AI reduces the cost of being wrong

Most discussions around AI focus on efficiency.

How many people can be replaced?

How much faster can work be done?

How much money can be saved?

Those are valid questions.

But I think they miss the bigger shift.

Historically, one of the most difficult parts of running a company was deciding what not to pursue.

Every initiative carried a meaningful cost.

Every experiment consumed time, capital, and attention.

Choosing one opportunity often meant rejecting several others.

As a result, companies became highly selective about where they placed their bets.

AI changes that equation.

A product idea that once required months of work can now be explored in weeks.

A proof of concept that once needed a dedicated team can often be validated by a handful of people.

The most important consequence is not that execution becomes cheaper.

It's that experimentation becomes cheaper.

And when experimentation becomes cheaper, the cost of being wrong falls dramatically.

That changes behaviour.

Companies can test more ideas.

Explore more markets.

Validate more assumptions.

Run more parallel initiatives.

The risk profile shifts.

The conversation around AI often focuses on productivity gains.

I suspect its greatest impact may be on optionality.

The companies that benefit most from AI may not be the ones that cut the most costs.

They may be the ones that use the same budget to place more intelligent bets.

If you had the same budget as last year, but AI doubled your execution capacity, would you:

A) Reduce costs

or

B) Run twice as many experiments?

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From the comments

A related thought:

Historically, a lot of decisions had to be made before enough information was available because testing was expensive.

What's interesting about AI is that it lowers the cost of finding out.

In many cases, we don't need to spend as much time predicting which idea will work.

We can simply test more of them.