A recent study published on ArXiv on April 24, 2026, has highlighted how the language used in the artificial intelligence debate is often characterized by strategic polysemy, a phenomenon that blends precise technical definitions with anthropomorphic or common-sense associations, profoundly influencing public perception and AI governance.
What happened
The paper titled "Strategic Polysemy in AI Discourse: A Philosophical Analysis of Language, Hype, and Power" Strategic Polysemy in AI Discourse: A Philosophical Analysis of Language, Hype, and Power analyzes the use of terms such as "hallucination", "chain-of-thought", "introspection", "language model", "alignment", and "agent" in the context of AI. The authors argue that these terms intentionally maintain multiple interpretations simultaneously. On one hand, they have narrow, specific technical definitions for experts; on the other, they evoke broader, intuitive, often human-like associations for the general public. This ambiguity is not accidental but "strategic," as it allows navigating between scientific rigor and media resonance, sometimes fueling unrealistic expectations or misunderstandings.
For example, the term "hallucination" to describe errors in large language models (LLMs) suggests an almost human ability to "invent," when in reality it refers to the generation of incoherent or factually incorrect text based on statistical patterns. Similarly, "agent" can refer to autonomous software performing specific tasks, but it also evokes the image of an entity with its own intentions and will. This linguistic duality is pervasive and extends to various areas of AI, from the discovery of new materials with systems like DielecMIND Expanding the extreme-k dielectric materials space through physics-validated generative reasoning to the generation of synthetic data for education Synthetic Data in Education: Empirical Insights from Traditional Resampling and Deep Generative Models, where technical complexity is often simplified or metaphorized.
Why it matters
This polysemy has significant implications for society, labor, and governance. Firstly, it distorts public understanding of AI, making it difficult to distinguish between current capabilities and future potentials, between science and science fiction. This can lead to excessive enthusiasm (hype) or unfounded fears, hindering informed and rational debate. For policymakers, the lack of terminological clarity complicates the formulation of effective regulations, such as the EU AI Act, which must rely on precise definitions to classify risks and establish responsibilities. If key terms are ambiguous, so too will be the laws, with consequences for citizen safety and rights.
In the world of work, misperceptions of AI capabilities can generate unwarranted anxiety about human replacement or, conversely, create unrealistic expectations about technological solutions. Understanding how AI operates, for example, in the integration between Reinforcement Learning (RL) and Model Predictive Control (MPC) for complex systems A Systematic Review and Taxonomy of Reinforcement Learning-Model Predictive Control Integration for Linear Systems, is crucial for professional retraining and skill adaptation. This is not just a semantic issue, but one of power and responsibility: whoever controls the language partly controls the narrative and decisions about AI.
The HDAI perspective
For Human Driven AI, clarity and rigor in language are indispensable pillars for promoting ethical AI and responsible development. Our mission is to demystify artificial intelligence, providing authoritative yet accessible analysis that avoids sensationalism and clickbait. We recognize that strategic polysemy, while it may serve communication or marketing purposes, risks undermining trust and critical understanding. It is essential for journalists, researchers, and policymakers to commit to using precise language, clearly distinguishing between metaphors and technical definitions.
Terminological clarity is a fundamental pillar for ethical AI and effective governance, and this will be a central theme at the HDAI Summit 2026 in Pompeii. Only through transparent communication can we build a healthy and productive relationship with AI, where benefits are maximized and risks are managed with awareness. We must educate the public to recognize and question the use of ambiguous terms, fostering a culture of critical thinking about AI.
What to watch
The evolution of AI language will be a key indicator of the sector's maturity. It will be interesting to observe whether the scientific community and the media adopt a more rigorous approach, or if the pressure for simplification and hype continues to prevail. The ability to distinguish between the real capabilities of AI, such as image style transfer via StyleVAR StyleVAR: Controllable Image Style Transfer via Visual Autoregressive Modeling, and exaggerated narratives will be crucial for the future of innovation and regulation.