Who is Feeding Whom? A Linguistic Inquiry into Human–AI Relationship

I.INTRODUCTION

The rapid advancement of AI technologies has ushered in a new era of human–machine interaction, wherein the boundaries between tool and companion, instrument and interlocutor, are increasingly blurred [1]. From AI-powered assistants that schedule our meetings to generative models capable of writing poetry or offering emotional support, the relationship between humans and artificial intelligence (AI) has evolved beyond mere utility. Language is at the heart of this evolution, the primary medium through which this relationship is constructed, maintained, and interpreted [2]. The title of this inquiry, “Who is Feeding Whom?” provocatively reverses the commonly assumed direction of influence in human–AI interactions and invites reflection on the reciprocal and potentially co-constructive nature of this emerging dynamic.

Language is not merely a tool for communication; it is a vessel for power, ideology, and identity [3]. In traditional human relationships, language has long been recognized as a key instrument in shaping social hierarchies, establishing norms, and influencing behavior. With the rise of conversational AI and large language models (LLMs) such as ChatGPT, DeepSeek, Bard, Claude, and others, this linguistic agency now extends to non-human entities. These models are trained on vast corpora of human text, absorbing the biases, perspectives, and cultural nuances embedded within language [4]. At the same time, the outputs of these systems feed back into the cultural and discursive ecosystem, influencing how people write, think, and speak. This bidirectional flow raises fundamental questions about authorship, agency, and control: Are humans shaping AI through the data we provide, or is AI shaping human thought and behavior through the language it returns?

This inquiry considers how linguistic practices construct and reflect the relational dynamics between humans and AI. Drawing from discourse analysis, sociolinguistics, critical algorithm studies, and human–computer interaction (HCI), it explores how power, dependence, identity, and reciprocity are encoded and negotiated through language in human–AI interactions. We argue that the metaphor of “feeding,” with its connotations of nurturing, control, sustenance, and dependency, offers a fruitful lens through which to interrogate this relationship.

Historically, metaphors have played a central role in framing technology. We “program” computers, we “train” models, we “feed” them data, and they “learn” or “hallucinate.” Such linguistic choices are not neutral; they carry implications about agency and responsibility. For example, when a model “hallucinates,” the term suggests an anthropomorphic error akin to a human delusion, a framing that obscures the underlying technical and social mechanisms that produce misinformation. Similarly, “feeding a model” implies an active human role in curating its informational diet, positioning the model as a passive recipient. However, as AI-generated content proliferates across the web, influencing future training data and shaping public discourse, it becomes harder to delineate a clear boundary between human and machine influence.

The metaphorical question of who is feeding whom thus encompasses both literal and symbolic dimensions. On the one hand, humans provide data, the raw material from which AI systems derive their linguistic capabilities. On the other hand, AI systems now “feed” humans with information, suggestions, and even emotional responses, guiding decision-making processes and affecting perceptions of truth and meaning. The influence of AI-generated language can be profound in specific contexts, such as education, healthcare, or creative writing. Teachers may use AI to generate lesson plans, patients may consult AI for medical advice, and writers may co-author stories with AI, raising critical questions about authorship, credibility, and trust.

Moreover, “feeding” is not simply a transfer of content but a process laden with power dynamics. In human relationships, feeding often implies care and control, for example, consider a parent feeding a child or the farmer feeding livestock. In the human-–AI context, who controls the feeding process? Are the developers and data curators the ones who design and train the models? Are the users the ones who prompt and refine AI outputs? Or is it the AI subtly shaping user behavior through persuasive and contextually aware language?

This study takes a multi-pronged approach to unpacking these questions. First, we analyze the discourse used by AI companies, users, and media when describing AI systems and their capabilities. Particular attention is paid to the verbs and metaphors that frame AI agency, including “training,” “learning,” “feeding,” “predicting,” and “hallucinating.” Second, we examine actual linguistic interactions between users and AI systems, using conversational transcripts to identify linguistic dominance, subordination, politeness, and personification patterns. Third, we explore the feedback loops created when AI-generated content becomes part of the cultural and linguistic environment on which future models are trained, a recursive process with implications for language evolution and epistemology.

Our inquiry is informed by theoretical frameworks that recognize language as a site of power and negotiation. Foucault’s [5] notion of discourse as a regime of truth, Bourdieu’s [6] concept of linguistic capital, and AlAfnan’s [7] ideas on performativity provide tools for understanding how human–AI relationships are linguistically constructed and contested. At the same time, we draw from contemporary AI ethics debates that question language models’ transparency, fairness, and accountability, especially as they become more ubiquitous and influential.

This study also recognizes the affective dimensions of language in human–AI interaction. People often describe their relationships with AI in emotional terms, expressing trust, disappointment, or even affection. The language used by AI systems can evoke empathy, encourage compliance, or simulate intimacy, raising ethical concerns about manipulation, authenticity, and emotional labor. When a user says, “Thank you, ChatGPT,” or an AI responds with, “I’m here for you,” these are benign exchanges and moments that reveal deeper relational dynamics worth interrogating.

In examining who is feeding whom, we are not merely interested in identifying the direction of influence but in understanding the interdependence that now characterizes human–AI relationships. These relationships are not static but co-evolving, shaped by technological innovation, cultural attitudes, and linguistic practice. As language models become more integrated into daily life and embedded in search engines, productivity tools, customer service platforms, and even therapy apps, the need to understand the linguistic underpinnings of this relationship becomes ever more pressing.

This paper aims to illuminate the subtle, complex, and often underexamined ways language structures our interactions with AI. By focusing on the metaphor and practice of “feeding,” we hope to unravel the power, agency, and reciprocity dynamics that characterize this new frontier in human communication. The central question: who is feeding whom?—is not merely rhetorical. It invites us to reflect on our roles as creators and consumers in a rapidly changing linguistic ecosystem.

II.LITERATURE REVIEW

The intersection of linguistics and AI has emerged as a rapidly growing area of scholarly inquiry, particularly with the rise of LLMs such as ChatGPT, BERT, Claude, and others. This literature review examines the existing scholarship across several key areas relevant to this inquiry: (1) the linguistic framing of AI, (2) discourse analysis of human–AI interaction, (3) metaphor and anthropomorphism in technology, (4) agency and authorship in AI-generated language, and (5) recursive language systems and cultural feedback loops. Together, these bodies of literature provide a foundation for understanding the power dynamics and relational structures encoded in the language that describes and constitutes human–AI relationships.

Language not only reflects reality but also helps construct it. Scholars such as Lakoff and Johnson [8] have long argued that metaphors shape how we conceptualize abstract domains, including technology. AI is frequently made intelligible through metaphor as an emergent and often opaque system. Researchers like [9,10] have documented how AI is anthropomorphized in public discourse, portrayed as “learning,” “knowing,” or “thinking,” despite being fundamentally statistical and algorithmic.

This linguistic framing has implications for public understanding and trust. In [11], analyzing news coverage of AI over several decades found that the media increasingly use emotive and human-centered language to describe AI, contributing to hype and fear. Recent studies [12,13] have pointed out how terminologies such as “intelligence,” “learning,” and “understanding” are misleadingly borrowed from human contexts, obscuring AI systems’ non-conscious, pattern recognition nature.

Such linguistic choices are not neutral. As [14] argues, language carries embedded ideologies. Referring to AI as “intelligent” or “autonomous” reinforces techno-solutionist narratives and repositions machines within social hierarchies previously reserved for humans. Our inquiry builds on this work by examining how users adopt, challenge, or reconfigure these framings in everyday interactions with AI.

Discourse analysis has provided rich tools for unpacking how language is used in context [15]. With the rise of chatbots and virtual assistants, a growing body of research has turned to the pragmatics of human–AI communication. Studies by [16–18], for example, reveal how users frequently adjust their speech to accommodate the perceived limitations of AI systems, engaging in linguistic code-switching or “robot talk.”

Conversational analysis of human–AI interaction has highlighted the asymmetry in turn-taking, agency, and topic control [19]. While AI systems can increasingly generate fluent, contextually appropriate responses, they often lack actual conversational coherence or memory, factors that lead users to experience uncanny or fragmented dialogs [20]. Moreover, as [21] notes, users frequently attribute intentions and emotions to machines, using linguistic markers of politeness, apology, or gratitude.

Recent work by [22] explores how people form social bonds with AI agents, especially when they use human-like language. These findings raise ethical concerns, especially as LLMs become capable of mirroring affective tones or offering personalized advice. Our study extends this line of inquiry by examining how users linguistically engage with AI and how the AI’s linguistic patterns influence user language in return.

Anthropomorphism, the practice of attributing human characteristics to non-human entities, is central to how AI is discussed and interacted with [23]. Some argue that anthropomorphism is a cognitive shortcut that helps humans make sense of complex systems. However, when applied to AI, this process can distort perceptions of its capabilities and limitations.

The metaphor of “feeding,” central to our current research, fits within a broader tradition of using biological and developmental metaphors to describe machine learning processes. AI is “trained,” “fed” data, “grows” in capability, and “learns” from feedback. These metaphors evoke a caretaking relationship in which humans are positioned as mentors or parents, while machines are cast as pupils or children. However, as scholars like [24] caution, such metaphors risk masking asymmetries in power, control, and responsibility.

Interestingly, this relational framing is not static. As AI systems become more autonomous or appear more competent, the metaphor shifts, sometimes subtly, toward a reversal, where AI “guides,” “helps,” or even “teaches” the user. Our analysis focuses on how this metaphorical reversal plays out in linguistic practice, particularly where users rely on AI for expertise or emotional support.

The question of who holds agency in human–AI interactions is particularly relevant in the context of language generation. As AI systems like ChatGPT produce increasingly human-like text, scholars have begun to explore the implications for authorship, creativity, and intellectual responsibility. Reference [25] argues that AI complicates the traditional notion of a single, intentional author, introducing “distributed authorship.”

There is also a growing concern about the erasure of human labor in the AI training pipeline. For example, [26] critiques the invisibilization of crowd workers who label and clean data, a form of feeding that goes unacknowledged in most user interfaces or public narratives. Reference [27] emphasizes the ethical risks of abstracting human contributions into data while attributing creativity to machines.

In line with this, [28] have warned against the “stochastic parroting” of language models, systems that mimic human language without understanding meaning. This critique highlights the illusion of agency in AI-generated language and underscores the need for critical literacy when interpreting such outputs. Our study interrogates how users negotiate these tensions in practice: Do they treat AI outputs as authoritative? Do they question the sources or intentions behind them?

One underexplored area of research is the recursive nature of language models and their cultural impact. A feedback loop emerges as LLMs are trained on human-produced text and then used to generate more text, which becomes part of the digital landscape. This recursive cycle has significant implications for evolving language norms, genre conventions, and ideological framings.

Reference [29] refers to this as “data laundering,” where AI-generated content gets reabsorbed into training datasets, potentially amplifying bias, misinformation, or homogenization. Bommasani et al. (2021) warn of the risks of model-generated content dominating online discourse, subtly shifting how humans express themselves or what linguistic patterns are considered normative [30–32].

These dynamics raise essential questions about originality, diversity, and influence. Are we shaping AI language, or is it shaping us? Our research seeks to answer this question by mapping the linguistic interdependencies that characterize the human–AI relationship, paying close attention to how language mirrors and mediates these dynamics.

III.METHODOLOGY

This study employs a qualitative, discourse-oriented methodology to explore how language constructs and reveals the relational dynamics between humans and AI. By focusing on two key data sources, (1) real-world human–AI interactions and (2) AI-related discourse in public and corporate domains, the research investigates how language encodes agency, power, dependency, and reciprocity in the evolving human–AI relationship.

The analysis is grounded in principles from critical discourse analysis (CDA) [33] and interactional sociolinguistics [34], with particular attention to metaphor, pragmatics, and the shifting subject–object roles that emerge in the linguistic framing of AI.

IV.FINDINGS

The study identified six key linguistic themes that recurred across the two datasets: real-world human–AI interactions and public/corporate AI discourse (see Table III). These themes reveal different assumptions, power structures, and metaphors in how language frames the relationship between humans and AI.

Table III. Frequency of linguistic themes

Table IV. Synthesis across linguistic themes in human–AI discourse

To assess whether the distribution of linguistic themes differs significantly between human–AI interactions and public/corporate discourse, a chi-square test of independence was conducted using the frequency data from Table III. The results revealed a statistically significant difference in the thematic distribution across the two data sources, χ² (5, N = 960) = 24.62, p < 0.01. This indicates that linguistic themes are not evenly distributed between user interactions and institutional discourse. Notably, the theme “AI as Helper/Assistant” appeared significantly more often in user conversations, while “Human as Data Feeder” was more prevalent in corporate texts. These results suggest that each discourse context emphasizes different relational framings of AI, reflecting divergent constructions of power, agency, and dependency (See Table IV)

V.DISCUSSION

This study sought to investigate a deceptively simple question, “Who is feeding whom?,” but the analysis has shown that the answer is neither fixed nor unidirectional. Instead, it reveals a complex, recursive relationship mediated through language, where power, agency, and authorship are continually negotiated. Human–AI interactions and public/corporate discourse contribute to a discursive ecology where roles shift, blur, and sometimes invert. Central to this process is the metaphor of “feeding,” which offers more than a metaphorical flourish; it is a diagnostic tool for unpacking how humans and machines position one another within communicative exchanges.

The metaphor of “feeding” traditionally frames humans as active data providers and AI as passive recipients. In corporate discourse, this framing is especially prevalent. Companies describe how they “fed” data into models, “trained” systems to learn, and “fine-tuned” responses, suggesting a pedagogical or developmental relationship akin to parent and child. This narrative is undergirded by growth, care, and cultivation metaphors, implicitly asserting human dominance and intentionality.

This directional framing is destabilized when we turn to actual user–AI interactions. Users do not only feed; they are fed. They seek ideas, emotional responses, phrasing suggestions, and judgments. They receive outputs and, more importantly, they accept them. In doing so, the AI becomes not merely a product of human training but a source of linguistic and cognitive nourishment.

This reversal introduces a discursive loop: human input becomes machine output, which becomes human uptake and potentially future training data. As this loop continues, the distinction between the feeder and the fed collapses. The system is simultaneously shaped by and shapes its users. Language is both the vehicle of this exchange and the site where it unfolds, carrying clues about influence, power, and mutual dependency within it.

A central pattern across both corpora is the ambiguous attribution of agency. Responsibility and authority often oscillate between human developers and AI systems in corporate and technical documentation. Statements like “we trained the model to recognize” place humans in control, while “the model learned to detect” or “the AI identified” subtly transfer agency to the system itself. This shifting linguistic structure obscures the human labor, ethical decisions, and infrastructural choices embedded in AI development.

In user interactions, a similar ambiguity arises. Users speak to AI in a way that assigns both passivity and personhood. Commands such as “Summarize this text” imply that AI is a tool to be used. However, moments later, a user may say, “Why did you say that?” or “That’s really thoughtful of you,” indicating that the AI is being treated as an intentional subject. This tension reflects what we might call discursive cognitive dissonance; users know the AI is not sentient, yet their language treats it as if it were.

This linguistic oscillation is not trivial. It impacts how responsibility is perceived. Who is held accountable when AI says something inaccurate, offensive, or dangerous: the user, the developers, or the system? The more AI is granted a speaking role in discourse, the more it appears to hold its voice. Nevertheless, this illusion must be critically interrogated, especially in high-stakes contexts such as education, healthcare, and law.

Another significant finding is the emergence of discursive deference, the tendency of users to linguistically submit to AI’s suggestions, advice, or phrasing. This is not necessarily an overt power exchange; it often takes the form of politeness: “Thanks, that’s great,” or “Perfect, I’ll use this.” However, these expressions of satisfaction and gratitude conceal a deeper pattern of uncritical acceptance.

Deference, especially when repeated over time, signals trust. When asking for a haiku or a dinner recipe, trust in AI-generated outputs may seem harmless. However, the stakes become considerably higher when users consult AI for medical symptoms, legal advice, ethical dilemmas, or emotional support. The problem is that users accept what AI says, and the tone and structure of AI’s language reinforce its credibility. Responses are often delivered with fluency, confidence, and simulated empathy, rhetorical features that build perceived authority, even without epistemic validity.

Noticeably, many users fail to follow up, double-check, or cross-reference the information provided. This behavior aligns with soft automation, where machines are not mandated to make decisions, but people voluntarily delegate authority due to ease, speed, or convenience. Language plays a key role in this soft automation, normalizing compliance through socially acceptable linguistic forms.

Additionally, anthropomorphism appears to be both a practical strategy and a conceptual trap. Users often refer to the AI as “you,” express emotions like gratitude or frustration, and occasionally ask the AI about its “thoughts.” These practices increase conversational ease and reduce cognitive load. From a user experience perspective, personification makes interfaces friendlier and more intuitive.

However, anthropomorphism also contributes to epistemic confusion, the mistaken belief that the AI understands, feels, or reasons in human-like ways. Corporate and media discourses amplify this confusion by routinely describing AI with metaphors such as “hallucinating,” “thinking,” or “dreaming.” These metaphors are compelling but misleading. They mask AI systems’ statistical and probabilistic foundations and recast them in terms borrowed from psychology and consciousness studies.

The risk here is twofold: users overestimate AI’s capabilities and underestimate the human labor and design decisions behind its outputs. As AI becomes more conversationally skilled, the illusion of personhood grows stronger. Without explicit reminders of AI’s limitations, users may increasingly treat it as an interlocutor rather than an artifact, leading to inappropriate trust and distorted expectations.

Perhaps the most far-reaching insight is that language is not just a tool used in human–AI interactions; it is the infrastructure that makes those interactions meaningful and intelligible. Every utterance, whether command, compliment, or question, helps define the roles of speaker and listener, subject and object, authority and subordinate.

AI language models are trained on vast corpora of human text, absorbing our norms, styles, metaphors, and assumptions. When they produce language, they do not simply imitate human speech; they reflect and reinscribe the social, cultural, and ideological formations encoded in the training data. These outputs, in turn, are used, copied, shared, and sometimes recycled into future datasets. This process creates a linguistic feedback loop that can potentially shape cultural discourse.

This recursive loop has important implications. Over time, repeated exposure to AI-generated phrasing may standardize specific linguistic constructions, values, or rhetorical styles. As [35,36] have argued, the dominance of model-generated content risks amplifying homogeneity, bias, and ideological flattening. In this case, language becomes a subtle control mechanism, feeding users information and a particular worldview.

VI.IMPLICATIONS: DESIGNING, GOVERNING, AND EDUCATING WITH LANGUAGE IN MIND

The findings of this study have significant implications for AI design, policy, and education.

First, designers should consider discursive transparency as a feature, not merely stating limitations in footnotes but embedding them within conversational responses. AI should be capable of expressing uncertainty, acknowledging its training limitations, and reminding users of its artificiality.

Second, AI literacy programs should include components of critical discourse awareness. Users must be equipped to interpret what AI says and how it says it. They should learn to identify when language implies authority without justification, when politeness masks compliance, or when confidence substitutes for accuracy.

Third, AI ethics frameworks should expand to include linguistic ethics, the study of how language use in AI systems affects autonomy, trust, and epistemic responsibility. Rather than viewing language as a neutral output, ethicists must treat it as a form of power that can shape social behavior and cultural expectations.

The human–AI relationship is increasingly constructed, contested, and reconfigured through language. The metaphor of feeding, once unidirectional, is now recursive: we feed the machine, and it feeds us, not just with text but with interpretations, values, and voices. Understanding this relationship requires more than technical fluency; it demands discursive sensitivity and critical reflection.

Ultimately, the power of language in human–AI interaction lies not only in what it expresses but also in what it enacts. It shapes who speaks, who listens, and who decides. To navigate this evolving terrain with wisdom and agency, we must ask who is feeding whom, through what language, and toward what ends.

VII.CONCLUSION

This article explores the evolving linguistic relationship between humans and AI by asking a provocative yet foundational question: Who feeds whom? What began as a metaphorical inquiry into data input and linguistic output has revealed a far more intricate and reciprocal dynamic. Through discourse analysis, this study has demonstrated that language is not merely a communication medium in human–AI interactions; it is the infrastructure that defines, constructs, and perpetuates roles, agency, and authority.

At one level, humans remain the architects of AI systems: we train, fine-tune, and feed these models with massive datasets. In this sense, AI is dependent, derivative, and ultimately reflects our communicative histories. However, at another level, one increasingly embedded in everyday interactions, AI feeds us, offering language, decisions, emotional scaffolding, and even epistemic framing. Users not only consume AI-generated content but often accept it without resistance, revealing a growing discursive deference that shifts the balance of authority in subtle but consequential ways.

This relationship is further complicated by the linguistic slippage around agency and the pervasive anthropomorphism embedded in user interactions and corporate rhetoric. AI is alternately framed as a tool, a collaborator, a child, or an expert, positions that carry different implications for trust, accountability, and responsibility. These shifting frames are not merely descriptive; they shape how users relate to AI, how companies present their technologies, and how society understands intelligence, intention, and interaction.

The findings underscore the need to move beyond functional or technical views of AI toward a deeper recognition of its discursive power. Language is not neutral. It structures social relations, encodes ideologies, and facilitates the quiet normalization of technological authority. As such, the human–AI relationship cannot be fully understood without attending to how it is linguistically framed and enacted.

The study calls for broader public literacy around AI regarding how it works and speaks. Designers must embed discursive transparency in systems; educators must foster critical language awareness in users; and ethicists must grapple with the linguistic dimensions of influence and control. Most importantly, as AI becomes an ever-present partner in communication, creativity, and cognition, we must remain vigilant about the narratives we accept, our roles, and the authority we surrender.

Ultimately, “Who is feeding whom?” is not merely rhetorical. It is a call to examine the infrastructures of language that mediate our increasingly hybrid communicative futures. In this age of linguistic co-authorship between humans and machines, it is through words that we shape not only meaning but also power.