The “Just Use ChatGPT” Problem
LLMs speed up writing and translation, but in new markets and regulated work, confidence can replace verification and risk scales quietly.
When I say I work with language and translation, the response is the same:
“Really? We don’t need language people anymore. We just use ChatGPT.”
AI writes fast. AI translates instantly. The output looks fluent.
But that confidence hides a condition:
ChatGPT works best when you already know enough to spot when it is wrong.
Why this is not an anti-AI argument
Language models are useful. If you are fluent in the target language, you can draft or translate, then review it. In that mode, AI removes friction and human judgement keeps the output aligned with your intent.
The trouble starts when judgement disappears.
Known unknowns vs blind spots
There are two ways people use AI for language.
Known unknowns are visible gaps. You know you need help, so you ask for it:
- Summarise this document
- Rewrite this email
- Combine and shorten this slide deck
You can still judge whether the result matches the goal.
Blind spots are different. They sit in places you do not know to question:
- Languages you don’t speak,
- formality and tone choices, plus
- cultural cues, that change how you come across.
These do not show up as errors. The text is smooth. The model is confident. So it ships.
Why it breaks outside your home market
In your own language, you have a “safety net”. Even without training, you can often feel when something is off.
In a language you do not speak, that safety net vanishes. Fluency becomes a mask. Confidence becomes the only signal you have.
That is why “just use ChatGPT” works right up to the moment you enter a new market. After that, you are no longer verifying quality. You are approving by default.
Scale turns small misses into real risk
Language models rarely fail loudly. They fail quietly, and they increase volume, which increases unnoticed mistakes.
Marketing teams are a pressure point.
They are being pushed to use AI for everything:
- ads
- landing pages
- emails
- sales decks
- copy
- localisation
The output is public and brand-critical. If the only quality control is “it sounds fine”, you are one confident mistake away from a campaign that reads wrong, lands wrong, or erodes trust.
Then comes the part most teams ignore: data.
Once people paste customer messages, contracts, or personal data into the wrong AI setup, language risk becomes governance risk.
If you cannot control where data goes or what gets stored, you do not have control. GDPR does not care that you were “just translating an email”, and EU regulators are not impressed by good intentions.
What to do instead
- Do not ban AI
- Build a workflow
- Use models where you can audit the output; add review gates when you cannot
- Treat new markets and regulated content as high risk by default
- Keep sensitive data inside controlled systems with
- access controls
- logging, and
- retention
The goal is simple: speed where mistakes are cheap, and structure where mistakes are expensive. Use orchestration, multiple engines plus checks, rather than one confident answer.
This is the gap Exfluency is built for. Translate everything from a single email to an entire website quickly inside a governed platform. When risk is higher, our linguists and subject matter experts can enhance and trust mine critical content so terminology stays stable and decisions stay defensible.
The real question
The real question is not “can ChatGPT translate this”.
It is “who can tell me when it is wrong, and what happens if we never notice?”
Want to read more about the risks of putting blind trust in general language models? Read more
Want to pressure-test your current workflow?
Contact us and tell us which languages and content types you’re scaling.