I’ve been thinking about this for a while, especially with all the discussions around AI replacing jobs.
One thing that feels consistently misunderstood:
AI doesn’t improve the quality of decisions by itself. It increases the speed at which existing decision logic operates.
That has a simple consequence:
Good systems become better. Weak systems fail faster.
But there’s another layer that is often ignored.
Right now, many companies are reacting to AI by reducing headcount. Some of that is rational:
- there is real slack in certain roles
- some work can already be automated or simplified
In those cases, AI acts as a kind of cleanup mechanism.
But this is where it gets more complex.
If companies reduce people too quickly, they don’t just cut cost — they also remove:
- domain knowledge
- informal networks
- context that is not documented anywhere
This kind of knowledge is not easily replaced by AI.
So you end up with a paradox:
AI increases speed, but the organization loses the very knowledge needed to make good decisions at that speed.
At the same time, layoffs are not always a signal of weak systems.
Strong organizations can also reduce roles because they:
- increase productivity per employee
- reallocate work
- shift toward new capabilities
The difference is what happens next.
Some organizations use AI to scale and create new opportunities. Others mainly use it to cut cost because they lack the structure to turn speed into growth.
So instead of asking:
“Will AI replace jobs?”
A more relevant question might be:
Is the organization structured in a way that can actually benefit from faster decision-making?
Because if not, AI won’t make it smarter. It will just make it faster at being wrong.
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