The Algorithmic Cut is a CILECT Vision & Innovation Grant research project led by Andreas Treske at Bilkent University’s Department of Communication and Design. The project investigates how AI is reshaping the craft of film editing and what it means to remain an author in an automated workflow. The essay below is one of the project’s first published findings.

How AI Is Quietly Rewriting the Language of Film (And How Editors Can Fight Back)

Open a new project in Premiere, Resolve, or Final Cut and the work has already begun. Audio is synced, faces are tagged, dialogue is transcribed. Add a third-party plugin and your rushes have been scored before you’ve watched them.

This is the Paradox of Skill: the faster the tools get, the less we practice the structural thinking that makes an editor. We drift from architects of time to curators of algorithmic probabilities.

Here are six ideas to push back.

1. Curator or architect?

Montage was the deliberate collision of Shot A and Shot B to make a new meaning. Prompting means choosing among suggestions. For the Algorithmic Native, who has never edited without AI, speed feels normal and friction feels like a bug.

Speed without comprehension is efficiency without authorship.

2. “Noise” is where the human truth lives

Plugins such as Gling or EditBuddy give clips a confidence score and penalize soft focus, shake, and off-center framing. That discards what Barthes called the punctum: the flawed, unexpected detail that moves us.

Try the Inversion Exercise

Sort your rushes by hand and note which clips you think an algorithm would reject, and why.
Cut a two-minute sequence using only those clips.
Optionally, run a scoring plugin and compare its verdict with your intent.

3. The silence problem

Transcript-based editing (Descript, text editing in Premiere or Resolve) treats film as language. It can’t see what Walter Murch called the breath and the blink: the pauses, glances, and held silences that carry a scene. Wiseman’s observational work could never be cut from a transcript.

4. The invisible Recommendation Editor

Auto Reframe, Smart Reframe, and Smart Conform crop widescreen to vertical by following faces. In doing so they erase scale, empty space, and the tension between people.

Platforms add another layer: concealed loops are built to inflate retention, while revealed loops can be used deliberately. Discovery algorithms decide which cuts get seen, so they co-author your edit.

5. “And then” versus “therefore”

Retention-optimized pacing favors “and then”: A happens, then B. The essay-film tradition, from Eisenstein onward, uses “therefore”: two images collide to make an argument. Algorithmic defaults smooth that collision away.

6. The Answering Cut

Show the person in the footage two versions: one shaped by automation, one cut by a human. Their response isn’t a vote or a veto. It shows where meaning entered the image, and sometimes a blind spot that began at the camera. Then choose a consequence:

Keep · Re-edit · Reframe · Return to footage · Shoot a new image

Becoming the director of the algorithm

You don’t have to reject AI. You have to build structural literacy: knowing how software handles time and what it assumes by default.

Start analog. Use paper edits, index cards, and a written thesis before opening your NLE.
Look for the punctum. Keep the hesitations and imperfections machines filter out.
Trace every element across Souriau’s levels: profilmic (what existed before the lens), filmic (what montage constructs), and synthesized (what AI generated).

Next time you cut, ask: am I the architect of meaning, or just approving the algorithm’s guess?