And Vijay Says…

Klarna is an interesting AI story partly because the AI worked.

When the company launched its AI customer-service assistant in 2024, the early numbers were extraordinary. After its first month, Klarna said the assistant was handling two-thirds of customer-service chats and doing work equivalent to 700 full-time agents. Average resolution time had fallen from 11 minutes to under two, repeat inquiries were down 25%, customer satisfaction was on par with human agents, and Klarna estimated the assistant would improve profits by $40 million in 2024.

A year later, something seemingly contradictory happened. Klarna began emphasizing humans again. CEO Sebastian Siemiatkowski acknowledged that the company’s aggressive focus on AI and cost reduction had affected service quality. The company began hiring again and put renewed emphasis on service quality and the role of people alongside AI.

It would be easy to tell this as another story about AI being overhyped, except Klarna didn’t abandon AI.

The more interesting question isn’t whether Klarna’s AI worked. It’s what happens to the human job when it does.

If machines handle a large share of straightforward customer interactions, the people who remain aren’t simply doing less customer service. They’re increasingly handling the things the automated system didn’t resolve, the things customers still want a person for, and the situations where something about the normal path didn’t work.

Klarna shows the first part of that pattern. The rest is what I’ve been trying to understand through this series. When machines disproportionately remove routine work, the averages can start lying to us about the work people actually do. As more human work consists of things that don’t fit the standard process, the side door can start becoming the whole building. When those decisions require more judgment, the dollar threshold starts looking increasingly crude because risk isn’t static. And when routine work was also how inexperienced people became experienced, the work was doing two jobs.

I’ve been treating those as different problems. I’m no longer sure they are.

The normal case wasn’t just most of the work. It was the organizing assumption of the company.

Think about how most organizations are built. Processes exist because enough situations repeat that we can decide in advance what should happen next. Jobs exist because enough tasks can sensibly be bundled together. Averages are useful because they tell us something about what people actually do. Approval limits work because fairly simple rules are good enough for a large number of reasonably predictable decisions. People get better partly because they do enough ordinary work before they’re asked to handle the difficult stuff. Even a cumbersome exception process makes sense when exceptions are actually exceptions.

The normal case made all of that possible, which is why I keep coming back to the argument behind Migrating Scarcity. When technology makes something abundant, scarcity doesn’t disappear. It moves to whatever the newly abundant capability still depends on.

There is something I didn’t take far enough in the book. What if the thing becoming abundant isn’t just a resource the organization uses? What if it’s the kind of work the organization was built around?

Much of the work companies are automating first has something important in common. It is structured enough, frequent enough or predictable enough to hand to a machine with confidence. Those are also many of the characteristics that made the work possible to standardize in the first place.

As more of that work moves to machines, what reaches people starts to look different. More of it involves ambiguity or disagreement. Facts don’t line up neatly. Objectives conflict. The policy covers most of the situation but not quite all of it.

And those aren’t just differences in difficulty. They put pressure on how work is measured, where decisions get made, when something gets escalated and how people learn to exercise judgment.

That raises a question I hadn’t really considered before writing these essays. If you were designing an organization mainly to handle that work, would you design the organization we have today?

I doubt it.

Take a different, hypothetical company. It automates 70% of a process. Unit costs fall, response times improve, customers get answers faster, and the business case delivers exactly what management promised. The automation was successful.

But the remaining 30% may now need different skills, different measures of productivity and more room for judgment. Escalation paths designed for occasional exceptions may start carrying much more of the human workload. Authority may still be calibrated to the old mix of work. And the company may eventually discover that some of the routine work it removed was also helping develop the people it now needs most.

The company solved the problem it set out to solve, but in doing so it exposed the next one. That’s the part that connects back to Migrating Scarcity. Removing a constraint and capturing the value released by removing it are not the same thing. If the constraint moves, eventually the organization has to move with it.

I think this may explain some of the frustration we’ll see as AI deployments mature. Automation rates will rise and unit costs will fall. Individual AI projects will show perfectly respectable returns. Yet executives may still wonder why the organization isn’t becoming as fast, adaptive or productive as the technology seemed to promise. The natural reaction will be to ask what else can be automated, and sometimes that will be exactly the right question.

But sometimes the AI will already have done its job. It will have removed enough of the normal work to expose something we hadn’t thought much about: much of the organization was built on the assumption that people would still be doing that work.

At that point, another model isn’t necessarily the answer.

The organization has to move with the scarcity.

For more than a century, one of management’s great achievements was learning how to make ordinary work repeatable enough to scale. We broke jobs into pieces, standardized processes and built organizations capable of handling enormous amounts of recurring activity efficiently.

AI may turn out to be extraordinarily good at precisely the work we became extraordinarily good at organizing. That doesn’t make the organization unnecessary. It changes what we need the organization to be good at.

We designed the organization around the normal case and built special machinery at its edges for everything that didn’t fit. AI may be taking the normal case first.

What happens when the edges are increasingly where the organization lives?

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.

Most companies have a number at which they stop trusting you.

You might be able to approve a $5,000 expense but not a $50,000 one, offer a customer a small credit but not a large one, or approve an ordinary claim but send an unusual one upstairs. We call these approval limits, and there is nothing particularly wrong with them. They are one of the ways organizations control risk.

The logic is familiar. The larger the consequence of a decision, the smaller the group of people allowed to make it. When something falls outside the rules, authority moves upward until it reaches someone the company trusts to make the call.

In The Average Is Lying to You, I argued that AI changes the mix of work left for people. In The Side Door Is the Whole Building Now, I followed that into the exception processes that increasingly have to handle it. Put those two together and you get an uncomfortable problem: the work left for people requires more judgment, while the systems governing that work were built to constrain it.

Imagine a claims manager looking at a $75,000 claim. The documentation is messy. The policy doesn’t quite fit. She’s handled hundreds of claims and thinks this one should be paid.

Should the company simply raise her approval limit from $10,000 to $75,000 and tell her to use her judgment? Of course not. The approval limit wasn’t invented because somebody disliked judgment. It exists because the company is taking risk. More discretion can mean more inconsistent decisions, more mistakes, more opportunity for fraud and, occasionally, more damage from someone who simply isn’t very good at exercising judgment. So authority can’t move by itself. Control has to move with it.

For decades, hierarchy has helped solve that problem. The claims manager sends the decision to her supervisor. The supervisor sends the unusual one to a specialist. Large enough decisions go another level higher. The organization controls risk partly by controlling who is allowed to take it. That works surprisingly well when unusual decisions are unusual. It works less well when technology systematically removes the usual ones.

Here’s where I think the interesting possibility is, and it isn’t just giving people more visibility after they’ve already decided. It’s that the boundary itself can stop being static.

Right now, that boundary is a dollar figure. Under $10K, decide. Over $10K, escalate. But a threshold like that treats every $75,000 claim the same, and every $12,000 claim the same, regardless of what’s actually inside them. Suppose instead the system looks at the case itself: a $75,000 claim with a familiar fact pattern, strong supporting evidence, an experienced adjuster and no fraud indicators can be decided on the spot. A $12,000 claim with contradictory evidence, unusual claimant behavior and a departure from normal policy gets escalated, even though it’s a fraction of the size. The dollar threshold is static. Risk isn’t.

That’s a different kind of control than watching a decision after it’s made. The evidence, the comparable cases and the departure from normal pattern are what decide, before money moves, whether this case needs another judgment or not. What gets logged afterward — her reasoning, how her decisions compare to her peers’, whether she’s repeatedly overriding the same policy — isn’t a substitute for that control. It’s what keeps recalibrating where the boundary sits.

That distinction matters because hierarchy is a fairly blunt instrument for governing judgment. A manager approving a decision doesn’t necessarily know more about the case than the person who sent it upstairs. Sometimes the manager contributes experience or judgment that genuinely improves the decision. Sometimes the decision moved because the organization decided long ago that someone with one title could take the risk and someone with another title couldn’t.

AI gives us a chance to distinguish between a decision that needs another judgment and one that merely needs another signature.

The person closest to the work can potentially have more room to exercise judgment while the organization gets better visibility into how that judgment is being used. Instead of controlling every decision through prior permission, it can decide which decisions need permission, which can happen inside defined boundaries, and which can be reviewed after the fact. Once permission can respond to context rather than just hierarchy, permission itself starts to look less like a policy and more like an architecture.

I called this Offensive Permission Architecture, or OPA, in Migrating Scarcity. The idea is that permission itself can be designed: who can act, under what conditions, with what information, inside what boundaries, and what gets reviewed before versus after the fact. Most companies inherited their answers to those questions from an operating model built before this kind of visibility was possible. AI changes enough of the underlying economics that those answers are worth reopening.

Most of the AI conversation is about the falling cost of doing work. I think something else may be falling too: the cost of controlling who is allowed to do it. If AI can make the context around a decision visible, preserve the reasoning behind it, and identify risk before and after someone acts, delegation itself becomes cheaper. We built authority around the value of the transaction because we couldn’t cheaply measure the risk of the decision. AI may change that.

It doesn’t mean hierarchy disappears. Some decisions are important enough that another person should look at them before anything happens. Some require genuinely different expertise. Some risks should never sit with one individual. But perhaps decisions should move upward because another person’s judgment adds something, not simply because hierarchy is the only control mechanism we have.

For decades, we have used hierarchy to answer two different questions: who has the judgment to make this decision, and who are we willing to trust with the risk. AI may finally let us separate them. A decision doesn’t have to move upward because it crossed a number on an org chart. It can move because another person’s judgment would actually make it better. That’s a very different reason to have a hierarchy.