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.
