Much of the discussion about AI and software development focuses on whether AI will replace developers.
A different risk may be emerging.
Organizations may use AI to automate precisely the work through which people traditionally developed the understanding required to become senior.
Senior developers did not enter organizations as senior developers. Their expertise accumulated over time.
They implemented small changes. They investigated defects. They read unfamiliar code. They made mistakes. They observed production failures. They asked why something had been designed in a particular way. They discovered that apparently strange implementations sometimes existed for good reasons — and sometimes for reasons that had stopped being good years earlier.
Through repeated exposure, they acquired more than technical skill.
They acquired context.
They learned not only what the system does, but increasingly why it became the way it is.
The Efficiency Paradox
AI can remove substantial amounts of routine work from software development. That can be valuable.
A junior developer may no longer need to spend hours writing relatively straightforward implementation code, searching documentation, constructing tests, investigating logs or performing other work that an AI system can complete much faster. Measured as delivery efficiency, this looks like progress.
But some apparently inefficient work may have performed another function:
It created experienced people.
The work product was not its only output.
Learning was another.
If we automate the activity while measuring only whether the immediate deliverable can still be produced, we may overlook the organizational capability that the activity was helping to create.
Work Has More Than One Outcome
This suggests a more general distinction.
An activity may produce an explicit outcome and one or more implicit outcomes.
For example:
Explicit outcome:
– A developer fixes a defect.
Implicit outcomes:
– The developer learns how the system behaves.
– They discover relationships between components.
– They understand why a particular design decision matters.
– They encounter a business rule that was previously invisible to them.
– They become better able to recognize similar problems in the future.
An AI system may produce the explicit outcome extremely efficiently while reducing some of the implicit outcomes.
The defect still gets fixed. But who learned from fixing it?
The Succession Problem
This becomes particularly important when experienced people leave.
Organizations already struggle to preserve understanding across personnel changes. Documentation may preserve requirements, architecture, code and decisions while much of the reasoning connecting them remains in people’s heads.
AI could introduce a second continuity problem. It may not only change how existing knowledge is preserved.
It may change how new people acquire enough understanding to become future custodians of that knowledge.
If today’s senior developers retire or leave, organizations cannot simply replace them with today’s juniors plus more capable AI.
The question is whether those juniors have had opportunities to develop the contextual and causal understanding that made the departing developers senior in the first place. This is therefore not merely a staffing pipeline problem.
It is a succession of understanding problem.
Expertise Is More Than Accumulated Information
It would be tempting to solve this by giving future developers better access to documentation and AI-generated explanations. That will help, but it may not be sufficient.
Experienced practitioners possess more facts, but they also have developed judgment.
They recognize when something looks suspicious before they can necessarily explain exactly why. They know which questions to ask. They recognize recurring causal patterns. They understand where apparently local changes may have wider consequences. Much of that ability was developed through interaction with real problems.
The challenge is therefore not simply to transfer everything a senior developer knows to a junior developer.
It is also to preserve opportunities through which less experienced people can develop their own understanding and judgment.
AI Changes the Learning Path
This does not imply that organizations should preserve inefficient manual work simply because previous generations learned through it. That would confuse the practice with the function it served.
The important question is instead:
Which learning functions did the old way of working provide, and how will those functions be preserved when AI changes the work?
Perhaps some learning can happen faster with AI. AI can explain unfamiliar code, expose dependencies, challenge assumptions, simulate alternatives and make organizational knowledge easier to interrogate.
Used deliberately, it may accelerate the development of understanding rather than diminish it.
But that requires designing AI-supported work for more than immediate productivity.
The objective cannot only be:
How can AI help this developer produce the result faster?
It must also include:
How does this developer become capable of understanding, questioning and eventually owning the results?
Implication for Continuity of Understanding
Continuity of understanding therefore has at least two dimensions.
The first is preservation:
Can the organization retain enough of its existing understanding when people leave, systems change and structures evolve?
The second is regeneration:
Can new people develop enough understanding to extend, challenge and eventually replace the understanding held by today’s experts?
A reference model can help with the first by connecting fragmented knowledge and preserving relationships, rationale and intent.
But it may also support the second. If important relationships between intent, requirements, decisions, implementation, evidence and outcomes are made accessible, a less experienced person does not have to reconstruct all of them accidentally over years of work.
They can explore them deliberately.
AI may make that exploration dramatically easier.
The opportunity is therefore larger than preserving the old apprenticeship model.
It is to create a better one.
Working Proposition
AI should not only increase the productivity of today’s experts. It should help create tomorrow’s experts.
When evaluating AI-supported ways of working, organizations should therefore assess not only:
- whether work is completed faster,
- whether fewer people are required,
- whether output quality remains acceptable,
but also:
- whether practitioners continue to develop contextual understanding,
- whether reasoning and rationale remain accessible,
- whether people learn from the work AI performs with them,
- whether judgment and ownership continue to develop,
- and whether the organization is creating the people who will be capable of carrying its understanding forward.
Otherwise, AI may solve today’s capacity problem while quietly creating tomorrow’s capability problem.
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