For decades, companies competed on execution.
Who could code faster?
Who could design faster?
Who could write faster?
Who could ship faster?
Then AI changed the cost of execution.
AI did not make execution irrelevant. It made many forms of execution faster, cheaper, and easier to access.
A small team can now generate code, produce designs, analyze data, write documentation, and test ideas at a speed that previously required a much larger organization.
That creates a new problem.
When execution becomes abundant, execution alone becomes a weaker competitive advantage.
The Scarce Resource Is Changing
The difficult part is increasingly not doing the work.
It is deciding what work deserves to be done.
Teams still need to answer questions such as:
- Which customer problem is worth solving?
- Which feature should we delay?
- Which market opportunity should we ignore?
- What should we avoid building completely?
- Where should engineering time actually go?
- Which AI-generated output is useful, safe, and commercially relevant?
I have started calling this The Execution Inflation Theory.
What Is the Execution Inflation Theory?
The idea is simple:
As AI increases the supply of execution, the value of judgment increases.
Inflation reduces the purchasing power of money when the supply grows faster than its value.
AI may create a similar effect across knowledge work.
When every company can produce more code, more content, more designs, and more prototypes, producing more is no longer enough.
The advantage shifts toward teams that can make better decisions.
Execution Still Matters
This does not mean execution is unimportant.
A strong strategy without delivery is still useless.
Poor engineering, slow releases, weak quality control, and unreliable operations can still destroy a good product.
But execution may become the baseline rather than the differentiator.
The strongest companies will combine:
- Clear product judgment
- Strong technical judgment
- Fast, reliable execution
AI improves the third part.
It does not automatically solve the first two.
Why This Matters for SaaS Teams
For SaaS companies, AI can accelerate development—but it can also accelerate the wrong roadmap.
A team can now build an unnecessary feature faster than ever.
It can create technical debt faster.
It can launch more experiments without learning anything useful.
It can generate more output while making no meaningful progress.
The central question is no longer:
How quickly can we build this?
It is:
Should we build this at all?
That is a much harder question, and AI cannot answer it without context, customer understanding, business constraints, and human responsibility.
My Current View
In the AI era, execution will remain essential.
But judgment will increasingly determine where that execution goes and whether it creates value.
The competitive advantage may belong to teams that know what to ignore, what to prioritize, and when not to build.
Do you agree?
Will judgment become the main competitive advantage in the AI era, or will execution continue to separate the best teams from everyone else?
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