I learned something useful from performance testing many years ago. When you remove a bottleneck from a system, another bottleneck usually appears.
That does not mean the optimization failed. The performance of the whole system has increased. The next constraint simply became visible because the previous one no longer dominated.
I wonder if this is a useful way to think about AI and knowledge work.
Much of knowledge work is built around expensive activities: finding information, reading it, comparing it, writing, analyzing, programming, documenting, communicating and reviewing. Organizations have developed roles, processes, queues and handovers around those constraints.
AI is now reducing the cost of several of those activities at once. The obvious conclusion is that fewer people will be needed. That may be true in some cases. But another possibility is that the bottleneck simply moves.
If software implementation becomes dramatically faster, development capacity may stop being the main constraint. Then other constraints become more visible: deciding what is worth building, understanding customer needs, integrating changes, assuring quality, absorbing change in operations, or learning from what actually happens after release.
The system may still become much faster overall. That matters because a role is not the same thing as the activities currently associated with it. A Product Manager may spend a lot of time managing a backlog because implementation capacity is scarce. A technical writer may spend a lot of time producing documents because creating and maintaining representations is expensive. A Quality Manager may spend a lot of time collecting and summarizing evidence because that work is slow and fragmented.
If AI removes those frictions, the activity may shrink without the underlying function disappearing.
That raises a more interesting question:
If AI can perform many of the activities associated with a profession, were those activities actually the function of the profession, or merely the mechanisms through which that function happened to be realized?
For a Product Manager, the deeper function may be understanding context and deciding what should change and why. For a technical writer, it may be preserving and making technical understanding available. For a Quality Manager, it may be maintaining enough understanding of quality, risk and evidence for the organization to make informed decisions. AI may therefore push roles upstream toward their underlying purpose.
There is also another effect: when something becomes cheaper, we often use more of it.
If software becomes cheaper to build, more software may become economically viable. If analysis becomes cheap, questions that were never worth investigating may suddenly be worth answering. If quality evidence can be gathered continuously, assurance may expand rather than simply become cheaper.
So the future may not be today’s amount of knowledge work performed by fewer people.
It may be far more knowledge work being performed, with different constraints determining where human attention is most valuable.
And that is where the performance-testing analogy becomes useful again.
The next bottleneck might be judgment. Or trusted evidence. Or customer attention. Or organizational ability to absorb change. Or something AI will also reduce later.
The important thing is that removing one constraint does not tell us that the system is finished. It tells us where to look next.
So perhaps the more useful question is not:
Will AI replace the knowledge worker?
But:
Which bottlenecks currently shape this role, what happens when AI removes them, and what function remains when the old mechanisms are no longer necessary?
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