IA inside
A Translator's Voice: a Position Paper on...
*
AI has made remarkable progress, particularly with the development of Neural Machine Translation (NMT). Large Language Models (LLMs) can now handle huge volumes of text quickly and with a level of accuracy that was unimaginable just a decade ago.
For certain types of content, AI can provide a "good enough" translation instantly and at virtually no cost. I called it The Translational Supremacy. This efficiency has created a new, massive market for automated translation that didn't exist before, catering to a demand for speed—almost free of charge—and accessibility over perfect accuracy.
No way than a human translator can compete with that. But despite this progress, there are many areas where human translators remain essential. AI-powered translation often struggles with cultural nuance, context and style, creativity and adaptability and, above all, specialist fields.
Therefore, the FUTURE of the translation market will likely be a hybrid model, with the emergence of AI as a powerful tool in a "two-tier" market: one for automated, low-cost translation for basic needs, and another for high-value human-led translation for content where accuracy, cultural relevance, technical expertise and style are paramount.
By contrast, the PRESENT of the translation market is in the hands of LSPs (Low Service Providers), who are only tech companies driven by efficiency and profit, not quality… In fact, they are actively pursuing a business model that minimizes or eliminates human translators. For them, AI is just a way to scale their operations and reduce costs. They are increasingly adopting a "no-touch" workflow for projects where quality can be sacrificed for speed and price.
For clients and LSPs the advantages are clear: speed, scalability, and cost. Machines can translate thousands of words in seconds, operate 24/7, and cost a fraction of human labor. Businesses, especially those operating globally, have embraced MT to localize content, streamline customer support, and break into new markets. The global machine translation market was valued at about USD 978 million in 2022 and is forecast to reach USD 2.72 billion by 2030, with a compound annual growth rate of 13.5% (Grand View Research).
The Harsh Realities of the Translation Market in 2025: Beyond the Hybrid Model
The current landscape for many LSPs and translators feels far more precarious. For numerous professionals, the reality isn't seamless partnership but displacement, with roles shrinking to post-editing machine-generated content at rates that can be as low as 25% of traditional tariffs, coupled with aggressive volume demands and tight deadlines. This isn't just anecdotal: a CEPR study published in March 2025 analyzed U.S. labor markets post the 2010 launch of Google Translate's mobile app and found that areas with higher AI adoption saw a notable decline in translator employment—up to a 10-15% drop in roles—and stagnating wages.
The current landscape for many LSPs and translators feels far more precarious. For numerous professionals, the reality isn't seamless partnership but displacement, with roles shrinking to post-editing machine-generated content at rates that can be as low as 25% of traditional tariffs, coupled with aggressive volume demands and tight deadlines. This isn't just anecdotal: a CEPR study published in March 2025 analyzed U.S. labor markets post the 2010 launch of Google Translate's mobile app and found that areas with higher AI adoption saw a notable decline in translator employment—up to a 10-15% drop in roles—and stagnating wages.
This trend has intensified with generative AI, where entry-level and general translation jobs are evaporating as companies opt for "good enough" automated outputs. Industry surveys echo this. The Society of Authors' 2024 poll (with follow-ups into 2025) revealed that over 40% of translators reported income decreases due to AI, with 75% expecting further negative impacts on future earnings. In a recent survey, in particolar, over 70% of respondents reporting decreased work volumes.
On platforms like X (formerly Twitter), translators share stories of zero work in entire months, with one Italian-English specialist noting a shift from 50-60 hours weekly to essentially working zero, or only some work in a very sporadic and unreliable way (Source). Moreover, surviving gigs increasingly involve machine translation post-editing (MTPE).
Currently the Hybrid Model Is Nothing but a Communication Language Element
MTPE is gaining traction every day, with translators refining AI-generated output, ensuring accuracy and cultural relevance while leveraging the speed of automation. But while AI is improving, it’s not infallible; errors in tone or context can still slip through, especially in low-resource languages with limited training data. This is where translators come in as finishers.
A professional translation, like any professional-level product or service, is worthless unless it is 100% finished. And it is the human who ensures that finishing touch, certainly not the machine translation engine, and even less all the EPICs you could imagine, which at best produce bland, impersonal content, and at worst inaccurate texts.
In other words, clients are satisfied with this lower yet sufficient level of quality; indeed, they seem to accept it readily as long as they pay less for each translation. Behind the combination of increased productivity and lower prices, quality is relegated decisively to the background.
The only problem is that the solution marketed as “ideal,” automated 100%, succeeds thanks only—and exclusively—to the finishing work of the translator.
This work is increasingly framed in terms of plain translation, modeled on plain language, in other words a simplified language where the goal is to use, as much as possible, short, clear, familiar words with as few syllables as possible, while avoiding jargon and technical terminology, transforming verbs into nouns or adjectives, so that the message is understood by everyone.
Plain translation—a translation that is “plain” in name only…
Moreover ethical pitfalls abound: overreliance on AI homogenizes language, erases dialects, and risks data breaches in cloud systems. Privacy concerns are rampant, with 2025 regulations like the EU's AI Act mandating human oversight for sensitive content.
So the industry should consider the economic impact on translators and balance technological adoption with fair compensation for human expertise, but it is not doing so. In reality, this widespread underpayment stems from a flawed assumption: that AI handles 70-90% of the work, leaving translators with "easy fixes," but it seems that only translators are aware of the contrary. Many of them believe that the "no translators" scenario is already here.
To navigate this, LSPs must adopt a strategic, ethical, and collaborative approach in their relations with translators, ensuring mutual sustainability and positioning themselves as indispensable partners in a rapidly evolving ecosystem. Below are key behaviors LSPs should embrace, grounded in 2025 industry realities and the looming threat of client disintermediation.
1. Fair Pay and Transparent Pricing
One of the most pressing challenges in today’s translation industry is the growing gap between the real effort required for machine translation post-editing and the way it is compensated. Freelancers frequently report earning 40–70% less for MTPE compared to human translation, with rates dropping to as little as $0.02–$0.05 per word. Yet, correcting AI’s errors often requires just as much skill and effort as starting from scratch. A 2025 GTS survey revealed that 66% of translators believe MTPE is as demanding—or even more demanding—than full translation. Despite this, 87% of language service providers (LSPs) continue to apply outdated per-word pricing models (ALC 2025), failing to capture the complexity of post-editing.
The solution lies in moving away from outdated per-word pricing and adopting edit-distance pricing, a model that ties compensation directly to the amount of text that must be rewritten or corrected. In practice, most AI-generated output requires 20–30% substantial rewriting—not merely light proofreading. Translation memory tools such as Trados or MemoQ already quantify these edit distances, providing an objective metric that both LSPs and freelancers can use to measure real effort. This shift would not only align compensation with actual cognitive workload but also reduce the frequent disputes between translators and agencies about “what counts” as light versus heavy editing.
Equally important is the establishment of minimum MTPE rates that reflect the professional expertise required. Current MTPE does not even approach a living wage in many markets and undervalues the skill required to correct nuanced errors in tone, register, or domain-specific terminology. By contrast, setting floor rates in the range of $0.07–$0.20 per word, or $25–$50 per hour, would better reflect the time and expertise involved. These benchmarks also bring MTPE closer to the standard range for human translation ($0.09–$0.35 per word), helping to restore fairness and sustainability in the profession.
Equally important is the establishment of minimum MTPE rates that reflect the professional expertise required. Current MTPE does not even approach a living wage in many markets and undervalues the skill required to correct nuanced errors in tone, register, or domain-specific terminology. By contrast, setting floor rates in the range of $0.07–$0.20 per word, or $25–$50 per hour, would better reflect the time and expertise involved. These benchmarks also bring MTPE closer to the standard range for human translation ($0.09–$0.35 per word), helping to restore fairness and sustainability in the profession.
From an economic perspective, the adoption of such models mirrors what has already taken place in other knowledge industries disrupted by automation. For example, in legal review and medical transcription, compensation models evolved from flat per-page or per-line fees to effort-based pricing that accounts for machine accuracy, human oversight, and the risk of errors.
The translation industry faces the same imperative: if human input is priced too low, quality collapses, clients lose trust, and LSPs accelerate their own disintermediation. Nimdzi (2025) warns that half of LSPs already report revenue erosion due to price wars—a direct signal that undervaluing human expertise undermines long-term business viability.
Ultimately, edit-distance pricing and minimum rates are not simply about fairness—they are about sustainability. Without them, experienced linguists will exit the profession, leaving only underqualified workers willing to accept unsustainable pay. For clients, this translates into higher risk, weaker cultural resonance, and potential reputational damage. For LSPs, it means being sidelined in favor of direct MT platforms, which can deliver “good enough” but lack the added human value that clients actually expect when they pay for professional services.
Ultimately, edit-distance pricing and minimum rates are not simply about fairness—they are about sustainability. Without them, experienced linguists will exit the profession, leaving only underqualified workers willing to accept unsustainable pay. For clients, this translates into higher risk, weaker cultural resonance, and potential reputational damage. For LSPs, it means being sidelined in favor of direct MT platforms, which can deliver “good enough” but lack the added human value that clients actually expect when they pay for professional services.
2. Training and Specialization
At the same time, the path forward requires more than fairer pay—it requires investment in training and specialization. Machine translation still fails in areas that demand cultural sensitivity, sector-specific knowledge, or creativity. Legal and medical content, for example, cannot be trusted to raw AI without risking serious errors. LSPs that fund training in MTPE standards (such as ISO 18587), adaptive MT tools, and domain-specific expertise will not only reduce risk but also open up higher-value opportunities: specialized work in these sectors pays 20–50% more.
The industry also needs to recognize specialization as a survival strategy; rather than treating translators as disposable editors, LSPs should position them as knowledge partners who actively shape AI quality. Unfortunately, sidelining translators or relegating them to low-paid post-editing roles, LSPs doesn’t just alienate talent; it threatens the entire translator training sector. Becoming a skilled translator, especially in specialized fields like legal, medical, or technical translation, requires years of education, mentorship, and hands-on experience. If the industry continues to devalue human translators, the pipeline for developing new talent could dry up, undermining the teaching of translation and, ultimately, the quality of language services.
The Training Pipeline at Risk
Translation programs, whether at universities like Middlebury (which is already in the process of concluding graduate programs in Monterey, California...), Kent State, or global institutions, rely on a vibrant industry to justify their existence. These programs combine linguistics, cultural studies, and domain-specific training (e.g., legal terminology or medical protocols), often requiring 2-4 years of academic study followed by 3-5 years of practical experience to produce a competent specialist.
The erosion of translator training has far-reaching consequences, highlighting a worrying trend: a catastrophic decline in formal language learning, compounded by economic realities. The fact that new translators struggle to earn a living wage, discourage graduates from entering the field. If fewer students enroll and fewer graduates stay, the training sector faces a death spiral: shrinking programs, fewer qualified instructors, and a dwindling pool of specialized talent.
Specialized fields like legal, medical, or technical translation require nuanced expertise that AI cannot reliably replicate. For example, MT output always contains errors, many of which can be classified as critical, such as incorrect drug dosages or procedural terms, which could be life-threatening without human intervention. Similarly, legal translations demand precision to avoid misinterpretations that could void contracts or trigger lawsuits. Without a robust training pipeline, the supply of such specialists will dwindle, leaving LSPs unable to meet client needs for high-stakes projects.
Clients, too, will feel the impact. Overreliance on raw MT, without skilled translators to refine it, leads to homogenized language, cultural missteps, and quality complaints. Many clients, attracted by the speed and low cost of AI-only translation, have experimented with it for a variety of tasks, but a significant portion of these clients discover that for important, client-facing, or nuanced content, AI-only solutions fail to deliver on quality. The errors, lack of cultural context, and unnatural phrasing become a business liability. They eventually return to LSPs that offer either human-in-the-loop services or traditional human translation.
3. Advocate for Ethical AI Use, LSPs and Clients Education
LSPs must address ethical concerns—data privacy, cultural homogenization, and quality risks—to maintain trust, but above all their responsibility to their own workforce—the translators. The very model of "AI as a co-pilot" is ethically hollow if it leads to the exploitation of the human professional. LSPs have a moral and professional obligation to their freelance and in-house translators, whose expertise is the true foundation of quality. A reliance on AI as a cost-cutting tool, without a corresponding ethical framework for human collaboration, is unsustainable.
So, on these four dimensions, LSPs should adopt the following practices:
- Offer a Human-First Option: For clients prioritizing accuracy and nuance, LSPs should explicitly position and promote a premium, human-centered service. Clear differentiation between low-cost, AI-only outputs and high-value, professionally curated translations allows for a sustainable model that preserves the craft of translation and affirms the centrality of human expertise.
- Guarantee Data Security: Sensitive projects should be processed through secure, on-premise AI systems rather than cloud-based solutions vulnerable to breaches. Encryption protocols and systematic human oversight are also necessary to ensure compliance with regulatory frameworks such as the GDPR.
- Educate Clients: LSPs must proactively communicate the limitations of raw machine translation—particularly in relation to idiomatic expressions, tone, and cultural nuance. Given that clients often overestimate AI’s accuracy by 20–30%, targeted education helps recalibrate expectations and underscores the enduring value of human intervention.
- Advance Fair AI Governance: Collaboration with professional associations and industry bodies is essential to establish ethical standards for AI integration in translation workflows. Such policies help prevent the exploitation of post-editors and support collective advocacy by translators for equitable working conditions.
4. Diversify Services to Stay Relevant
To remain competitive in an environment where clients increasingly consider bypassing Language Service Providers in favor of direct access to AI tools, it is imperative for LSPs to broaden their service portfolios beyond traditional translation. One promising avenue is the integration of value-added services such as transcreation, cultural consulting, and localization strategy. These areas rely heavily on creativity, cultural sensitivity, and contextual awareness—dimensions of language use that remain resistant to full automation. As a result, they command significantly higher rates, with transcreation, reflecting the premium placed on human creativity and cultural expertise.
At the same time, LSPs can strengthen their relevance by investing in the development of custom AI solutions. Rather than relying on generic LLM platforms, LSPs can collaborate with translators to train domain-specific, client-tailored models. This approach not only reduces the limitations inherent in off-the-shelf AI systems but also creates a value proposition rooted in proprietary expertise.
Finally, focusing on niche markets represents another essential strategy for safeguarding long-term sustainability. Fields such as gaming, law, and medicine demand a high degree of domain-specific accuracy, contextual adaptation, and risk management that AI alone cannot reliably provide. Translators specialized in such domains are less vulnerable to AI-driven disruption, as human oversight remains indispensable to ensuring both quality and compliance.
In short, by diversifying their service offerings—blending creative, strategic, and technical expertise—LSPs can not only mitigate the risk of disintermediation but also position themselves as indispensable partners in a translation ecosystem increasingly shaped by artificial intelligence. Equally important, such diversification ensures that translators themselves are not relegated to low-paid, mechanical post-editing tasks, but are instead recognized as highly skilled professionals whose expertise commands fair compensation. In this way, the sustainability of the industry is tied not only to client satisfaction but also to the necessity of enabling translators to make a decent living.
Reality Check: A Translator-Less World?
The notion of a translator-less world remains, for the foreseeable future, more hypothetical than real. While machine translation has achieved impressive progress in processing large volumes of text at speed, it continues to fall short in dimensions requiring intuition, creativity, and ethical discernment. In high-stakes or culturally sensitive contexts, human translators remain indispensable. Yet, as artificial intelligence advances and begins to approximate near-human fluency in narrow domains, the distinction between technological potential and practical applicability becomes increasingly difficult to draw. The present challenge, therefore, is not one of replacing translators, but of reconfiguring their role within hybrid workflows where human and machine operate in tandem. In this evolving landscape, the most viable model is one of symbiosis: AI delivers efficiency and scale, while human translators contribute depth, nuance, and cultural resonance.
The future of translation is not a zero-sum game between human and machine. It's an evolving symbiosis where each side plays to its unique strengths. AI provides efficiency and scale, while human translators contribute the depth, nuance, and cultural resonance that remain indispensable. Language Service Providers (LSPs) must understand and embrace this reality. The future of the translation industry depends on them practicing what the most viable model preaches every day: a commitment to the powerful and necessary collaboration between human and machine.
And here lies the essential truth: no matter how advanced artificial intelligence becomes, the final responsibility, the ultimate act of refinement and completion, rests with human translators. They are—and will remain—the true finishers of every translation, ensuring that language is not merely transferred, but fully realized in meaning, tone, and cultural integrity.
*
About finition, have you ever heard a well-known joke, often told to illustrate the value of expertise and experience over the time spent on a task:
A huge, brand-new cruise ship grinds to a complete halt in the middle of the ocean. The captain is desperate. The crew can't find the problem. They call in the world's best engineers, who work for days, examining the engine room with state-of-the-art instruments, but nothing works: the ship remains motionless. The owner, at his wit's end, is willing to try anything. He contacts an old retired mechanic, a local legend, who arrives carrying nothing but a beat-up old toolbox. He doesn't say a word. He walks around the engine room, listening intently to every sound and every vibration. He stops in front of a massive valve, observes it for a long time, then pulls a small hammer from his toolbox. He gives the valve two or three sharp taps in a very specific spot. Immediately, with a loud clatter, the engines start up again. The ship begins to move. The crew cheers, and the captain is relieved. The old mechanic, calm, puts his hammer away and hands the owner a bill for $15,000. The owner is shocked. "$15,000?!", he exclaims. "You only spent a quarter of an hour and gave it a few taps with a hammer! It can't possibly be worth $15,000!" The old man looks at him, unfazed. "Sir," he says, "I've itemized the bill for you." He hands over a small piece of paper on which he has written:
• Tapping with the hammer: $100
• Knowing where to tap: $14,900
Well, this is not a joke! LSPs should know that. And Clients too.
