When AI Slows You Down

The Illusion of Speed

I used a paid AI transcription tool and produced a Vietnamese text incredibly fast from the audio. I then fed it into a paid AI translation model, which converted everything into English in seconds. It felt like magic until I started working with the output.

What I used to do before AI was a single, seamless workflow: listen carefully to the recording, grasp the meaning and context, and render it directly into natural English.

Now, that process has fragmented into a series of fragile steps:

1. The AI generates a transcription, which often struggles with names, technical terms, slang, fast or unclear speech, regional accents, overlapping voices, or background noise.
2. I spend considerable time correcting the transcript, knowing that any error here will cascade into bigger problems later.
3. The AI translates the revised Vietnamese text into English.
4. I review the English version for accuracy, natural flow, tone, cultural nuance, and consistency.

I understand that results vary significantly depending on the tool, model version, audio quality, and speaking conditions. Leading automatic speech recognition systems in 2025–2026 can achieve low word error rates with clean, studio-quality speech in high-resource settings. However, performance typically degrades with conversational Vietnamese.

The challenges are especially pronounced when dealing with regional accents, tonal variation, casual slang, idiomatic expressions, overlapping speech, or background noise — conditions that are common in real-world recordings. In such contexts, error rates rise noticeably, and the amount of post-editing required increases accordingly.

Vietnamese, as a tonal Austroasiatic language with relatively lower training data density than English or other major European languages, still shows a significant gap between automated output and professional human transcription and translation quality. This gap is not just anecdotal. A 2025 study published in JAMA Network Open found that AI-generated translations of discharge instructions into Vietnamese were consistently inferior to professional human translations in fluency, adequacy, meaning preservation, and error severity.

Consequently, by the time I finished reviewing, correcting, and rewriting, I had often spent more total time than if I had simply listened to the audio once or twice and translated it directly, while the speaker’s intent was still fresh in my mind.

From Creator to Tireless Reviewer

Before AI, my work felt simpler and, in many ways, more enjoyable. I would listen to the recording, absorb the speaker’s intent, tone, and message, then craft an English version that felt right. The process had a natural rhythm, with moments of genuine flow.

With AI, that role has quietly shifted. I’ve become, in effect, a full-time quality inspector. Every sentence demands constant judgment: Did the AI understand the context correctly? Does this English sentence truly convey what the speaker meant? Is the tone appropriate for the audience? Have any cultural references been lost or distorted?

The problem is that AI-generated text often sounds fluent and confident. And that is precisely what makes it dangerous. Errors no longer announce themselves; they hide in plain sight. I find myself reading every line more carefully than I would a colleague’s draft; the model’s understanding of spoken Vietnamese simply can’t match my own ears and bilingual intuition.

This kind of work is mentally exhausting. It reflects what research has begun to show: decision fatigue sets in much faster during tasks that require constant reviewing and correcting than during generative, creative work. A study by Syed Md Faisal Ali Khan and Salem Suhluli explores exactly these cognitive challenges in generative AI (GenAI)-assisted tasks, highlighting how sustained verification increases mental load and fatigue. Creation invites flow; continuous verification drains it.

After just a few hours, even small decisions start to feel heavy. My brain feels fried, despite the quiet irony that AI was supposed to save me time. That said, some research on thoughtful AI integration suggests it can reduce overall cognitive load in certain repetitive scenarios — though my experience with nuanced audio work leans heavily toward the draining side.

The Unpredictability Headache

Another major frustration was AI’s unpredictability. The same 60-minute audio file could yield slightly different transcriptions or translations depending on when or how I ran it, often influenced by factors like temperature settings or the specific model version. A sentence that sounded natural in one attempt might come back awkward or subtly wrong the next. There was never a clear explanation, no error log, no transparent reasoning. Just the opaque, probabilistic nature of the system, which forced me to stay constantly on guard. More than once, I fell into the familiar “just one more prompt” trap: adding extra context, tweaking instructions, even switching between models. Each iteration felt like it might finally produce the “perfect” output. But it rarely did.

Looking back, I often realized I could have translated those same sections manually in a fraction of the time. What appeared to be a shortcut kept turning into a detour — one that was not only inefficient but quietly draining.