Why Most Optimizations Can’t Be Proven

When talking about optimization, there’s a tendency to look for a clear truth: this is faster. This is more efficient. This is better.

But in most cases - you can’t actually prove it.

Not because the measurement is wrong, but because the optimization is only correct in a specific context.

Optimization Almost Always Starts as Local

You change a function. Swap an algorithm. Move a computation.

And in the measurement: “it runs faster for me.”

And that can be true - and still say almost nothing about the system as a whole.

The Gap Between Local and Systemic Optimization

The core gap is between local optimization and systemic optimization.

Local optimization:

• Measures a piece of code
• In a specific scenario
• With specific input
• Under specific load

Systemic optimization:

• Is affected by flow
• By queues
• By timing
• And by interactions between components

What looks like a targeted improvement can shift the bottleneck - or even worsen behavior somewhere else.

”It’s Faster for Me” Is a Meaningless Statement

That’s why “it’s faster for me” is a meaningless statement.

Where is “for me”?

• Under what load
• With what input
• And at what time of day

Systems behave differently:

• Under low load vs. high load
• With uniform input vs. edge cases
• During quiet time vs. bursts

The optimization doesn’t change - the context does.

Optimizations Aren’t Absolutely Correct

And here lies the fundamental problem: optimizations aren’t absolutely correct.

They depend on:

• Input pattern
• Usage profile
• Arrival times
• And timing between components

A small change in context can turn a “clear improvement” into a silent regression.

Why It’s Hard to “Prove” an Optimization

You can show:

• One measurement
• One graph
• One scenario

But you can’t show that the improvement will hold:

• Tomorrow
• Under different load
• Or with a different combination of requests

At least not until you truly understand the system.

The Bottom Line

Stable systems don’t look for “proven” optimizations. They look for understood optimizations.

Ones where you know:

• When they help
• When they don’t
• And what they cost in a different context

Because optimization without context isn’t an improvement. It’s a bet.