Linguists in the AI era: From resistance to renaissance

Geneva is a city of multilingualism. The seamless exchange of translation and interpretation among six UN languages is a daily occurrence for more than a thousand interpreters and translators based in Geneva. The linguistic profession is enriched by the University of Geneva School of Translation and Interpretation, a renewed academic institution.

Understandably, the rise of AI has instilled a sense of existential concern within Geneva’s linguistics community, as tools like ChatGPT and other sophisticated large language models (LLMs) began to take over tasks related to translation and interpretation.

Will machines render their expertise obsolete? The answer is both yes and no. While AI will automate many language tasks, the linguistic profession is not vanishing—it is transforming. At the heart of this evolution lies a tension between syntax and semantics. Linguists who engage with this duality stand to thrive, becoming essential architects in shaping AI’s capacity to navigate human language.

Syntax vs. semantics: The core of AI’s blind spot

Take two sentences: “Vandals destroyed the shop” (active) and “The shop was destroyed by vandals” (passive). Structurally distinct, they share identical meanings. AI systems, such as large language models, excel at parsing syntactic patterns—tracking word order, grammatical roles, and token frequencies. However, meaning remains elusive. Semantics requires understanding why the passive voice shifts focus to the shop’s fate, or how cultural context shapes metaphors like “a storm of protests”.

This divide mirrors linguistics’ intellectual history. Early 20th-century scholars, like Ferdinand de Saussure, framed language as a structural system, prioritising syntax. Decades later, Noam Chomsky revolutionised the field by arguing that syntax alone couldn’t explain language’s creativity or how humans infer meaning from ambiguity. His transformational grammar reinstated semantics as indispensable.

Today’s LLMs embody this unresolved debate. They generate fluent text by mimicking syntactic patterns in vast datasets but often miss nuance, irony, or intent. A model might mechanically translate “break a leg” into another language, oblivious to its idiomatic meaning. It cannot grasp why a diplomatic statement uses the passive voice to obscure accountability, or when doing so risks ethical compromise.

Linguists as AI’s bridge builders

This gap is where linguists hold unique value. Their expertise in both structure and meaning positions them to address AI’s limitations. Three opportunities emerge:

1. Training models beyond data scarcity: LLMs struggle with low-resource languages lacking extensive digital corpora. Linguists can encode syntactic rules and semantic frameworks into these systems, ensuring smaller languages are not marginalised. For example, annotating datasets to teach AI agent-patient roles in Indigenous languages preserves both grammatical structure and cultural worldview.
2. Annotating for depth, not just data: Machines learn from labelled text like layers of meaning analogous to mediaeval palimpsests. Linguists can play a critical role by annotating texts to capture irony, politeness strategies, or domain-specific semantics (e.g., legal “reasonable doubt” vs. colloquial use). Such work transforms raw data into tools for teaching AI contextual sensitivity.
3. Responsible and ethical AI design: In high-stakes domains—law, medicine, diplomacy—miscommunication carries real consequences. Linguists can audit AI outputs, ensuring translations preserve intent or that automated summaries avoid distorting nuance. Their role becomes advocating for meaning integrity in systems built for scale.

A call to action: Adaptation over resistance

Resisting AI’s rise proves as futile as resisting the printing press. Instead, linguists might pivot by focusing on four strategic shifts:

• Strategic upskilling: Acquiring a basic understanding of AI and skills such as data annotation and syntax-based analysis of AI analysis.
• Specialising in tacit knowledge: Prioritising domains where semantics dominate—transcreating marketing content, interpreting cultural subtext, or curating ethically sensitive datasets—capitalises on irreplaceable human judgement.
• Collaborative partnerships: Working alongside AI developers to refine model training, troubleshoot semantic errors, or design adaptive language technologies, especially in under-represented languages.
• Championing ethical priorities: Advocating for transparency in AI’s decision-making and pushing to preserve linguistic diversity ensures technology respects, rather than erases, cultural nuance.

Conclusion: Linguistics’ new frontier

Chomsky once critiqued AI’s purely statistical approach as ‘rote mimicry’. By bridging syntax and semantics, linguists can transform LLMs from pattern-recognition engines into tools capable of thoughtful communication. This is not about job preservation—it is about elevating language itself.

The linguistic professions are not disappearing. They are entering a renaissance. Those who embrace syntax and semantics as twin compass points will ensure AI amplifies—rather than flattens—human expression. In doing so, their work becomes not just relevant but revolutionary.

Geneva and its diverse and rich community of translators and interpreters can play a vital role in charting linguistic preparations for the AI era.