AI Image Culling vs. Traditional Photo Culling

Traditional photo culling and AI image culling both solve the same surface-level problem: too many images. the difference is what you're actually trying to remove.

A photographer might come back from a shoot with 4,000 photographs.

Some decisions are pretty objective.

Missed focus.

Closed eyes.

Bad expressions.

Exposure problems.

Burst duplicates.

Modern photography culling software can help find that stuff, and that's genuinely useful.

Generative culling gets weird in a different way.

You might have 300 campaign images and none of them are technically accidental.

You asked for them.

They're sharp.

Nobody blinked.

The lighting is beautiful.

And somehow 280 of them still have to go.

The questions are different

Now you're asking:

Which composition actually expresses the idea?

Which direction deserves development?

Are these images different enough?

Which ones belong together?

Do I have enough shot diversity?

Can I see the product?

Why did I generate 28 medium portraits and zero close-ups?

What should I generate next?

That's less about finding technical failures and more about understanding the body of work.

Technical quality isn't creative judgment

A technically flawless AI image can be completely forgettable.

A slightly strange one can make the campaign.

I'm not saying technical quality doesn't matter either, obviously it does. I'm saying it can't make the decision for you.

The relationship between the images matters.

Traditional photo culling often asks:

Which captures survived the shoot?

Generative creative culling increasingly asks:

Which possibilities deserve to become the campaign?

Same abundance.

Different edit.

That's the second problem WALLPAPrrr is built around.