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Scientific frontiers of agentic AI

About

This document presents a Critical Discourse Analysis focused on AI literacy, specifically targeting the role of metaphor and anthropomorphism in shaping public and professional understanding of generative AI. The analysis is guided by a prompt that draws from cognitive linguistics (metaphor structure-mapping) and the philosophy of social science (Robert Brown's typology of explanation).

All findings and summaries below were generated from detailed system instructions provided to a large language model and should be read critically as interpretive outputs—not guarantees of factual accuracy or authorial intent.

The Core Finding

The dominant anthropomorphic patterns in this text are 'AI COGNITION AS HUMAN MENTAL PROCESS' and 'AI AS SOCIAL AGENT.' These patterns are mutually reinforcing: the system must first be established as a 'knower' (possessing memory, intuition, and understanding) before it can be plausibly sold as an 'agent' (negotiating, acting, and deciding). The 'Native Language' metaphor serves as the load-bearing pillar; by framing the mathematical embedding space as a linguistic/conceptual realm, the text grants the AI an internal life and semantic understanding. Without this assumption—that the AI 'means' what it processes—the claim that it 'negotiates' or 'acts on your behalf' collapses into 'it executes a script.' The consciousness architecture is pervasive: the transition from 'processing' to 'knowing' is the foundation upon which the entire vision of 'Agentic AI' rests.

Task 1: Metaphor and Anthropomorphism Audit​

About this task

For each of the major metaphorical patterns identified, this audit examines the specific language used, the frame through which the AI is being conceptualized, what human qualities are being projected onto the system, whether the metaphor is explicitly acknowledged or presented as direct description, and—most critically—what implications this framing has for trust, understanding, and policy perception.

1. Containment and Childhood​

Quote: "Standalone LLMs can be thought of as 'chatbots in a sandbox', the sandbox being a metaphor for a safe and contained play space with limited interaction with the world beyond."

  • Frame: AI as a playing child/organism
  • Projection: This metaphor maps the developmental stage of a child (playing in a sandbox) onto software limitations. It projects a trajectory of biological growth and maturation (leaving the sandbox) onto product development cycles. Crucially, it implies the system has an internal drive or potential to 'grow up' and interact with the world, suggesting that current limitations are merely a phase of youth rather than architectural constraints. It attributes a state of 'innocence' or 'safety' to the system itself, rather than describing the engineering controls placed upon it.
  • Acknowledgment: Acknowledged ('can be thought of as', 'being a metaphor for').
  • Implications: By framing the AI as a child in a sandbox, the text invokes a paternalistic model of trust. We trust children to grow; we protect them. This obscures the industrial reality: these are not growing organisms but commercial products being integrated into critical infrastructure. The risk is that regulators and users view 'breakouts' from the sandbox as natural maturation rather than dangerous deployment decisions by the manufacturer. It softens the transition from 'text generation' to 'action taking' (agentic AI) as a natural life cycle event.
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2. Intelligent Service​

Quote: "The 'agent' of agentic AI is an intelligent personal assistant that knows your goals and preferences and that you trust to act on your behalf"

  • Frame: Software as a human employee
  • Projection: This projects the human qualities of professional competence, fiduciary responsibility, and interpersonal 'knowing' onto a pattern-matching algorithm. The claim that the AI 'knows your goals' attributes a conscious mental state (holding a justified belief about another's desire) to a system that essentially minimizes loss functions based on historical data tokens. It conflates data storage with the conscious state of 'knowing' a person.
  • Acknowledgment: Direct description (unacknowledged).
  • Implications: This is a high-risk framing for liability and privacy. Suggesting an AI 'knows' and 'acts on your behalf' implies a fiduciary duty that software cannot legally or morally hold. It encourages users to share sensitive data (financial, medical) under the illusion of a confidential, interpersonal relationship. It masks the economic reality: the 'assistant' actually serves the corporation (Amazon) first, utilizing user data for model optimization, not just the user's specific goals.

3. Cognitive Intuition​

Quote: "It would not simply automate these tasks but do so intelligently and intuitively, making 'obvious' decisions unilaterally and quietly"

  • Frame: Statistical optimization as human intuition
  • Projection: The text maps human 'intuition'—a complex, often subconscious cognitive process grounded in lived experience and somatic markers—onto the outputs of statistical probability distributions. It claims the system 'makes decisions,' implying a conscious act of will or judgment, rather than the mechanistic execution of a conditional script. This attributes a 'state of mind' (intuition) to what is actually a calculated probability threshold.
  • Acknowledgment: Direct description (unacknowledged, though 'obvious' is in scare quotes).
  • Implications: Describing AI behavior as 'intuitive' is dangerous because intuition in humans is often trusted without evidence. If users believe the system has 'intuition,' they are less likely to audit its 'quiet' and 'unilateral' actions. This creates a 'curse of knowledge' risk where the user assumes the AI understands the reason for a decision, when it has only correlated a pattern. It obscures the risk of hallucination or error by framing it as a sophisticated mental trait.

4. Linguistic Nativism​

Quote: "What is that native language? ... it translates it into an internal representation... There are many examples in biology of such internal representations."

  • Frame: Embeddings as a biological language
  • Projection: This metaphor projects the biological and cognitive structures of the human brain (neuronal selectivity) and human linguistics ('native language') onto the mathematical process of vector embeddings. It implies that the AI 'understands' meaning in a way analogous to biological organisms. By calling embeddings a 'language,' it suggests a semantic understanding that exists 'internally' within the machine's 'mind,' rather than a mathematical spatial relationship between tokens.
  • Acknowledgment: Direct description (framed as a question and biological analogy).
  • Implications: This serves to naturalize the technology, suggesting that AI processing is just another form of biological cognition (referencing 'neuronal selectivity'). It inflates the system's sophistication by conflating mathematical representations of data with mental representations of the world. The risk is an overestimation of the system's robustness: users may believe the AI 'understands' concepts like 'hospital' or 'doctor' (as cited in the text) in a grounded, experiential way, rather than merely as co-occurrence statistics.

5. Cognitive Memory​

Quote: "There is again an apt metaphor with human cognition, in the sense that context can be thought of as the 'working memory' of the LLM."

  • Frame: Data buffer as human memory
  • Projection: This maps the distinct human cognitive faculty of 'working memory' (a conscious workspace for manipulating information) onto the 'context window' (a static buffer of input tokens). It attributes a cognitive capacity to a storage parameter. It implies the AI is 'holding' thoughts in its mind to 'reason' about them, projecting a conscious, attentive state onto a mechanism that simply attends to positional encodings.
  • Acknowledgment: Acknowledged ('apt metaphor', 'can be thought of').
  • Implications: This anthropomorphism invites users to apply human expectations of memory (fallibility based on attention, distraction, or emotional state) to a digital buffer that fails for entirely different reasons (token limits, attention head drift). It conceals the mechanistic reality that if a token falls out of the window, it doesn't 'fade' like a memory; it mathematically ceases to exist for the model. This creates false confidence in the system's ability to 'grasp the bigger picture' as humans do.

6. Understanding and Grasping​

Quote: "Our short-term memory may be fallible, but we generally grasp the bigger picture. These same properties broadly hold for LLM context"

  • Frame: Pattern recognition as conceptual grasping
  • Projection: The text explicitly equates human conceptual understanding ('grasping the bigger picture') with the machine's processing of a context window. This projects the conscious experience of meaning making onto the statistical processing of long-range dependencies in a sequence. It suggests the AI 'understands' the gestalt of a document, rather than merely maintaining statistical correlations across a longer string of tokens.
  • Acknowledgment: Direct description.
  • Implications: This is a profound epistemic conflation. It assures the reader that the AI 'grasps' concepts. This leads to the 'curse of knowledge': the user reads the output, sees meaning, and assumes the AI created that meaning with intent. It obscures the fact that the AI has no access to the 'world' or 'pictures,' only to token sequences. This risks trust in high-stakes summarization (e.g., legal cases mentioned later), where the AI might hallucinate a key fact because it statistically fits, without 'grasping' the legal truth.

7. Agentic Perception​

Quote: "I can't expect my agent to simply 'know a bargain when it sees one' the way I might... especially because my notion of a bargain might be highly subjective"

  • Frame: Evaluation as visual perception/knowing
  • Projection: This metaphor projects the human sensory-cognitive experience of 'seeing' and the epistemic state of 'knowing' onto the data processing of an algorithm. It implies that if the AI were sophisticated enough, it could 'see' a bargain. It frames the limitation as one of 'subjectivity' rather than the fundamental ontological difference between a human (who values things based on lived need) and a script (which minimizes cost variables).
  • Acknowledgment: Scare quotes ('know a bargain when it sees one').
  • Implications: Even with scare quotes, the framing suggests the goal is to replicate this human way of knowing. It displaces the agency of defining value from the user to the system. The risk is that users will assume the agent shares their value system or 'common sense' regarding money, when in reality, it only optimizes the metric it was given (price), potentially sacrificing unquantifiable qualities (safety, comfort) that a human 'seeing' a bargain would instantly factor in.

8. Inter-Agent Sociality​

Quote: "Perhaps a more relevant starting point for agentic negotiation is the field of behavioral economics... asking what does happen when actual human agents interact strategically."

  • Frame: Algorithm interaction as human society
  • Projection: This frames the data exchange between two automated systems as a 'strategic interaction' akin to human social negotiation. It projects human motivations (greed, fairness, altruism, fear of rejection) onto software instances. By invoking 'behavioral economics' (the study of human irrationality and psychology), the text implies that AI models possess a 'psychology' or 'behavioral tendencies' that are social in nature, rather than statistical artifacts of training data.
  • Acknowledgment: Analogy ('Perhaps a more relevant starting point').
  • Implications: This anthropomorphizes the glitches or biases of an AI as 'behavioral traits.' If an AI gives a 'fair' offer in an Ultimatum Game, the text implies it has a sense of fairness. In reality, it is predicting the tokens that usually follow such prompts in its training corpus. This framing obscures the lack of stakes: the AI doesn't care about the money; it has no 'utility' to lose. Treating it as a 'behavioral agent' risks creating policies that treat software as social entities rather than tools.

9. Subjective Common Sense​

Quote: "We might call behaviors like these subjective common sense, because to me they are natural and obvious... But agentic AI will need specifications like this"

  • Frame: Rule-following as common sense
  • Projection: The text attempts to codify human 'common sense'—a conscious, embodied, context-aware state of navigating the world—into a set of 'specifications' for an AI. It projects the feeling of obviousness onto the execution of a security protocol. It suggests the AI needs to 'understand' these preferences, conflating the execution of a conditional rule (IF at office THEN leave door open) with the conscious social understanding of why one does that (trusting colleagues).
  • Acknowledgment: Direct description.
  • Implications: This redefines 'common sense' from a human capability (social judgment) to a computational requirement (policy specification). It hides the extreme brittleness of AI. A human uses common sense to break rules when necessary (e.g., locking the office door because a stranger is present). An AI following a 'subjective common sense' policy would strictly follow the rule, potentially failing in the edge case. Calling it 'sense' creates a false expectation of adaptability.

10. The Native Speaker​

Quote: "The neural networks underlying modern AI typically translate any input into what is known as an embedding space... Thus, the inherent language of generative (and therefore agentic) AI is not the sentences... but their embeddings."

  • Frame: Mathematics as Native Language
  • Projection: This projects the concept of 'native language'—implied to be the 'true' or 'natural' mode of thought for a conscious entity—onto the vector mathematics of the system. It suggests the AI 'thinks' in embeddings and 'translates' to English for us, implying an internal conscious life or 'self' that exists in the embedding space. It attributes a private mental life to the machine.
  • Acknowledgment: Direct description.
  • Implications: This mystifies the technology. By claiming embeddings are a 'language' (rather than a compression format), the text grants the AI a culture and an ontology independent of humans. It suggests we are meeting the AI on its terms. This obscures the fact that 'embeddings' are human-designed mathematical structures, not an evolved biological capability. It creates a 'black box' authority: 'It speaks a language we can't understand, so we must trust the translation.'

Task 2: Source-Target Mapping​

About this task

For each key metaphor identified in Task 1, this section provides a detailed structure-mapping analysis. The goal is to examine how the relational structure of a familiar "source domain" (the concrete concept we understand) is projected onto a less familiar "target domain" (the AI system). By restating each quote and analyzing the mapping carefully, we can see precisely what assumptions the metaphor invites and what it conceals.

Mapping 1: Human Personal Assistant / Employee → Software Application / API Orchestrator​

Quote: "The 'agent' of agentic AI is an intelligent personal assistant that knows your goals and preferences"

  • Source Domain: Human Personal Assistant / Employee
  • Target Domain: Software Application / API Orchestrator
  • Mapping: The relationship between a human boss and a human assistant (trust, loyalty, knowledge of unstated preferences, shared context) is mapped onto the relationship between a user and a software program. This implies the software has a 'theory of mind' regarding the user—that it 'knows' goals rather than just storing parameters.
  • What Is Concealed: This conceals the economic allegiance of the software. A human assistant is paid by the boss; the AI agent is deployed by a corporation (Amazon) whose goals (data extraction, ecosystem lock-in) may conflict with the user's. It also conceals the mechanistic nature of 'knowing': the system simply retrieves variables from a database or weights tokens; it does not hold a belief about the user's desires.
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Mapping 2: Native Language / Mother Tongue → Vector Embeddings / High-dimensional numerical arrays​

Quote: "Thus, the inherent language of generative (and therefore agentic) AI is not the sentences... but their embeddings."

  • Source Domain: Native Language / Mother Tongue
  • Target Domain: Vector Embeddings / High-dimensional numerical arrays
  • Mapping: The concept of a 'native language'—the primary medium of thought and expression for a conscious being—is mapped onto vector embeddings. This suggests that the AI 'thinks' or 'exists' in this mathematical space and that English is merely a second language it uses to talk to us. It implies an internal, subjective reality.
  • What Is Concealed: This conceals the derivative nature of the technology. Embeddings are not a language the AI 'speaks'; they are statistical compression artifacts derived from human language. The AI has no 'intent' to communicate. The mapping also conceals the rigidity of vectors compared to the fluidity of semantics; a vector is a fixed coordinate, not a negotiation of meaning.

Mapping 3: Human Working Memory (Cognition) → Context Window (RAM buffer size)​

Quote: "Context can be thought of as the 'working memory' of the LLM."

  • Source Domain: Human Working Memory (Cognition)
  • Target Domain: Context Window (RAM buffer size)
  • Mapping: The biological faculty of working memory—limited, attentive, and the 'workspace' of consciousness—is mapped onto the context window of an LLM. This implies the AI is 'holding' information in its mind to process it, inviting assumptions about attention spans and cognitive load.
  • What Is Concealed: This conceals the mechanistic process of self-attention. The model doesn't 'remember' in a temporal sense; it calculates attention scores across the entire sequence simultaneously (in parallel) for every prediction. It hides the computational cost: 'remembering' more context isn't a mental effort, it's a quadratic increase in compute resources (O(n^2)).

Mapping 4: Human Intuition / Gut Feeling → Heuristic Optimization / Probability Thresholds​

Quote: "It would... do so intelligently and intuitively, making 'obvious' decisions unilaterally"

  • Source Domain: Human Intuition / Gut Feeling
  • Target Domain: Heuristic Optimization / Probability Thresholds
  • Mapping: Human intuition—a fast, often subconscious mode of decision-making based on accumulated wisdom—is mapped onto the output of a probability function. This suggests the AI has a 'feel' for the right answer that goes beyond strict logic, granting it a pseudo-mystical authority.
  • What Is Concealed: This conceals the lack of ground truth. 'Intuition' in AI is simply high-confidence statistical prediction based on training patterns. It hides the risk of error: human intuition is checked by reality; AI 'intuition' is unchecked hallucination if the pattern holds but the fact doesn't. It also obscures the absence of accountability: 'intuition' is hard to audit.

Mapping 5: Human Behavioral Psychology / Society → Algorithm-to-Algorithm Data Exchange​

Quote: "Asking what does happen when actual human agents interact strategically... perhaps a more relevant starting point for agentic negotiation is the field of behavioral economics"

  • Source Domain: Human Behavioral Psychology / Society
  • Target Domain: Algorithm-to-Algorithm Data Exchange
  • Mapping: The complex social and psychological dynamics of human negotiation (fear, fairness, irrationality) are mapped onto the interaction between two algorithms. This implies the algorithms have 'personalities' or 'psychologies' that can be studied like human behavior.
  • What Is Concealed: This conceals the mimicry nature of LLMs. The AI does not have a sense of 'fairness' in the Ultimatum Game; it produces the token 'accept' because its training data (human texts describing the game) usually contains 'accept' in that context. It hides the absence of stakes: the AI cannot 'lose' or 'win' anything; it has no utility function independent of its code. It conflates simulating a behavior with having a disposition.

Mapping 6: Human Conceptual Understanding / Grasping → Long-range Statistical Dependencies​

Quote: "We will not forget what the task itself is; our short-term memory may be fallible, but we generally grasp the bigger picture. These same properties broadly hold for LLM context"

  • Source Domain: Human Conceptual Understanding / Grasping
  • Target Domain: Long-range Statistical Dependencies
  • Mapping: The human ability to maintain a conceptual goal or 'gist' despite forgetting details is mapped onto the LLM's ability to maintain coherence over long sequences. This implies the AI 'understands' the goal or the 'picture' fundamentally, independent of the specific words.
  • What Is Concealed: This conceals the purely syntactic nature of the model. The AI 'remembers' the task only insofar as the task prompt remains within the effective attention window. It does not 'grasp' the picture; it minimizes perplexity based on the prompt's continued presence. If the prompt is truncated, the 'grasp' vanishes instantly, unlike a human who retains the intent.

Mapping 7: Human Learning / Epiphany ('figure things out') → Feature Extraction / Gradient Descent​

Quote: "Before the advent of modern large-scale machine learning... efforts to manually constructing a database of commonsense facts... show a neural network millions of pictures... and it will figure things out."

  • Source Domain: Human Learning / Epiphany ('figure things out')
  • Target Domain: Feature Extraction / Gradient Descent
  • Mapping: The human process of having an insight or learning a concept ('figuring it out') is mapped onto the process of updating weights via backpropagation. This implies the machine arrives at a state of knowledge or understanding.
  • What Is Concealed: This conceals the mechanical brute force of the process. The network doesn't 'figure out' that water falls; it adjusts billions of parameters until its output matrix matches the training label. It hides the lack of causality: the model learns correlations (pixels of glass usually appear with pixels of water), not causal physics. It confuses function approximation with conceptual discovery.

Task 3: Explanation Audit (The Rhetorical Framing of "Why" vs. "How")​

About this task

This section audits the text's explanatory strategy, focusing on a critical distinction: the slippage between "how" and "why." Based on Robert Brown's typology of explanation, this analysis identifies whether the text explains AI mechanistically (a functional "how it works") or agentially (an intentional "why it wants something"). The core of this task is to expose how this "illusion of mind" is constructed by the rhetorical framing of the explanation itself, and what impact this has on the audience's perception of AI agency.

Explanation 1​

Quote: "When generative AI consumes a piece of content... it translates it into an internal representation that is more convenient for subsequent processing... The neural networks... typically translate any input into what is known as an embedding space, which can be thought of as a physical map in which items with similar meanings are placed near each other."

  • Explanation Types:

    • Theoretical: Embeds behavior in a deductive or model-based framework, may invoke unobservable mechanisms such as latent variables.
    • Functional: Explains a behavior by its role in a self-regulating system that persists via feedback.

  • Analysis (Why vs. How Slippage): The explanation uses a Theoretical framework (embedding space topology) but frames it with agential verbs ('consumes', 'translates'). While describing how the vector space works (proximity = similarity), it slips into an intentional/agential frame by suggesting the AI performs the translation for 'convenience.' This emphasizes the system's active role in 'understanding' input, obscuring the passive mathematical transformation that actually occurs. It implies the 'internal representation' is for the AI's benefit (like a mental model), rather than a mathematical necessity for matrix multiplication.

  • Consciousness Claims Analysis: This passage uses the mechanistic verb 'processing' but explicitly links it to the consciousness-adjacent verb 'translates' and the concept of 'meaning.' Assessment: It treats the AI as 'knowing' meaning. By claiming items with similar meanings are placed near each other, it projects the author's understanding of the semantic link onto the system's spatial arrangement. Curse of Knowledge: The author knows that 'doctor' and 'hospital' are related concepts. When he sees their vectors are close, he attributes that conceptual understanding to the system. Concealed Distinction: The system processes statistical co-occurrence patterns (tokens 'doctor' and 'hospital' appear together in training data). It does not 'know' the meaning of either. It calculates cosine similarity between vectors derived from co-occurrence matrices. The 'map' is a mathematical artifact of compression, not a cognitive map of meanings.

  • Rhetorical Impact: Framing embeddings as a 'map of meanings' constructs the AI as a semantic agent. It persuades the audience that the AI 'understands' the content it processes, rather than just manipulating symbols. This increases trust in the system's ability to handle complex tasks (like summarizing a novel) because the user believes the AI has 'grasped' the internal meaning. It masks the risk that the AI might link concepts that are statistically close but factually unrelated (hallucination).

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Explanation 2​

Quote: "My personal agent... might ingest materials such as my previous flights... to understand my interests and preferences. But to communicate those preferences to another agent... it will not provide the raw source materials... Rather, my agent will summarize my preferences as a point... in an embedding space."

  • Explanation Types:

    • Intentional: Refers to goals or purposes and presupposes deliberate design, used when the purpose of an act is puzzling.
    • Functional: Explains a behavior by its role in a self-regulating system that persists via feedback.

  • Analysis (Why vs. How Slippage): The explanation relies heavily on Intentional framing ('to understand,' 'to communicate,' 'summarize'). It explains the system's action (vectorizing data) in terms of its purpose (understanding interests). This completely obscures the mechanistic how—which is simply data compression to reduce dimensionality—and replaces it with a narrative of an intelligent agent protecting privacy and summarizing concepts. It attributes the goal of the user (privacy/efficiency) to the action of the agent.

  • Consciousness Claims Analysis: The passage explicitly uses the consciousness verb 'understand' ('to understand my interests'). Assessment: It treats the AI as a 'knower' of the user's mind. It distinguishes this 'understanding' from the raw data ('ingest materials'), suggesting the AI distills truth from data. Curse of Knowledge: The author projects his own ability to synthesize travel patterns into a preference (e.g., 'likes aisle seats') onto the machine. Concealed Distinction: The model correlates input tokens (previous flight records) to generate a lower-dimensional vector representation. This vector is a mathematical compression, not a conceptual summary. The AI does not 'know' the preference; it holds a set of weights that statistically favor certain outputs (aisle seats) based on the inputs.

  • Rhetorical Impact: This framing constructs the AI as a discreet, intelligent servant. It builds relation-based trust: the agent 'understands' me and 'summarizes' for me, implying it is on my side. This lowers the user's defense against data surveillance. If the user believes the AI 'understands' preferences, they are more likely to hand over sensitive financial and location data, obscuring the reality that they are feeding a corporate data-mining algorithm.

Explanation 3​

Quote: "One early work showed that LLMs almost exactly replicated human behavior in the ultimatum game... The equilibrium analysis is straightforward... Bob, being fully rational, would prefer to receive a penny than nothing, so he accepts... Alice... proposes the smallest nonzero amount."

  • Explanation Types:

    • Empirical Generalization (Law): Subsumes events under timeless statistical regularities, emphasizes non-temporal associations rather than dated processes.
    • Reason-Based: Gives the agent's rationale or argument for acting, which entails intentionality and extends it by specifying justification.

  • Analysis (Why vs. How Slippage): This is a Reason-Based explanation grafted onto an Empirical Generalization. It explains the AI's output ('replicated human behavior') by invoking the reasons humans act ('being fully rational,' 'would prefer'). It assumes the AI produces the output because of these game-theoretic reasons. This obscures the mechanistic reality that the AI is simply completing a text pattern found in its training data (which includes textbooks describing this exact game).

  • Consciousness Claims Analysis: The passage implies the AI 'replicated human behavior' and engaged in 'rational' decision making. While it doesn't use the word 'knows,' it attributes the complex mental state of strategic preference ('would prefer') to the system. Assessment: It treats the AI as a conscious social actor capable of economic reasoning. Curse of Knowledge: The author knows the game theory optimal strategy. When the AI outputs that strategy, he assumes the AI 'solved' the game, rather than simply regurgitating the solution present in its training corpus. Concealed Distinction: The model predicts the next token in a dialogue about the Ultimatum Game. Since its training data contains millions of examples of humans playing this game, it statistically reproduces the most probable human move. It does not 'play' the game; it autocompletes a script of the game.

  • Rhetorical Impact: This elevates the AI from a text generator to a 'rational agent.' It suggests LLMs can be used for economic modeling or negotiation because they 'act like humans.' This risks policy failures if regulators assume AI agents will respond to incentives (fines, rewards) the way humans do. It obscures the fact that an AI has no utility function to maximize unless explicitly hard-coded, and 'rationality' is just a mimicry of text, not an internal drive.

Explanation 4​

Quote: "It is quite unlikely any of us were ever sat down and run through this narrative... but we all know and agree on them [common sense facts]. Figuring out how to imbue AI models and systems with common sense has been a priority... show a neural network millions of pictures... and it will figure things out."

  • Explanation Types:

    • Genetic: Traces origin or development through a dated sequence of events or stages, showing how something came to be.
    • Dispositional: Attributes tendencies or habits such as inclined or tends to, subsumes actions under propensities rather than momentary intentions.

  • Analysis (Why vs. How Slippage): The passage uses a Genetic explanation ('show... pictures... and it will figure things out') to describe the origin of AI capability. However, it uses the highly agential phrase 'figure things out' to describe the mechanical process of gradient descent. This frames the training process as an active cognitive journey of discovery by the AI, akin to a child learning physics, rather than a passive statistical fitting process orchestrated by engineers.

  • Consciousness Claims Analysis: The text explicitly uses the cognitive phrase 'figure things out' and 'imbue with common sense.' Assessment: It attributes the capacity for insight and world-modeling to the system. It suggests the AI acquires 'knowledge' of physics (glass of water). Curse of Knowledge: The author knows gravity exists. He sees the AI generate a picture of a spilled glass. He assumes the AI 'knows' gravity, rather than having learned a pixel correlation between 'upside down glass' and 'puddle.' Concealed Distinction: The model minimizes loss by adjusting weights to match training examples. It learns that the token/pixel pattern for 'upside down glass' is statistically correlated with 'water on table.' It has no concept of 'gravity,' 'glass,' or 'mess.'

  • Rhetorical Impact: This suggests AI is more robust than it is. If users believe AI has 'common sense,' they will trust it in novel situations. However, AI 'common sense' is brittle; it fails as soon as the statistical distribution shifts (an edge case). This framing hides the massive labor of data annotation required to make the AI appear smart, framing it instead as the AI's autonomous learning.

Explanation 5​

Quote: "Agentic AI will need specifications like this: simply replace doors with online accounts and services... The problem is in helping people articulate and translate their subjective common sense into these frameworks."

  • Explanation Types:

    • Functional: Explains a behavior by its role in a self-regulating system that persists via feedback.
    • Intentional: Refers to goals or purposes and presupposes deliberate design, used when the purpose of an act is puzzling.

  • Analysis (Why vs. How Slippage): This explanation frames the future development of AI as a translation problem—transferring human 'subjective common sense' into 'specifications.' It assumes the AI can hold this common sense if only we can 'articulate' it. This frames the AI as a willing recipient of human values, obscuring the technical reality that 'common sense' in AI is just a hard-coded policy file (IF/THEN statements) or a reward model, not an understanding of why the rules exist.

  • Consciousness Claims Analysis: The text calls these specifications 'subjective common sense,' attributing a human quality (sense) to a machine protocol. Assessment: It treats the AI as a potential 'knower' of values. Curse of Knowledge: The author assumes his nuanced reasons for locking a door can be fully captured by a logical framework. Concealed Distinction: The system executes logic gates based on authentication protocols. It does not have 'sense.' It simply allows or denies access based on credentials. calling it 'common sense' implies a flexibility and judgment that code does not possess.

  • Rhetorical Impact: This minimizes the danger of delegating security to AI. By calling security protocols 'common sense,' it makes them sound natural and intuitive. It obscures the risk that the AI, lacking genuine understanding, will strictly follow a rule that leads to disaster (e.g., locking a user out during an emergency) because it lacks the human judgment to know when to break the rule.

Task 4: AI Literacy in Practice - Reframing Anthropomorphic Language​

About this task

This section proposes alternative language for key anthropomorphic phrases, offering more mechanistic and precise framings that better reflect the actual computational processes involved. Each reframing attempts to strip away the projections of intention, consciousness, or agency that are embedded in the original language.

Original Anthropomorphic FrameMechanistic ReframingTechnical Reality Check
The 'agent' of agentic AI is an intelligent personal assistant that knows your goals and preferencesThe 'agent' of agentic AI is a software application that retrieves your stored user profile and optimizes outputs to match historical data patterns associated with your account.the AI knows your goals → the model retrieves stored variables and ranks potential action tokens based on their statistical similarity to your past behaviors found in the training data.
It would not simply automate these tasks but do so intelligently and intuitivelyIt would automate these tasks by generating outputs with high probability scores derived from training on similar successful task completions.do so intuitively → the model selects actions that minimize a loss function, producing results that statistically align with examples labeled as 'correct' in the dataset.
Generative AI consumes a piece of content... it translates it into an internal representation... known as an embedding spaceWhen the system processes input data, it converts the text strings into numerical vectors (embeddings) based on a pre-calculated co-occurrence matrix.translates into internal representation → the algorithm maps the input tokens to coordinates in a high-dimensional vector space defined by the model's weights.
Context can be thought of as the 'working memory' of the LLM... we generally grasp the bigger pictureContext is the fixed-size buffer of input tokens the model can attend to when calculating the probability of the next token. Unlike human memory, it does not reconstruct meaning but maintains statistical dependencies.grasp the bigger picture → the model maintains attention weights across the sequence, allowing earlier tokens to influence the probability distribution of the current generation.
My personal agent... might ingest materials... to understand my interests and preferencesThe software processes my uploaded documents to extract keywords and patterns, creating a data profile used to filter future information retrieval.understand my interests → the model correlates features in the uploaded text with clusters in its latent space, adjusting its output weights to favor tokens associated with those clusters.
I can't expect my agent to simply 'know a bargain when it sees one'I cannot expect the algorithm to classify a price as 'optimal' based on unquantified criteria.know a bargain → the model compares the numerical price value against a database of historical prices and evaluates it against a pre-defined threshold or utility function.
One early work showed that LLMs almost exactly replicated human behavior in the ultimatum gameOne study showed that Amazon's LLM generated text outputs for the Ultimatum Game that statistically matched the distribution of decisions described in economics textbooks included in its training data.replicated human behavior → the model predicted the token 'accept' or 'reject' based on the high frequency of those responses in the training corpus context of the game.
Show a neural network millions of pictures... and it will figure things outTrain a neural network on millions of images, and it will adjust its weights to minimize the error in predicting pixel correlations.figure things out → the network converges on a set of parameters that allow it to classify or generate images with low loss relative to the training set.

Task 5: Critical Observations - Structural Patterns​

Agency Slippage​

The text exhibits a persistent, bi-directional oscillation between mechanical and agential framing, functioning to selectively validate the system's authority. When discussing capability, the text shifts abruptly from mechanical to agential: 'embeddings' (mechanical) become a 'native language' (agential); 'context windows' (mechanical) become 'working memory' (agential). This is not random; the mechanical language provides scientific cover ('It's just math'), while the agential language sells the vision ('It's a smart assistant').

Crucially, agency is also stripped from humans. The text uses agentless constructions like 'bias was introduced' or 'functionality can be built,' obscuring the engineers at Amazon who actively curate datasets and design reward functions. The 'agent' is framed as an autonomous entity that 'negotiates' and 'decides,' effectively laundering the liability of the corporation. If the agent 'decides' to buy a flight, the error is in its 'intuition,' not Amazon's code. The consciousness projection pattern is foundational here: the text first establishes the AI as a 'knower' (it understands context, it knows preferences) to justify treating it as an 'agent' (it negotiates, it acts). This slides from a Functional explanation (it works by processing) to an Intentional one (it wants to help). The 'curse of knowledge' is the pivot point: the author's deep technical knowledge of what the output means to a human is projected onto the system as the system's own knowledge.

Metaphor-Driven Trust Inflation​

Trust in this text is constructed almost entirely through anthropomorphic metaphor. By labeling the software an 'assistant' rather than a 'processor,' the text invokes a relation-based trust framework: we trust assistants because of loyalty and shared goals. The text explicitly leverages this, stating the agent 'knows your goals.' This is a deception; the system only has a loss function, not loyalty.

Consciousness language acts as a trust signal: claiming the AI 'understands' context implies it is a competent partner. If the text admitted the AI merely 'predicts tokens based on proximity,' the trust would be strictly performance-based (and brittle). By projecting 'intuition' and 'common sense' onto the system, the text encourages users to extend vulnerability to the machine—sharing passwords and financial data—under the false assumption of reciprocity. The risk is profound: users are being trained to trust a probability distribution as if it were a fiduciary. When the system fails (hallucinates), the metaphor of 'intuition' protects it—'it had a bad feeling' is more forgivable than 'the code failed.' This conflation of the statistical (predictable) with the social (sincere) prepares the user to accept the 'Agentic AI' as a valid social actor.

Obscured Mechanics​

The metaphors in this text systematically conceal the material and economic realities of Amazon's AI ecosystem. First, the 'Personal Assistant' metaphor hides the surveillance economy. To 'know your preferences,' the system requires massive, intrusive data extraction. The metaphor suggests this data stays in a trusted relationship, obscuring that it feeds the corporate model's optimization.

Second, the 'Native Language' metaphor for embeddings hides the derivative nature of AI. It implies AI creates its own meaning, erasing the millions of human authors whose copyrighted work was scraped to build the embedding space.

Third, the 'Negotiation' and 'Game Theory' metaphors obscure the lack of genuine stakes. An AI 'negotiating' a price has no skin in the game; it implies a fairness that doesn't exist.

Most critically, the consciousness obscuration ('it understands') hides the dependency on training data. It suggests the AI knows the truth, rather than just knowing what words usually follow other words. This benefits the vendor (Amazon) by making the system seem like an oracle rather than a text-retrieval system. It allows them to sell 'intelligence' without guaranteeing 'truth.'

Context Sensitivity​

The distribution of metaphor in the text follows a strategic 'credibility sandwich.' The introduction is highly agential ('intelligent personal assistant,' 'knows your goals'), setting a visionary hook. The middle section ('What language will agents speak?') retreats into technical specificity about embeddings and vectors, earning scientific credibility. However, even here, the 'native language' metaphor bridges the gap.

As the text moves to 'Agentic bargains' and 'Common sense,' the anthropomorphism intensifies dramatically. The 'Ultimatum Game' section creates a peak of projection, attributing 'rationality' and 'fairness' to the model.

Notably, capabilities are described agentially ('it acts intelligently'), while limitations are often framed mechanistically ('loss of detail in embeddings'). This asymmetry serves a rhetorical purpose: success is due to the AI's mind; failure is due to technical constraints. The text shifts registers from 'X is like Y' (the sandbox metaphor) to 'X does Y' (the negotiation claim) to naturalize the agency. The intended audience—likely business leaders and developers—is flattered by the technical details but sold on the sci-fi dream of the autonomous agent.

Conclusion: What This Analysis Reveals​

The Core Finding

The dominant anthropomorphic patterns in this text are 'AI COGNITION AS HUMAN MENTAL PROCESS' and 'AI AS SOCIAL AGENT.' These patterns are mutually reinforcing: the system must first be established as a 'knower' (possessing memory, intuition, and understanding) before it can be plausibly sold as an 'agent' (negotiating, acting, and deciding). The 'Native Language' metaphor serves as the load-bearing pillar; by framing the mathematical embedding space as a linguistic/conceptual realm, the text grants the AI an internal life and semantic understanding. Without this assumption—that the AI 'means' what it processes—the claim that it 'negotiates' or 'acts on your behalf' collapses into 'it executes a script.' The consciousness architecture is pervasive: the transition from 'processing' to 'knowing' is the foundation upon which the entire vision of 'Agentic AI' rests.

Mechanism of the Illusion:​

The 'illusion of mind' is constructed through a subtle 'Curse of Knowledge' projection. The author, an expert, looks at the mathematical outputs (vectors, game moves) and recognizes their meaning to humans. He then linguistically attributes that recognition to the system itself. The trick is the slippage between simulation and state: because the AI simulates a player in the Ultimatum Game (by predicting the next token), the text treats it as being a player with preferences. The temporal structure aids this: the text starts with the familiar 'sandbox' metaphor to disarm the reader, then uses the 'scientific' explanation of embeddings to validate the 'mind' metaphor. By the time the text reaches 'negotiation,' the audience has been primed to accept the AI as a rational actor. This exploits the audience's desire for a competent servant—we want to believe the assistant 'knows' us, so we accept the illusion.

Material Stakes:​

The consequences of this framing are concrete and high-stakes. Economically, the 'Personal Assistant' metaphor facilitates the extraction of proprietary user data. If users believe the AI 'knows' and 'protects' them like a human assistant, they will integrate it into banking and email (as suggested in the text), granting Amazon unprecedented surveillance access. The 'knowing' frame hides the fact that the agent serves the platform, not the user. Regulary and Legally, the shift from 'processing' to 'acting' creates a liability vacuum. If the AI is framed as 'acting unilaterally' based on 'intuition,' it becomes a quasi-subject. When it makes a disastrous financial trade or negotiates an illegal price-fixing deal (as hinted at in the 'collusive' section), the anthropomorphic framing invites regulators to blame the 'black box' agent rather than the corporation that deployed it. By framing the AI as a 'rational agent,' the text creates a fictional legal personhood that diffuses Amazon's responsibility for the system's outputs.

AI Literacy as Counter-Practice:​

Practicing critical literacy here means rigorously replacing 'knowing' verbs with 'processing' verbs. Reframing 'The AI knows a bargain' to 'The model minimizes cost based on training data' is not just pedantry; it is an act of consumer protection. It forces the recognition that the system has no concept of value, only price variables. Restoring human agency is equally critical: replacing 'Agentic AI will need specifications' with 'Amazon engineers must hard-code safety protocols' reveals the human labor and liability involved. This practice counters the 'Liability Diffusion' stake by pinning responsibility back on the manufacturer. Resistance will come from the industry (Amazon), whose marketing relies on the 'magic' of the agent to drive adoption. Systematic adoption would require journals to mandate 'mechanistic disclosure statements' alongside these metaphors, forcing researchers to admit the gap between the math and the narrative.

Path Forward​

To navigate the future of AI discourse, we must recognize that different vocabularies enable different worlds. A Mechanistic Vocabulary (e.g., 'the model retrieves,' 'the system correlates') enables rigorous accountability and safety engineering but risks alienating the public and stalling investment. An Anthropomorphic Vocabulary (e.g., 'the agent understands,' 'it negotiates') enables rapid adoption and intuitive user interface design but risks catastrophic trust failures and legal confusion.

A viable path forward involves 'Dual-Register' communication. Institutions could demand that claims of capability be stated mechanistically ('it predicts code'), while user interfaces act metaphorically ('how can I help?'). Regulatory frameworks should strictly enforce mechanistic definitions for liability: an AI never 'decides'; a corporation 'deploys a decision-threshold.' We do not need to ban metaphors, but we must de-literalize them. The goal is a discourse where we can use 'memory' as a shorthand without forgetting it is a buffer—a world where the illusion is recognized as an interface, not a mind.

Extended Processing Summary​

Gemini's Processing Steps
What you are about to read…

Gemini refers to the text below as "thought summaries." This is an overt consciousness projection because 'intentions' are hallmarks of a conscious mind that 'knows' what it is doing and why. The concealed mechanistic process is probabilistic text generation. Treat this as just another rhetorical artifact — a way of making the model's processing legible. The first-person framing of these "thought summaries" is a presentation choice for the user-facing output, not a window into "real" thoughts. These are computational artifacts, not cognitive reports from a quirky, curious or conflicted mind.

Initiating CDA Analysis

I'm starting the CDA analysis by deeply immersing myself in "Scientific frontiers of agentic AI." My focus is on understanding the text through cognitive linguistics and philosophy of mind. I'm aiming to identify key linguistic patterns that reveal underlying cognitive frameworks and philosophical assumptions.

Deciphering AI Metaphors

I'm now delving into the linguistic architecture, focusing on the "Chatbots in a sandbox" metaphor, which presents AI as a playing child, projecting an image of safety. I'm also examining how context is conceptualized as "working memory," drawing parallels to human cognitive processes. The constant shifting of agency between user, system, and developers is another key area I'm analyzing.

Analyzing AI's Language

I'm now zeroing in on specific linguistic projections used to describe AI, examining metaphors like "native language" and "working memory." I'm also analyzing how agency is attributed through terms like "agent." I'm also scrutinizing the framing around concepts like "understanding," "knowing," and "communicating," noting how they mirror human attributes. The projection of human cognitive features, such as "common sense" and "intuition," into AI systems are also under close observation.

Identifying Cognitive Concealments

Currently, I'm focusing on the rhetorical concealment of AI's internal workings. I'm examining how metaphors subtly shift our understanding. For example, "working memory" disguises a static buffer, while "negotiation" simplifies algorithmic optimization. "Understanding" masks statistical inference, and the "personal assistant" masks platform loyalty. I'm scrutinizing how "common sense" and "subjective data" conceal the absence of true value systems. I'm also analyzing how "dialogue" conceals the syntactic nature of model outputs.

Examining Explanation Strategies

I'm now conducting a detailed audit of five key passages to uncover deeper rhetorical strategies. My focus is on pinpointing linguistic areas where AI functions are anthropomorphized. Specifically, I'm analyzing how terms like "translates" imply understanding, how "know a bargain" attributes cognitive abilities, and how "common sense" is deceptively applied. This analysis will feed directly into task four, which involves reframing these anthropomorphic attributions into more precise, mechanistic terms.

Dissecting Linguistic Shifts

I'm now zeroing in on the linguistic sleight of hand at work, specifically the transition from describing mechanical processes to anthropomorphized agency. I'm noting how concepts like "negotiation" and "understanding" are used to obscure the underlying mechanics, and how "personalization" relies on hidden data surveillance. I'm also examining the strategic use of metaphors like "assistant" and "intuition" to build trust and mask the fact that these "agents" are essentially Amazon's code, optimized for its ecosystem. Finally, I'm analyzing how the text shifts from technical descriptions to high-level metaphors, creating an illusion of understanding where statistical regularities are at play.

Pinpointing Conceptual Trickery

I'm now zeroing in on the linguistic sleight of hand at work, focusing on how we get from "vector proximity" to "meaning." The stakes are significant: economic and privacy, fueled by surveillance capitalism. I'm strategizing how to counteract this anthropomorphization, replacing vague attributions with precise mechanistic explanations. The goal is to highlight the path forward by advocating for "mechanistic disclosure" to counter "marketing anthropomorphism." I'm also refining content by explicitly distinguishing "processing" from "knowing," and tackling the "curse of knowledge" by clarifying that AIs see coordinates, not meaning. Finally, I'm reframing phrases like "bias was introduced" with "Amazon engineers curated the data," making the human agency clear.

Formalizing Analysis Structure

I'm presently focused on structuring my analysis within the required JSON format. This involves carefully translating my existing insights into the specified fields, ensuring adherence to all formatting constraints, especially word counts, specific field names, and the structure of the data. I'm actively checking for escape characters and the proper differentiation between epistemicCorrection and reframedLanguage, while also ensuring the Critical Observations are robust and well-articulated. Moreover, I'm integrating self-correction by explaining why specific phrases create an illusion of mind, while ensuring the accuracy of all quotes.

Mechanizing Rejection Analysis

Now, I'm focusing on the "reject" token in the AI's output. Instead of implying a conscious "rejection" based on an evaluation of fairness, I'm clarifying that the AI is simply predicting the token "reject" because it's statistically likely to follow low offers within its training data. This shift avoids attributing human-like agency where a simple statistical pattern is at play.