Why Efficiency Thinking Is the Last Great Industrial Myth
What happens when optimization scales faster than meaning
Efficiency is one of the most successful ideas ever invented.
It transformed agriculture.
It powered the industrial revolution.
It scaled manufacturing.
It made cost visible and progress measurable.
And today, it’s quietly exhausting organizations.
Not because efficiency is wrong —
but because it is being applied to work it was never designed to govern.
Efficiency assumes stable work
Industrial efficiency rests on a few core assumptions:
Tasks can be decomposed
Variation can be minimized
Decisions can be standardized
Outputs can be optimized independently
Context changes slowly
When those assumptions hold, efficiency works beautifully.
But most knowledge work violates every single one of them.
Meaning shifts.
Exceptions multiply.
Coordination dominates execution.
Interpretation becomes the bottleneck.
Efficiency thinking doesn’t disappear in these environments.
It mutates.
Software removed the natural limits
For decades, efficiency was constrained by friction:
Physical processes
Human memory
Organizational resistance
The cost of change
Software removed those constraints.
Now we can:
Encode decisions endlessly
Automate exceptions instead of revisiting intent
Add configuration instead of reducing ambiguity
Optimize locally while fragmenting globally
What looks like progress is often efficiency logic outrunning human sense-making capacity.
This is not a tooling failure.
It’s an intellectual overextension.
Efficiency creates the illusion of control
Dashboards reassure.
Automation soothes.
Configuration feels like mastery.
But efficiency tools rarely remove work.
They redistribute it.
From systems → to people
From execution → to interpretation
From visible effort → to cognitive load
Nothing breaks.
Everything works.
Mostly.
And that is precisely why the damage persists.
The mercenaries of complexity thrive here
A growing segment of the IT industry profits from this gap.
They don’t sell outcomes.
They sell optionality.
Their promise is always the same:
“You can customize this”
“It adapts to your workflow”
“It scales with your complexity”
What they never price is the interpretive burden those options impose.
So complexity is exported downstream.
To teams.
To managers.
To operators.
To humans.
The vendor books revenue.
The organization absorbs the cost.
Efficiency didn’t fail — it was overapplied
Efficiency is not evil.
It’s domain-specific.
It works where:
Work is repeatable
Variation is undesirable
Context is stable
Meaning is fixed
It fails where:
Work is interpretive
Variation is meaningful
Context shifts
Meaning must be negotiated
Applying industrial efficiency logic to interpretive work doesn’t make it faster.
It makes it fragile.
The real constraint is not speed — it’s coherence
Organizations don’t stall because data moves too slowly.
They stall because:
Meaning diverges
Context fragments
Decisions lose lineage
No one remembers why things are the way they are
Efficiency optimizes execution.
It does not preserve understanding.
Once understanding erodes, optimization only accelerates confusion.
A different management question
The question is no longer:
“How do we make this faster or cheaper?”
It is:
“How do we reduce the cognitive effort required to make this make sense?”
That question leads somewhere very different.
The uncomfortable conclusion
Efficiency is no longer the constraint.
Human interpretation is.
Until organizations manage that deliberately,
every efficiency gain will quietly increase cognitive load.
Not because people are resistant.
But because meaning does not scale the way execution does.