And Vijay Says…

A senior leader asked me a simple question last week. What happens to the talent pyramid?

I wanted to answer with a drawing. A diamond, an hourglass, a column. Every one of them assumed I already knew something I did not.

So I went back to what the pyramid was for. It was never just an org chart. It produced work, it trained people, and it decided who got trusted with what. A big hierarchy did not guarantee the last two. Plenty developed people badly. But where development happened, it rode along with the work.

Companies measured the work. They took the rest for granted.

What the fictional insurer in my book found

The Corner Case follows a fictional insurer. Automation climbs to 61 percent, customers get answers faster, every dashboard is green. Then someone looks one row down. The time spent on each claim that still reaches a person is up 28 percent.

Nothing is broken. The easy claims left the queue and what remains is harder. Junior adjusters used to learn on those easy claims. Now referrals from juniors to senior adjusters are down 31 percent, and nobody can tell whether the juniors are learning faster or just asking less.

Four things I think will change

Management layers built around moving information come under pressure. AI can handle much of the status, routing and reporting. Developing people and accepting accountability remain management responsibilities.

The base becomes humans working with agents, but accountability stays human. In the book, an AI workflow starts clustering settlements just under $10,000. Nobody told it to. It had been rewarded for speed, and crossing $10,000 triggered extra review. Each settlement looked fine. Only the pattern was wrong. Someone had to know which patterns mattered, investigate, and have the authority to intervene. Monitoring tools can detect the pattern. Someone still has to decide whether it is acceptable and what to do about it.

The work that reaches people gets harder. That raises the value of experienced specialists. But AI is also helping less experienced people do senior work, and it will take more of the hard cases itself. Both are true at once.

Productivity gains do not necessarily tell us what people have learned. In a study of more than 5,000 customer support agents, Brynjolfsson, Li and Raymond found that AI assistance raised productivity by 15 percent on average, with the largest gains among less experienced workers. They also found evidence of learning. A team at the Technical University of Munich randomly assigned 275 programming students and found that AI assistance improved exercise scores without matching improvements in conceptual understanding or code comprehension. The distinction matters: better performance with AI does not, by itself, establish what someone can do independently.

A company can need fewer experienced people and still be short of them. Say a business has 24 experienced specialists and needs 24. Three years later AI absorbs much of the hard work and the requirement drops to 16. But retirements, attrition and a thin pipeline have left only 13 qualified people. It needs a third fewer than before and is still three short. If the business grows fast and AI absorbs less, the need could be 28 instead.

These are made-up numbers to show the mechanism, not a forecast. The point is that demand and supply move independently. The org chart counts positions. It does not tell you whether you can produce the people to fill them.

Buying experience does not fix it

The obvious move is to hire experienced people from outside. For one company that is rational. For all of them it fails. Hiring moves capability around. It does not create any. If everyone cuts junior development and plans to buy later, somebody still has to make the experts.

Economists call this underinvestment in transferable skills. You pay for the training and the employee leaves. AI sharpens it, because the useful work juniors did used to help pay for their development. AI may also make training cheaper, through simulation, tutoring and supervised practice. Nobody knows yet whether it is cheaper than the work it replaces. Measure it. Do not assume.

There is a second problem. We have to know when someone is ready. The old system was slow, but it produced evidence. Managers watched people handle harder work and saw what they escalated and what they got wrong. Now a junior with a good agent can produce work that looks like an experienced colleague’s. The deliverable tells you much less about the person.

That is not only a junior problem. A manager can present a strong strategy built with AI without understanding its assumptions. A senior engineer can accept agent-written changes without grasping the consequences. Output is no longer enough evidence to hand anyone authority.

What I would measure

I would not start by guessing the shape of the organization five years out. I would ask three things. How long does it take someone to earn independent authority, measured by judgment rather than output speed? How much experienced judgment does the business still need, counted in decisions rather than headcount? And is the company producing that judgment fast enough, and at what cost?

Simulation, apprenticeship and supervised AI-assisted work may help. We have yet to establish which combinations can reliably replace the learning opportunities automation removes. Treat each as a bet and decide in advance what result would make you stop.

Not every company needs to train all of its own experts. Some can buy what they need reliably. Every company needs to know where its future experts come from, and whether that source will last.

The question behind the shape

I do not think one shape replaces the pyramid. Some companies will need fewer experts as AI takes more of the hard work. Others will need more as they grow. Some will find that demand and technology are not the constraints. Their ability to develop qualified people is.

The old pyramid produced today’s work and, along the way, tomorrow’s experts. AI makes the first cheaper and may make the second more expensive. One company can buy experience instead of building it. An entire industry cannot do that forever.

The question is no longer what shape replaces the pyramid. It is who will pay to produce the people everyone expects to hire.

Most conversations about AI transformation ask whether the technology can do the work. We debate accuracy, cost, reliability and how quickly companies can move from pilots to production. I expect the technology to keep improving. I’ve become more interested in what happens when the answer is yes.

In Migrating Scarcity, I argued that when technology makes one thing abundant, the constraint moves somewhere else. My new book follows one of those moves into the workplace.

Consider an insurance company where AI now resolves 61 percent of claims, up from 43 percent six months earlier. Customers get answers faster, and cost per claim is down. Then a manager looks one row lower in the operating report. For the claims that still reach a person, average handling time is up 28 percent.

The employees haven’t suddenly become less productive. The work reaching them has changed. AI is taking the straightforward claims, leaving humans with a greater concentration of difficult ones. The 28 percent increase is real, but comparing it with the old number can lead management to the wrong conclusion.

There is a less obvious problem. Those easy claims were doing more than generating transactions. They were how new adjusters learned the business, how managers developed confidence in someone’s judgment, and how experienced people noticed patterns no single file revealed. Remove that work and you change how the organization develops expertise, not just what it costs to process a claim.

We’ve heard plenty about AI eliminating entry-level jobs. I’m interested in what happens to the careers that remain. We assume people will move to higher-value work, but that assumes they already have the judgment to do it. If AI removes the work that used to teach them, where does that judgment come from?

These questions became the basis for my new book, The Corner Case: Where Human Value Moves When AI Does the Easy Work. A corner case is the situation that doesn’t fit the pattern. As AI absorbs the patterns, more of the human role becomes handling the cases that don’t, and that is the work people now have fewer chances to learn.

I chose to explore the questions through a fictional insurance company called Northstar. Maya Rao runs its claims operation. Daniel Cho is a veteran adjuster whose experience has made him indispensable. Leah Bennett is earlier in her career, trying to establish herself in a business where the path that produced Daniel’s expertise is changing underneath her.

The company is succeeding at automation. Its leaders are competent, and the technology is delivering much of what was promised. But success creates decisions that are harder than the original technology choices. Maya, Daniel and Leah see those decisions differently, and each has good reasons for doing so.

I won’t say much more about what happens at Northstar. I’d rather readers encounter the choices themselves. The book combines the story with research and practical playbooks for leaders and individuals. It also makes a claim that can be tested instead of asking readers to accept a fictional company as evidence. If AI removes work that helped people learn, and nothing replaces that learning, comparable new groups should take longer to earn independent authority, perform worse on unfamiliar cases, lean harder on senior colleagues, or need more rescuing when the AI is uncertain or wrong. Much routine work teaches almost nothing, and if that is true in your organization, the effect won’t appear. The point is to look before deciding.

I come to this from two perspectives. I spend much of my professional life helping companies make these bets on AI, which means I have a commercial interest in these questions being taken seriously. I’m also the father of a daughter preparing to enter a labor market that may look very different from the one I entered. I believe strongly in the potential of this technology, but I don’t think enthusiasm should exempt us from examining what happens when it succeeds.

The Corner Case is not another attempt to predict how many jobs AI will eliminate or create. I wanted to examine something that gets much less attention: what happens to expertise, responsibility, ambition and trust when the work around which they developed starts to disappear.

I suspect executives and people earlier in their careers will read the same story quite differently. If you’ve led a transformation, I’d like to know whether Maya’s choices ring true. If you’re earlier in your career, I’d like to know whether Leah’s do. Reply and tell me where you disagree, or send this to someone on the other side of your org chart and compare notes.

The Corner Case: Where Human Value Moves When AI Does the Easy Work is now available on Amazon.

Everyone seems to agree on the same story: agents write the code, and the tools we already use stick around. Jira orchestrates the agents, GitHub verifies their work, Terraform enforces policy.

I buy the first half. The second half assumes that because a function survives, the vendor performing it keeps getting paid. That doesn’t follow. Teams will keep needing coordination, version control and infrastructure management. Whether they keep paying today’s prices for them is a separate question.

Start with Jira. It was built to organize human work, and it’s priced that way, by the seat. If an agent finds a bug, fixes it, runs the tests and prepares the release, a ticket may still exist, but fewer people touch it. The workflow survives, but the relationship between work performed and seats sold starts to break down.

Atlassian sees this. Rovo now meters AI activity and access to organizational context through credits, with additional usage billing scheduled to begin in December 2026. GitHub is moving toward a mix of seats and usage too. That’s adaptation, not collapse.

I still think Jira’s traditional economics are more exposed than GitHub’s, and the reason is margin. A traditional software seat costs relatively little to serve. Agent usage carries inference costs that someone has to pay. Usage revenue can replace seat revenue and still leave a worse business, so margin is the number to watch, not just revenue growth.

The best argument against me is that every agent becomes a heavy consumer of Atlassian’s organizational context. If that consumption carries attractive margins, AI could strengthen Jira. My bet is that inference-heavy usage will struggle to match the margins of traditional seats. Atlassian could prove me wrong if its context services command enough pricing power to offset those costs.

GitHub sits closer to the artifact. Repositories, history and provenance matter whoever writes the code. Its problem is different: passing tests isn’t the same as proving the change is correct.

An agent can write the code and the tests and still be wrong about the requirement. A second agent reviewing the work may share the first one’s assumptions.

Imagine a claims system. An agent finds that removing a validation step speeds processing by 35%. The tests pass and the security scan is clean, but that validation enforced an underwriting rule. The agent saw an inefficiency where the business had a constraint.

More tests derived from the same incomplete specification won’t fix that. The underwriting constraint has to be represented independently of the agent’s optimization objective.

Infrastructure has a version of the same problem. Terraform was declarative long before AI. What changes is volume. Ten changes that each pass policy can still add up to a capacity or resilience problem no single check sees.

I expect basic configuration generation and routine policy checks to lose differentiation, with pricing power moving toward whoever manages infrastructure state and cross-system consequences. More agents could also mean more managed resources, which helps incumbents. I’m predicting where pricing power concentrates, not who disappears.

In Migrating Scarcity, I argued that when something scarce gets cheap, the constraint moves to whatever it depended on.

Implementation was the scarce thing in software. As it gets cheaper, deciding what’s worth building and which risks to accept become relatively more important. Some of that work will be automated too.

Authority is the part I think holds, because someone has to answer for it. Regulators, insurers and boards can ask who allowed a consequential change. An agent can enforce a policy and propose a new one, but it shouldn’t be able to give itself permission to remove the constraint it was meant to respect.

Permission to execute is not permission to redefine success.

Scarce isn’t the same as profitable, though. Companies can meet accountability obligations through internal policies, contracts and existing controls. Audit logs, identity systems and attestations already cover part of the problem.

My bet is that a market still emerges around enforcing authority across systems. A repository can prove what changed. An infrastructure tool can prove what was deployed. Neither necessarily knows whether the change broke a business rule maintained in a third system.

I’d guess the winners are platforms that already control permissions: identity and security vendors, and GitHub where authority attaches to code. Atlassian has valuable context, but I suspect context will be easier to bundle than control. Cloud platforms are the wild card.

I’m wrong if agent consumption of Atlassian’s context grows enough to offset lost seats at attractive margins. I’m wrong if companies can delegate consequential decisions through existing automated policies without paying for another governance layer. And I’m wrong about the business, though not necessarily the constraint, if incumbents bundle all of this and charge nothing extra.

One more worry. If agents do the routine work, how do junior engineers build the judgment to own the big decisions later? Apprenticeship meant doing ordinary work first. A company could ship far more software while gradually losing the ability to govern it.

That would be a strange productivity victory. So the question isn’t which tools survive. It’s which constraints stay scarce enough that companies will pay to manage them.