I wrote it, but the same mechanism may be operating inside the labor market.

If AI makes routine production abundant, one of the next scarcities may be accumulated human judgment. And accumulated judgment sits on a very different improvement curve from software. Models can improve quickly. Compute can be added. Software can be copied. Human judgment still has to be developed through some combination of exposure, decisions, feedback, mistakes, reflection and increasing responsibility.

We may get much better at that process. Simulation can compress some experience. AI can provide faster feedback. Training can become more deliberate than the accidental apprenticeship many professions rely on today. But compression isn’t elimination, and there is still a stretch of time between deciding that you need experienced people and having them.

That matters because each company can make a perfectly sensible decision on its own and still contribute to a problem for the whole industry. An insurer can prefer hiring experienced adjusters to developing juniors. A law firm can want associates who arrive ready to exercise judgment. A technology company can decide it needs fewer entry-level developers and more senior ones. If enough companies make those decisions at roughly the same time, they can all end up trying to buy something fewer of them are producing.

Eventually the price signal should matter. Salaries rise, experienced people become harder to recruit, and developing talent becomes economically attractive again. But markets can adjust prices much faster than they can create experience.

That delay is what makes this more than another argument about whether AI destroys entry-level jobs. Companies can invent new entry points. Training can improve. Simulations can become remarkably good. People may learn alongside AI faster than they learned without it. The harder question is whether enough organizations have an economic reason to pay for that development before the shortage becomes obvious.

Perhaps employees will finance more of their own professional development. Perhaps companies with exceptional training systems will treat talent development as a competitive advantage. Perhaps universities and professional bodies will move closer to the work itself. Perhaps new apprenticeship models appear precisely because the old bargain between junior production and learning no longer works. I don’t know which model wins.

But I think AI is exposing something the old professional pyramid allowed us not to price very carefully. Junior work wasn’t just cheap labor. It was also one of the mechanisms through which an economy produced experienced judgment.

AI may make much of the labor faster and cheaper without making judgment equally fast to create.

If that happens, the scarcity hasn’t disappeared. It has moved from doing the work to producing the people capable of handling what remains.

And that may be a much slower constraint to solve.