12 January 2026
When post-editors become the cleanup crew
AI has now secured its place in translation workflows. That debate is over. What remains unresolved is a quieter, more uncomfortable issue: what happens to the humans who are asked to “just fix” what AI produces.
Post-editing, often labelled “Light PE” is seen as a quick task requiring little effort. After all, post-editors are expected to find serious errors (“misleading the reader”): mis-interpretations, factual mistakes, omissions and additions (aka. hallucinations). And make sure client-specific terminology and phrases are in place.
AI delivers speed. Humans ensure quality. Everyone wins.
That’s the theory.
In practice, the reality is messier.
Across the industry, post-editors are increasingly being handed raw or semi-polished AI output and asked to transform it into fluent, brand-aligned, culturally appropriate content under tight deadlines and shrinking budgets. The assumption is that any AI pre-translation will make the whole process faster and considerably cheaper than translating from scratch.
Very often, it isn’t.
The cognitive cost nobody accounts for
Editing poor or mediocre text is not a neutral activity. It is mentally taxing in a very specific way.
When a linguist writes from scratch, they make a coherent series of decisions: terminology, tone and register, logical cohesion, sentence structure, emphasis. The text grows organically. There is momentum.
Post-editing breaks that flow. Every sentence already exists. Every word carries the weight of someone else’s decision. The linguist must constantly decide whether to leave it alone, tweak it, or delete it. This creates what psychologists call decision fatigue, amplified by frustration.
Many experienced translators will confirm: correcting bad AI output drains more mental energy than producing a clean first draft with proper tools and context.
Humans dislike undoing and unravelling work they would rather did not exist in the first place. Because it is less than a starting point, it’s a hindrance.
Anchoring makes bad text sticky
There is also a well-documented cognitive bias at play: anchoring.
Once a sentence is on the screen, even an incorrect one, it subtly constrains thinking. Reviewers, editors, and yes, translators themselves tend to gravitate toward what’s already there. Clearing the deck mentally takes deliberate effort and often leads to far more extensive rewrites than originally planned.
This is why starting “from scratch” can feel liberating and fast, while post-editing can feel slow and oppressive, even when the word count you can show at the end of the day is lower.
“Light post-editing” is rarely light
The term “light post-editing” sounds precise. It isn’t.
It assumes that:
In untrained AI-to-AI workflows, those assumptions often collapse.
Instead, post-editors find themselves:
At that point, the task is no longer post-editing. It is full rewriting, just without the authority, time, or compensation normally attached to that level of work.
What we end up with is ghost-writing under time and financial constraints.
The psychological toll of being a permanent fixer
Most professional linguists are not anti-AI. Quite the opposite. Many already use AI extensively, often more effectively than their clients do. They have learnt how to:
What erodes morale is being reduced to a cleanup function for automation that ignores all of that upstream.
Being treated as the person who “makes it acceptable at the end” rather than the expert who could have shaped it correctly from the start creates unease and resentment. Not loud resentment. Quiet, corrosive resentment.
Forcing experts to repair flawed AI output can add time rather than save it. Instead of producing one good text, they end up fixing two imperfect ones: the original AI-generated source and its AI-generated translation.
Over time, that affects quality, goodwill, and retention. Many ultimately decide to step away from a profession they no longer enjoy. The word is getting out into the world, not least the universities offering translation studies.
This is the paradox at the heart of many AI workflows: apparent speed at the start creates drag at the end.
This is not an argument against AI
It matters to say this clearly.
The problem is not AI. The problem is where AI is inserted into the workflow and who controls it.
There is a profound difference between:
Conflating the two under the banner of “efficiency” is how well-intentioned projects fail.
What works better in practice
Some organizations are already adjusting, quietly and pragmatically.
The real efficiency question
The uncomfortable truth is this: forcing expert linguists to clean up low-effort AI is not efficiency. It is cost displacement.
It shifts effort downstream, hides it under the label of post-editing, and slowly degrades both quality, trust, and professional pride.
AI is an extraordinarily powerful tool. But like every powerful tool, it works best when guided by people who know what they are doing.
The choice is not between humans and AI. It is between using AI intelligently and creatively, or paying humans later to fix everything that’s gone wrong.
The future of translation will not be decided by how much content can be pushed through a pipeline, but by how intelligently that pipeline is designed.
AI will play a central role. So will humans. The organisations that understand how the two reinforce each other will not only move faster. They will move better
About the author
Isabelle Weiss, founder of Alpha CRC, has been a leading voice in the translation industry for almost forty years. Weiss has been a consistent presence in the company since our beginnings in 1987, and continues to work directly with translations for clients.
