How is AI Impacting Human Intellect
Lessons from language translation industry
[Guest special by Vivekananda Pani, cofounder of Reverie, a language platform which was acquired by Jio. Vivek shares a candid perspective on impact of AI on language translation industry - a must read if you are trying to understand the evolving nature of roles in AI world]
The adoption of artificial intelligence (AI) for intellectual activities mirrors, in many ways, the historical replacement of human physical labor by machines. When industrial machinery replaced manual labor, societies adapted by reorganizing work, productivity, and even daily life.
One unintended consequence was a shift toward sedentary lifestyles, requiring deliberate compensatory practices such as exercise and fitness regimes to replace what was once organically embedded in work.
A similar transformation is now underway for cognitive labour. As AI systems increasingly perform tasks that once required human judgment, creativity, and expertise, humans are being repositioned, not always upward, but within the value chain.
This article examines these dynamics through a concrete example from the language technologies and translation industry, highlighting economic and qualitative consequences of AI adoption in the linguistic space.
The Transition from Manual to AI-Driven Machine Translation
Operating in the Language Technologies domain, we were repeatedly asked by our clients to support localisation, translating existing business content to enable shared understanding across languages. We initially approached this cautiously, assisting with limited volumes of content rather than offering translation as a primary service. Over time, these requests led us to design a product explicitly aimed at augmenting professional translators, not replacing them.
We developed an automated machine translation engine and invested heavily in training models to steadily improve linguistic accuracy, consistency, and domain relevance. As MT (machine translation) quality improved, human translators began relying heavily on machine-generated outputs. Productivity gains were immediate and substantial:
A skilled translator who previously translated 1,000–1,500 words per day without MT support could now translate two to three times as much. Prior to MT adoption, professional translators earned approximately ₹2 per word (conservatively).
Yet this efficiency masked a deeper structural shift in the profession. As automation became embedded in workflows, the translator’s role subtly changed, from linguistic author to post-editor of machine output. What emerged was not merely a story of efficiency, but a redefinition of value, where increased throughput did not translate into greater economic or professional recognition for skill developed over years.
At first glance, this appears to be a textbook case of productivity enhancement through automation. However, the downstream effects were far more complex.
The Hidden Tax of Automation
1. Economic Consequences: Productivity Without Prosperity
As machine translation accuracy improved, organizations began adopting MT at scale, mainly for content that was non-critical but necessary for basic comprehension. Access to translations was no longer limited to finding a translator. For content that still required human involvement:
Turnaround times dropped dramatically.
Supply of translation capacity increased.
Market prices fell sharply.
Today, many translators struggle to earn even ₹0.50 per word. There is a rush of translators willing to work for as low as ₹0.20 per word with support of Machine Translation. Despite higher individual throughput, overall income declined, as value shifted from human labor to AI infrastructure providers (cloud platforms, MT APIs, and model vendors). This reflects a broader pattern seen across AI-mediated industries: efficiency gains accrue to capital and platforms, not labor.
Quality Paradox: When Skill Loses Its Market Value
The quality of a translator is usually judged by the refinement of language and vocabulary they produce. A bad translator is not someone who gives out the wrong meaning but one who doesn’t use the most appropriate vocabulary. Machine translation systems tend to produce:
Consistent vocabulary
Grammatically acceptable structures
Domain-neutral phrasing
For less-skilled translators, the task shifted from translation to error spotting, correcting only obvious mistakes. Ironically, this narrowed the skill gap between weak and strong translators. For highly skilled translators, the situation worsened:
Their nuanced language choices were no longer demanded.
Clients valued speed and cost over stylistic or cultural precision.
They were forced to compete with less-skilled peers on throughput alone.
As a result, quality became economically invisible, even though it still mattered linguistically.
Cognitive Effects: Passive Acceptance and Loss of Originality
Machine-generated translations reinforce trust in the system and gradually create a habit of passive acceptance. What becomes evident over time is not a sudden failure, but a slow behavioral drift.
The reliance on machine output leads even experienced translators to overlook errors. Over time, this leads to erosion of vigilance resulting in outcomes that are paradoxically more dangerous with:
Seasoned experts inadvertently approving absolute blunders.
Well-formed, grammatically polished sentences that introduce catastrophic shifts in meaning.
Errors that are harder to detect precisely because they appear linguistically refined.
Ironically, a not so skilled translator working without machine support might have produced awkward phrasing or minor grammatical issues, but would rarely alter meaning as MT does.
For highly skilled and experienced translators the act of translating gradually turns into scanning and approving, their edits become fewer and less substantial.
Most translators agree that high-quality MT systems deliver 75–80% acceptable output. In practice, this means in 80 out of 100 cases, translators read the source text, glance at the MT output, and accept it. If the MT output is not obviously wrong, it is rarely re-imagined or re-expressed. If the machine output were absent, translators would likely produce a different, and often better translation.
Future AI Training Impact
One of the least visible but most critical effects appears in future AI training. Since humans accept most machine outputs unchanged, less genuinely human-generated data is produced.
The remaining edits are often minor or stylistic, reinforcing existing machine patterns. Over time training data becomes increasingly machine-biased. AI systems “learn” that their own outputs are more reliable than human input. Future models may override or discount human feedback altogether.
The Skills We Trade for Speed
What we are witnessing is not just economic or cognitive displacement, but a loss of skill itself. Experienced translation professionals, who entered the field well before machine translation and AI tools and adopted these systems out of necessity to remain relevant and economically viable.
Yet, when they step away from automation, something remarkable happens: after an initial period of discomfort, often measured in days or weeks, they recover their former fluency, speed, and judgment. Their skills were compressed, not erased.
The newer generation of translators who have spent the last five to eight years working almost exclusively with AI assistance, often find themselves genuinely incapacitated without it.
This is the clearest signal yet that we are not merely changing tools; we are reshaping how expertise is formed, retained, and lost.
Recognizing this, we have fundamentally rethought our approach, because building systems that make humans faster is no longer enough. The real responsibility is to build systems that ensure humans remain capable.
The true measure of AI’s success will not be how efficiently it replaces human effort, but how well it preserves human skill when the machine is turned off.
What’s your take?