A few years back, catching machine translation errors was easy. The output was broken. Word salad, wrong cases, verbs stranded at the wrong end of the sentence, a Russian loanword sitting where a Turkmen one should be. You didn't need to be a linguist to smell it. You needed eyes.
That's over. The output I clean up now reads well. It flows. It has rhythm. And a good chunk of the time, it's still wrong — just quietly, expensively wrong. The failure moved. It used to live in the grammar. Now it hides in the meaning.
The fluency mask
Here's the problem in one line: a broken sentence warns you, a smooth one lulls you.
When an LLM hands me a paragraph of Turkmen that scans cleanly, my guard drops. So does the reviewer's. So does the client's, who can't read a word of it but sees full stops in the right places and assumes the job is basically done. The polish is doing work it hasn't earned. It's convincing you the content is correct because the surface is correct, and those are two entirely different things.
Take oil and gas. I got a passage back from a model recently describing a valve procedure — grammatically spotless Turkmen, natural phrasing, the kind of thing I'd sign off on if I only skimmed. Except it had swapped the sequence of two steps and softened a hard safety instruction into a suggestion. In a manual where someone follows those steps with a wrench in their hand, that's not a style nit. The old broken MT would never have fooled me into missing it, because I'd have been rewriting the whole thing anyway. The fluent version almost slid through.
Legal does the same, differently. The model loves to resolve ambiguity. A contract clause that's deliberately hedged comes back tidy and decisive in Turkmen, because the model's whole training pushes it toward the most probable, cleanest reading. But in a contract, the hedge is the point. Fluency actively works against you there.
Why Turkmen makes it worse
Every language has this problem now. Turkmen has it harder, and it's worth spelling out why, because it changes the risk math for a PM staffing a project.
There's almost no clean training data. The model has seen a lot of English, a lot of Russian, some Turkish. It has seen comparatively little good Turkmen. So when it doesn't know a term — and in software or specialized technical work it often doesn't — it doesn't stop. It confabulates. It builds a plausible-looking Turkmen word out of a Turkish root, or reaches sideways into Russian, or invents a compound that no Turkmen speaker has ever used but that looks like it could exist. And it delivers all of this with the same even, confident tone it uses for the parts it gets right.
That's the trap. In a high-resource language, a confident answer is usually a correct one, because the model has seen the real thing thousands of times. In Turkmen, confidence and correctness have come unglued. The model is equally sure whether it's right or hallucinating, and there's no tremor in the output to tell you which.
Script makes it messier still. Turkmen lives across Latin and Cyrillic, and models will happily mix conventions inside a single document, or transliterate a proper noun three different ways on three different pages — each one perfectly readable on its own.
What this means for the person paying me
So the value of a native human on Turkmen has shifted, and I think the pricing conversation should follow it.
You're not paying me to fix grammar anymore. The machine handles grammar fine. You're paying me for the one thing it can't fake: I know when a fluent sentence is lying. I can read a paragraph that looks finished and feel the wrongness in it — the invented term, the flattened hedge, the reordered step, the register that's technically correct but socially off. That's not a spellcheck function. That's judgment, and it's the exact thing that got harder to spot, not easier.
Which is why flat post-editing rates on Turkmen make me tired. The pitch is always "the machine did most of it, just tidy up." But the machine did the easy part and left the dangerous part looking done. Verifying fluent-but-possibly-wrong output is often slower than verifying obviously-rough output, because I have to distrust every clean sentence individually instead of just rewriting a broken one. Confidence I have to check is more work than mess I can see.
My blunt advice to a PM: stop treating MT quality as a single dial that goes up. Two things went in opposite directions. The average quality went up. The detectability of the remaining errors went down. On a low-resource language especially, that second number is the one that should worry you, because a smooth wrong answer is the kind that ships.
Give me the machine draft, fine. I'll use it. But price the read, not the tidy-up. The tidy-up is trivial now. The read is the whole job.