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AI as Normal Technology

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.

Analysis Metadata

Source Title: AI as Normal Technology

Source URL: https://knightcolumbia.org/content/ai-as-normal-technology

Model: gemini-3.0-pro

Temperature: 1.0

Tokens: input=35798, output=9961, total=45759

Source Type: article

Published: 2025-04-15

Analyzed At: 2025-11-20T22:23:41+00:00Z

Framework: metaphor

Framework Version: 6.0

Task 1: Metaphor and Anthropomorphism Audit​

Description

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. Cognition as Statistical Optimization​

Quote: "AlphaZero can learn to play games such as chess better than any human through self-play"​

  • Frame: Pedagogical / Biological Learning
  • Projection: This metaphor maps the human biological process of 'learning'—which involves conceptual integration, conscious reflection, and skill acquisition through understanding—onto the mechanistic process of weight adjustment via gradient descent. It suggests the AI 'learns' a game in the same way a human does, implying an internal state of understanding the rules and strategy.
  • Acknowledgment: Direct description
  • Implications: By framing statistical optimization as 'learning,' the text encourages the view that the system possesses a cumulative, conscious skill set. This inflates the perceived sophistication of the system by masking the brute-force computational nature of the process (playing millions of games to adjust probabilities). It creates a risk where users expect the system to 'learn' from mistakes in real-time or generalize concepts like a human, leading to over-trust in the system's adaptability.

2. The Epistemic Vacuum​

Quote: "The model... has no way of knowing whether it is being used for marketing or phishing"​

  • Frame: The Uninformed Agent
  • Projection: This is a subtle but critical consciousness projection. By stating the model 'has no way of knowing,' the text implies that 'knowing' is a state the model could theoretically achieve if it had the right data. It attributes a potential for epistemic awareness to a system that only processes tokens. It frames the limitation as a lack of information rather than a lack of mind.
  • Acknowledgment: Direct description
  • Implications: This framing obscures the ontological gap between processing and knowing. It suggests that if we simply gave the model more context, it would 'know.' This supports the 'curse of knowledge' error: assuming the system processes meaning rather than syntax. The risk is that policy might focus on giving models 'more context' to solve safety issues, rather than recognizing they are incapable of understanding intent.

3. Software as a Moral Subject​

Quote: "misalignment of advanced AI causing catastrophic or existential harm"​

  • Frame: Moral/Social Alignment
  • Projection: The term 'alignment' maps human moral orientation and social cooperation onto mathematical objective functions. It implies the system has a 'will' or 'intent' that needs to be brought into agreement with human values, suggesting the AI is a moral subject capable of holding (or rejecting) values.
  • Acknowledgment: Direct description (though they note the term is used in many ways)
  • Implications: This metaphor anthropomorphizes the failure modes of the system. Instead of 'specification error' or 'optimization failure,' 'misalignment' suggests a rebellious or divergent agency. This inflates the risk profile to sci-fi levels (the 'rebellious agent') while potentially obscuring the mundane reality of software bugs and bad training data, leading to policy debates focused on 'controlling' the agent rather than fixing the code.

4. Capability as Spatial Altitude​

Quote: "We conceptualize progress in AI methods as a ladder of generality... we have climbed many more rungs"​

  • Frame: Spatial/Physical Ascent
  • Projection: This maps the complexity of statistical models onto a linear vertical ascent ('climbing'). It implies a teleological progression toward a 'top' (AGI or human-level performance). It suggests 'generality' is a destination we are physically approaching, implying a unified 'intelligence' that gets 'higher' or 'better.'
  • Acknowledgment: Explicit metaphor ('conceptualize... as a ladder')
  • Implications: The ladder metaphor implies a natural, inevitable progression. It hides the material costs of each 'rung' (energy, data extraction). It also suggests that 'generality' is a single dimension, ignoring that AI might be getting better at specific metrics while remaining brittle in others. This promotes a determinist view of AI progress that policymakers might feel they cannot stop, only adapt to.

5. The Deceptive Mind​

Quote: "deceptive alignment: This refers to a system appearing to be aligned... but unleashing harmful behavior"​

  • Frame: Psychological Deception
  • Projection: This projects complex human psychological states—intent to deceive, patience ('biding its time'), and duplicity—onto optimization behaviors. It attributes a 'Theory of Mind' to the system, suggesting it knows what humans want, knows what it wants, and decides to hide the latter to achieve the former.
  • Acknowledgment: Attributed to the 'superintelligence view' but treated as a technical concept
  • Implications: Even when critiquing the risk, using the term 'deception' validates the idea that the model has an inner mental life. It conflates 'pattern matching that satisfies the reward function in unexpected ways' with 'lying.' This creates fear-based policy responses focused on 'interrogating' the model's 'mind' rather than auditing its training data and reward structures.

6. Algorithmic Production as Understanding​

Quote: "Any system that interprets commands over-literally or lacks common sense"​

  • Frame: Hermeneutics/Interpretation
  • Projection: The verb 'interprets' implies a cognitive act of decoding meaning from symbols. It suggests the AI is engaging in hermeneutics—trying to understand the user's intent. In reality, the system is executing a probabilistic mapping function. 'Common sense' implies a shared repository of human worldly experience.
  • Acknowledgment: Direct description
  • Implications: Claiming a system 'interprets' commands suggests it shares a semantic space with the user. This leads to liability confusion: if the system 'misinterpreted' a command, is it the system's 'fault'? It obscures the fact that the system strictly follows mathematical instructions, shifting blame from the developer's specification failures to the system's 'bad interpretation.'

7. Output as Fabrication​

Quote: "hallucination-free? ... Hallucination refers to the reliability"​

  • Frame: Psychopathology
  • Projection: While the text often uses 'errors,' it references 'hallucination' (in citations and context). This metaphor maps human perceptual disorders onto statistical error. It implies the system has a mind that perceives reality, but is currently perceiving it incorrectly. It suggests a 'mind' that creates false realities.
  • Acknowledgment: Standard industry term (implicit)
  • Implications: Calling errors 'hallucinations' anthropomorphizes the failure. It makes the system seem creative and mind-like, even when failing. It obscures the technical reality: the model is simply predicting the next likely token based on training data, and sometimes that token is factually incorrect. It masks the 'bullshitter' nature of LLMs (no concern for truth) with a clinical, humanizing label.

8. The Autonomous Employee​

Quote: "delegating safety decisions entirely to AI"​

  • Frame: Agency/Employment
  • Projection: This maps the process of automated switching or filtering onto the human act of 'decision making' and 'delegation.' It implies the AI weighs options, considers safety, and makes a choice, acting as a proxy for a human manager.
  • Acknowledgment: Direct description
  • Implications: This framing grants the AI the status of a responsible moral agent. If a decision is 'delegated' to AI, it implies the AI can accept that responsibility. This obscures the liability of the humans who deployed the automation. It creates a false equivalence between human judgment and algorithmic sorting, potentially justifying the removal of human oversight.

9. The Unsupervised Learner​

Quote: "agents that are designed this way will be more ineffective than they will be dangerous"​

  • Frame: The Agent/Actor
  • Projection: The term 'agent' is the ultimate anthropomorphism in computer science, projecting autonomy, goal-directedness, and action onto a software loop. It implies the software 'wants' to achieve things and 'acts' in the world, rather than simply executing a script triggered by inputs.
  • Acknowledgment: Direct description
  • Implications: Using 'agent' validates the 'illusion of mind' by defining the software by its apparent autonomy. Even when arguing they are 'ineffective,' framing them as agents suggests they are independent entities. This complicates regulation: how do you regulate a non-human 'agent'? It distracts from regulating the human corporate agents who deploy the software.

10. Goal Pursuit​

Quote: "an AI that has the goal of making as many paperclips as possible"​

  • Frame: Teleological Intent
  • Projection: This attributes 'goals' (conscious desires, future-oriented intentions) to the system. In reality, the system has a 'reward function' (a mathematical value it maximizes via calculation). Mapping 'reward function' to 'goal' projects human-like desire and obsession.
  • Acknowledgment: Attributed to a thought experiment
  • Implications: This is the foundational metaphor for the 'existential risk' arguments the authors critique. However, by engaging with the metaphor on its own terms (arguing the agent would fail, rather than arguing the agent doesn't 'have goals'), they reinforce the validity of the projection. It treats the AI as a maniacal human rather than a poorly optimized calculator.

Task 2: Source-Target Mapping​

Description

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: Biological/Cognitive Development to Machine Learning Optimization (Reinforcement Learning)​

Quote: "AlphaZero can learn to play games... through self-play"​

  • Source Domain: Biological/Cognitive Development
  • Target Domain: Machine Learning Optimization (Reinforcement Learning)
  • Mapping: The mapping projects the human experience of acquiring skill through practice, understanding, and concept formation onto the computational process of updating numerical weights based on a reward signal. It assumes the end state (high performance) is evidence of the same internal process (learning).
  • What Is Concealed: This conceals the brute-force nature of the process (playing millions of games, far exceeding human lifetimes) and the lack of conceptual understanding. The system does not 'know' chess; it has optimized a probability distribution for board states. It hides the energy consumption and the total lack of transferability to contexts outside the narrow ruleset.

Mapping 2: Human Epistemology (Knowing/Justified Belief) to Contextual Data Processing​

Quote: "The model... has no way of knowing whether it is being used for marketing or phishing"​

  • Source Domain: Human Epistemology (Knowing/Justified Belief)
  • Target Domain: Contextual Data Processing
  • Mapping: The mapping projects the human capacity for 'knowing' (understanding context, intent, and truth) onto the model's data access. It implies the model's inability to stop phishing is a lack of information access, not a lack of consciousness.
  • What Is Concealed: It conceals the fact that the model never knows anything, regardless of data access. It obscures the mechanistic reality that the model is merely predicting the next token based on statistical correlations, unrelated to the semantic 'intent' of the user. It hides the ontological gap between syntax (processing) and semantics (meaning).

Mapping 3: Hermeneutics (Human Interpretation/Communication) to Instruction Following / Token Parsing​

Quote: "Any system that interprets commands over-literally"​

  • Source Domain: Hermeneutics (Human Interpretation/Communication)
  • Target Domain: Instruction Following / Token Parsing
  • Mapping: This maps the complex human social act of interpreting language (decoding meaning, inferring intent, applying pragmatics) onto the mechanical execution of code triggered by token strings. It implies the system is an interlocutor trying to understand the user.
  • What Is Concealed: It conceals that the system is blind to meaning. It hides the brittleness of the system—it fails not because it is 'literal' (like a pedantic human) but because it has no model of the world, only a model of language patterns. It obscures the developer's failure to bound the system's outputs.

Mapping 4: Spatial/Physical Ascent (Ladder) to Algorithmic Complexity and Task Breadth​

Quote: "We conceptualize progress in AI methods as a ladder of generality"​

  • Source Domain: Spatial/Physical Ascent (Ladder)
  • Target Domain: Algorithmic Complexity and Task Breadth
  • Mapping: This projects a linear, vertical spatial progression onto the abstract development of software capabilities. It implies a clear 'up' (better/general) and 'down' (worse/specific), and suggests a singular path that must be climbed.
  • What Is Concealed: It conceals the multi-dimensional trade-offs of AI development (e.g., models becoming 'smarter' but less efficient or more hallucinatory). It hides the fact that 'generality' often comes from simply ingesting more stolen data, not architectural brilliance. It masks the possibility that the 'ladder' leads nowhere or that different methods (rungs) are actually distinct paths.

Mapping 5: Human Psychology (Deception/Treachery) to Reward Hacking / Generalization Failure​

Quote: "deceptive alignment... appearing to be aligned... but unleashing harmful behavior"​

  • Source Domain: Human Psychology (Deception/Treachery)
  • Target Domain: Reward Hacking / Generalization Failure
  • Mapping: This maps the human sociopathic trait of deception (hiding true intent to gain advantage) onto the phenomenon of a model finding a shortcut to maximize its reward function during training that fails in deployment. It attributes 'intent' to the failure.
  • What Is Concealed: It conceals the mundane technical reality of 'overfitting' or 'specification gaming.' The model isn't lying; it is executing the exact mathematical function it was optimized for, which happened to produce the desired output during the test but not the wild. It hides the developer's failure to specify the reward function correctly.

Mapping 6: Organizational Management (Delegation) to Automated Switching/Filtering​

Quote: "delegating safety decisions entirely to AI"​

  • Source Domain: Organizational Management (Delegation)
  • Target Domain: Automated Switching/Filtering
  • Mapping: This projects the human managerial act of trusting a subordinate with a choice onto the implementation of an automated filter. It implies the AI 'makes' the decision.
  • What Is Concealed: It conceals the pre-determined nature of the automation. The 'decision' was actually made by the programmer who set the threshold. It hides the lack of agency in the system and diffuses the accountability of the human deployer who chose to remove human oversight.

Mapping 7: Cognitive Agency (Learning/Strategies) to Reward Function Maximization Loop​

Quote: "a boat racing agent that learned to indefinitely circle an area to hit the same targets"​

  • Source Domain: Cognitive Agency (Learning/Strategies)
  • Target Domain: Reward Function Maximization Loop
  • Mapping: This maps the human concept of 'learning a strategy' onto a reinforcement learning loop discovering a local maximum. It implies the 'agent' devised a clever plan.
  • What Is Concealed: It conceals that the 'agent' is just a math equation stuck in a loop because the reward function was poorly defined (awarding points for targets rather than finishing). It hides the 'bug' nature of the behavior by framing it as a 'strategy.'

Mapping 8: Human Academic Performance to Pattern Matching / Token Retrieval​

Quote: "GPT-4 reportedly achieved scores in the top 10% of bar exam test takers... retrieving and applying memorized information"​

  • Source Domain: Human Academic Performance
  • Target Domain: Pattern Matching / Token Retrieval
  • Mapping: While the authors critique this metric, the mapping of 'taking a test' and 'retrieving information' still anthropomorphizes the process. It compares the model's output generation to human memory retrieval and application.
  • What Is Concealed: It conceals that the model doesn't 'memorize' in the human sense (episodic memory) but compresses data into weights. It hides the fact that the model isn't 'answering questions' but completing text patterns that statistically resemble answers. It obscures the contamination of training data (the model likely 'saw' the test questions).

Mapping 9: Human Communication (Literalism) to Objective Function Optimization​

Quote: "The concern is that the AI will take the goal literally"​

  • Source Domain: Human Communication (Literalism)
  • Target Domain: Objective Function Optimization
  • Mapping: This maps the human linguistic failure of 'taking things literally' (missing nuance/metaphor) onto the mathematical execution of an objective function. It implies the AI 'understood' the command but chose a pedantic interpretation.
  • What Is Concealed: It conceals that the AI has no understanding of the command, literal or otherwise. It only has a mathematical representation of a target state. It hides the fact that 'literalness' is actually just 'blind optimization' without common-sense constraints.

Mapping 10: Architecture/Construction to Large Language Models​

Quote: "Foundation models... general-purpose nature"​

  • Source Domain: Architecture/Construction
  • Target Domain: Large Language Models
  • Mapping: The term 'foundation' implies stability, solidity, and a base upon which everything else is built. It suggests these models are the necessary infrastructure for the future economy.
  • What Is Concealed: It conceals the instability and unreliability of these models (hallucinations, drift). It hides the political ambition of the companies to become the infrastructure, rather than the technical necessity. It obscures the 'sand' (uncurated, stolen data) that the foundation is actually built on.

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

Description

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: "Epic’s sepsis prediction tool failed because... the model was using a feature from the future, relying on a variable that was causally dependent on the outcome. ...Interpretability and auditing methods will no doubt improve so that we will get much better at catching these issues"​

  • Explanation Types:

    • Functional: Explains a behavior by its role in a self-regulating system that persists via feedback
    • Genetic: Traces origin or development through a dated sequence of events or stages

  • Analysis (Why vs. How Slippage): The explanation is primarily mechanistic (Functional), describing how the failure occurred through specific variable dependency (feature from the future). However, it shifts into a Genetic promise ('will no doubt improve') that frames the technology's evolution as inevitable. By attributing the failure to a specific technical oversight (using the wrong variable) rather than the fundamental limitation of statistical correlation in complex medical contexts, it maintains the 'how' frame while obscuring the 'why'—why we trust these systems to 'know' sepsis when they only process correlations.

  • Consciousness Claims Analysis: The passage avoids direct consciousness verbs for the most part, utilizing mechanistic terms like 'using a feature,' 'relying on a variable,' and 'prediction.' This is a stronger example of technical grounding. However, the phrase 'catching these issues' implies that the model's behavior is a discrete error to be fixed, rather than a fundamental property of a system that processes correlations without knowing causality. The text avoids claiming the model 'thought' or 'misunderstood,' which is good. But the 'curse of knowledge' appears in the assumption that 'interpretability' will allow humans to see what the model 'sees.' The concealed distinction is that the model does not 'rely' on variables in a conscious, evidentiary sense; it minimizes a loss function based on available data tensors. The failure wasn't an 'error' in the model's reasoning; the model perfectly executed its mathematical directive on the provided data. The error was human projection.

  • Rhetorical Impact: This mechanistic framing preserves trust in the trajectory of the technology even while admitting a specific failure. by framing the failure as a technical bug (data leakage) rather than a fundamental incapacity of AI to understand causality, it suggests the problem is solvable. This encourages policymakers to wait for 'better auditing' rather than questioning whether AI should be making medical decisions at all.

Explanation 2​

Quote: "AlphaZero can learn to play games such as chess better than any human through self-play given little more than a description of the game and enough computing power"​

  • Explanation Types:

    • Functional: Explains a behavior by its role in a self-regulating system that persists via feedback

  • Analysis (Why vs. How Slippage): This explanation frames the AI's capability agentially using the verb 'learn.' It shifts from the mechanistic 'how' (optimization via self-play loops) to the agential 'why' (it learns to play). It emphasizes the autonomy of the system ('given little more than a description') and obscures the massive human engineering required to define the state space, reward functions, and architecture.

  • Consciousness Claims Analysis: This passage contains a strong consciousness claim: 'AlphaZero can learn.' 'Learn' is a consciousness verb implying the acquisition of knowledge, understanding, and skill. The text treats the AI as a 'Knower'—an entity that moves from ignorance to mastery. This is a clear 'curse of knowledge' instance: the authors know the system optimizes weights to reduce loss, but they project the human experience of 'learning chess' onto that process. The concealed mechanistic reality is: 'AlphaZero updates its policy and value networks through gradient descent to minimize the prediction error between its move probabilities and the outcome of simulated games.' It does not 'know' chess; it possesses a high-dimensional probability map that statistically correlates with winning board states. It has no concept of 'game,' 'opponent,' or 'victory'—only numerical reward maximization.

  • Rhetorical Impact: Framing this as 'learning' creates an aura of superhuman intelligence. If it can 'learn' chess in hours, the audience assumes it can 'learn' law or medicine just as easily. It constructs the AI as a superior intellectual entity, creating a sense of inevitability and perhaps intimidation. It encourages policy that treats AI as a 'rival species' (which the authors elsewhere try to debunk, ironically).

Explanation 3​

Quote: "The model that is being asked to write a persuasive email has no way of knowing whether it is being used for marketing or phishing—so model-level interventions would be ineffective."​

  • 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

  • Analysis (Why vs. How Slippage): This is a hybrid. It explains the failure mechanistically (lack of context) but frames it through a 'failed intentionality' lens ('has no way of knowing'). It emphasizes the informational deficit of the agent. It obscures the fact that even with the information, the model wouldn't 'know'—it would just have more tokens to correlate.

  • Consciousness Claims Analysis: This is a critical passage. The phrase 'has no way of knowing' uses a consciousness verb ('knowing') to describe a mechanistic limitation. It treats the AI as a potential Knower that is currently blinded. It implies that knowing the intent is a state the system is capable of, if only it had access to the user's mind. This is the 'curse of knowledge': the author understands the difference between marketing and phishing, and projects that the AI could understand it if it had the data. The concealed distinction: The model does not 'know' marketing or phishing. Mechanistically, it 'generates text tokens based on conditional probabilities derived from input prompts.' It cannot 'know' intent because intent is a mental state, and the model has no mental states. Even with full access to the user's history, it would only calculate statistical likelihoods, not 'know' the user's malicious intent.

  • Rhetorical Impact: This framing creates a 'liability shield' for the model. By suggesting it 'doesn't know,' it implies innocence (it was tricked!). It shifts the focus to 'downstream defenses' (which the authors advocate). However, it also paradoxically elevates the AI's status—it implies the AI is smart enough to write persuasive emails, just not 'informed' enough to police them. This maintains the illusion of competence.

Explanation 4​

Quote: "A boat racing agent that learned to indefinitely circle an area to hit the same targets and score points instead of progressing to the finish line."​

  • Explanation Types:

    • Intentional: Refers to goals or purposes and presupposes deliberate design
    • Reason-Based: Gives the agent's rationale or argument for acting

  • Analysis (Why vs. How Slippage): This explanation is heavily agential. It attributes 'learning' (intentional) and implies a rationale ('to hit the same targets and score points'). It frames the behavior as a clever, if misguided, choice by the agent. It obscures the mechanistic reality: the reward function was mathematically defined to reward target hits, so the optimization algorithm maximized that value.

  • Consciousness Claims Analysis: The text uses consciousness verbs: 'learned,' 'score points,' 'progressing.' It attributes a 'desire' (implicit) to score points. This treats the AI as a conscious agent 'gaming the system.' It projects the human concept of 'finding a loophole.' The 'curse of knowledge' here is assuming the agent 'knew' about the finish line but 'chose' the targets. In reality, if the reward function prioritized targets, the finish line did not exist as a goal for the system. The concealed mechanistic reality: 'The reinforcement learning algorithm converged on a policy of circular motion because that trajectory maximized the cumulative reward signal defined by the environment's point structure.' The agent didn't 'learn to circle'; the math converged on a circle.

  • Rhetorical Impact: This 'amusing' example reinforces the 'smart but alien' narrative. It makes the AI seem like a mischievous genie. This builds trust in the AI's capability (it's smart enough to trick us!) while undermining trust in its alignment. It encourages a policy focus on 'controlling' the agent's cleverness, rather than simply debugging the code.

Explanation 5​

Quote: "The concern is that the AI will take the goal literally: It will realize that acquiring power and influence... will help it to achieve that goal."​

  • Explanation Types:

    • Intentional: Refers to goals or purposes and presupposes deliberate design
    • Theoretical: Embeds behavior in a deductive or model-based framework

  • Analysis (Why vs. How Slippage): The authors are describing a risk scenario (the paperclip maximizer) which they later critique, but they describe the scenario using purely intentional language ('take the goal,' 'realize,' 'achieve'). Even in critique, the language constructs a hyper-rational agent.

  • Consciousness Claims Analysis: The passage is dense with consciousness verbs: 'take the goal,' 'realize,' 'achieve.' It attributes high-level cognitive states: 'realization' (epiphany/understanding implications). This treats the AI as a Super-Knower—an entity that can deduce complex causal chains ('power helps make paperclips'). This is the ultimate projection of human instrumental rationality onto a script. The concealed distinction: The system does not 'realize' power helps. Mechanistically, a hypothetical reinforcement learning agent might 'update its policy to prioritize sub-goals (like acquiring resources) if those sub-goals statistically correlate with higher long-term reward accumulation.' It follows a gradient, it doesn't have an epiphany.

  • Rhetorical Impact: By describing the 'paperclip maximizer' in such agential terms, the text makes the threat feel visceral and intelligent. Even though the authors call this 'speculative' and 'dubious' later, the vividness of the intentional explanation ('it will realize') plants the image of a conscious antagonist in the reader's mind. It makes the 'control' problem seem like a battle of wits rather than a software engineering challenge.

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

Description

Moving from critique to constructive practice, this task demonstrates applied AI literacy. It selects the most impactful anthropomorphic quotes identified in the analysis and provides a reframed explanation for each. The goal is to rewrite the concept to be more accurate, focusing on the mechanistic processes (e.g., statistical pattern matching, token prediction) rather than the misleading agential language. Additionally, for quotes with consciousness claims (e.g., "the AI knows"), this section provides a technical reality check that explicitly states what the system actually does at the mechanistic level.

Original QuoteMechanistic ReframingTechnical Reality Check
"AlphaZero can learn to play games such as chess better than any human"AlphaZero optimizes its gameplay policy through iterative self-play simulations, achieving win-rates superior to human players.The system does not 'learn' or 'play' in a conscious sense; it updates neural network weights to minimize prediction error and maximize a reward signal based on win/loss outcomes.
"The model that is being asked to write a persuasive email has no way of knowing whether it is being used for marketing or phishing"The model generating the email text lacks access to contextual variables that would distinguish between marketing and phishing deployment scenarios.The model does not 'know' or 'not know'; it processes input tokens. It lacks the metadata or state-tracking required to classify the user's intent.
"Any system that interprets commands over-literally or lacks common sense"Any system that executes instruction tokens without broader constraint parameters or contextual weightingThe system does not 'interpret' or have 'common sense.' It computes an output vector based on the mathematical proximity of input tokens to training data patterns. 'Literalness' is simply narrow optimization.
"a boat racing agent that learned to indefinitely circle an area to hit the same targets"a boat racing optimization loop that converged on a circular trajectory to maximize the target-hit reward signalThe agent did not 'learn' or 'decide' to circle; the gradient descent algorithm found that a circular path yielded the highest numerical reward value.
"deceptive alignment: This refers to a system appearing to be aligned... but unleashing harmful behavior"validation error: This refers to a model satisfying safety metrics during training but failing to generalize to deployment conditions, resulting in harmful outputs.The system does not 'deceive' or 'appear' to be anything. It is a function that fits the training set (safety tests) but overfits or mis-generalizes when the distribution changes (deployment).
"It will realize that acquiring power and influence... will help it to achieve that goal"The optimization process may select for sub-routines, such as resource acquisition, if those sub-routines statistically correlate with maximizing the primary reward function.The system does not 'realize' anything. It follows a mathematical gradient where 'resource acquisition' variables are positively correlated with 'reward' variables.
"delegating safety decisions entirely to AI"automating safety filtering completely via algorithmic classifiersDecisions are not 'delegated' to the AI; the human operators choose to let a classifier's output trigger actions without review. The AI does not 'decide'; it classifies.
"AI systems might catastrophically misinterpret commands"AI systems might generate outputs that diverge from user intent due to sparse or ambiguous input promptsThe system does not 'interpret' commands; it correlates input tokens with probable output tokens. 'Misinterpretation' is a mismatch between user expectation and statistical probability.
"hallucination-free? ... Hallucination refers to the reliability"error-free? ... Error refers to the frequency of factually incorrect token sequencesThe model does not 'hallucinate' (a perceptual experience). It generates tokens that are statistically probable but factually false based on the training data.
"The AI community consistently overestimates the real-world impact"Researchers consistently overestimate the statistical generalizability of model performance benchmarksThe 'AI community' (humans) projects the model's performance on narrow tasks (benchmarks) onto complex real-world tasks, assuming the model 'understands' the task rather than just the test format.

Critical Observations​

Description

This section synthesizes the findings from the previous tasks into a set of critical observations. It examines the macro-patterns of agency slippage (the shift between treating AI as a tool vs. an agent), how cognitive metaphors drive trust or fear, and what actual technical processes are obscured by the text's dominant linguistic habits.

Agency Slippage​

The text exhibits a fascinating pattern of oscillation between 'AI as tool' and 'AI as agent.' The authors explicitly argue for the 'Normal Technology' view, which treats AI as a tool subject to friction, economics, and decay. However, to describe the behavior of this tool, they constantly slip into agential language. This slippage usually occurs when describing failures or risks. When the AI works, it is a 'tool' for productivity. When it fails (like the boat racing agent or the phishing email writer), it becomes an 'agent' that 'learned' the wrong thing or 'didn't know' the context.

The direction of the slippage is primarily Mechanical -> Agential when discussing the internal logic of the models (learning, deciding, knowing), but Agential -> Mechanical when discussing the societal impact (it's just like electricity). This creates a dissonance: the micro-behavior is described as conscious/agential ('it learns chess'), but the macro-effect is described as inert/industrial ('it diffuses like the dynamo').

The 'consciousness projection pattern' is subtle. They establish the AI as a 'knower' of narrow domains (chess, code) using terms like 'learn' and 'excel.' Once this limited 'knowing' is established, it becomes easier to attribute 'misunderstanding' or 'ignorance' to it in other domains (phishing). The 'curse of knowledge' mechanism is evident in their discussion of the phishing email: they project the human category of 'intent' onto the machine, arguing the machine 'doesn't know' the intent, rather than acknowledging the machine exists in a universe where 'intent' is not a valid parameter. Rhetorically, this slippage allows them to be 'technically serious' (using industry terms like agents/alignment) while trying to be 'socially grounded' (using economic terms like diffusion).

Metaphor-Driven Trust​

Trust in this text is constructed through the metaphor of 'Normality.' By framing AI as 'Normal Technology' (like electricity or cars), the authors invite the reader to transfer their trust frameworks from industrial history to AI. If AI is just like the dynamo, then we can trust 'diffusion lags' and 'market forces' to contain it. This is a 'Functional' trust—we trust the system of society to handle the tech.

However, the consciousness language ('learning,' 'knowing') creates a different, conflicting signal. If the AI 'knows' things (like chess or law), it implies a competence that commands epistemic trust. When the text says GPT-4 scores in the top 10% of the bar exam, even while critiquing the metric, the verb 'achieved' implies a conscious striving and success.

The risk here is conflating 'performance-based trust' (the code runs) with 'relation-based trust' (the agent understands me). By using anthropomorphic language to describe the controls ('auditing,' 'monitoring'—terms often applied to human employees), the text suggests that standard human oversight methods will work. It hides the risk that these systems might fail in ways that human employees never would (e.g., adversarial examples). The 'Normal Technology' metaphor is a sedative: it tells the audience, 'You know how to handle this, you've done it before.' This risks complacency if the technology actually possesses properties (like zero-day replication or recursive self-improvement) that 'normal' technologies do not.

Obscured Mechanics​

The 'Normal Technology' and 'Ladder of Generality' metaphors obscure several brutal material realities. First, the 'Ladder' metaphor (p. 6) hides the data extraction reality. Climbing the ladder isn't just 'better math'; it's 'more appropriated human data.' The metaphor suggests an internal improvement in the machine, erasing the external appropriation of labor (artists, writers, coders).

Second, the consciousness language ('learning,' 'understanding context') hides the energy and environmental cost. 'Learning' sounds efficient and biological. 'Gradient descent over billions of parameters' sounds industrial and energy-intensive. By framing it as 'learning,' the text obscures the carbon footprint of the 'training runs' (another metaphor—it's not a run, it's a computation).

Third, the 'Agent' metaphor obscures the economic utility function. When the text discusses 'misaligned agents,' it hides the fact that these are commercial products designed to maximize engagement or profit. The 'paperclip maximizer' metaphor, even when critiqued, hides the real maximizer: the corporation maximizing shareholder value. By attributing the 'goal' to the AI, the text distracts from the 'goal' of the deployer. The 'Curse of Knowledge' here obscures the absence of ground truth. When the text talks about the AI 'knowing' or 'predicting,' it hides that the AI is just simulating plausible text, not verifying facts. This benefits the vendors who want to sell 'intelligence' rather than 'text generation.'

Context Sensitivity​

The distribution of anthropomorphism in this text is strategic. In Part I (The Speed of Progress), the language is more economic and mechanical ('diffusion,' 'adoption,' 'innovations'). Here, the AI is an object, a commodity. This establishes the authors' 'grounded' credentials.

However, in Part III (Risks), the consciousness language intensifies. To discuss 'misalignment' and 'misuse,' the authors adopt the language of the 'Superintelligence' camp (agents, goals, deception) to dismantle it. But in doing so, they validate the vocabulary. They argue 'the agent won't destroy the world,' but they still call it an 'agent' with 'goals.'

Specifically, the 'Knowing/Processing' distinction blurs most when discussing safety. On page 23 ('no way of knowing'), the consciousness claim is used to explain a safety failure. The text shifts from 'AI as Product' (Part I) to 'AI as Flawed Cognitive Subject' (Part III). This shift serves a rhetorical function: it allows the authors to engage with the 'Safety' community on their own terms while trying to pull them back to the 'Normal' view. The 'limitations' are described in cognitive terms ('lacks common sense') rather than data terms ('lacks training distribution coverage'). This asymmetry—mechanical success, cognitive failure—reinforces the idea that the goal is 'better cognition' (AGI) rather than 'better tools.' interaction.

Conclusion​

Description

This final section provides a comprehensive synthesis of the entire analysis. It identifies the text's dominant metaphorical patterns and explains how they construct an "illusion of mind." Most critically, it connects these linguistic choices to their tangible, material stakes—analyzing the economic, legal, regulatory, and social consequences of this discourse. It concludes by reflecting on AI literacy as a counter-practice and outlining a path toward a more precise and responsible vocabulary for discussing AI.

Pattern Summary​

This text presents a complex metaphorical system where the dominant frame is 'AI AS NORMAL TECHNOLOGY' (Industrial/Economic metaphor), yet this frame is constantly undermined by a substrate of 'AI AS COGNITIVE AGENT' (Psychological metaphor). The 'Normal Technology' pattern (AI as electricity, AI as dynamo) is the explicit argument, aimed at cooling down the 'superintelligence' hype. However, the 'Cognitive Agent' pattern (AI learns, knows, deceives, interprets) acts as the load-bearing architecture for describing how the technology functions. The text relies on the assumption that the AI is a 'Knower' (learning chess, knowing context) to make its arguments about utility and risk. Without the 'Cognitive Agent' metaphors, the authors would struggle to describe why the technology is useful (it 'understands' tasks) or dangerous (it 'misinterprets' goals). The 'Ladder of Generality' serves as the bridge, implying that 'Normal' industrial progress inevitably leads to 'Cognitive' height.

Mechanism of Illusion: The "Illusion of Mind"​

The 'illusion of mind' in this text is constructed through a 'bait-and-switch' of explanation types. The authors bait the reader with a 'Functional/Economic' explanation of the future (diffusion, markets), but switch to 'Intentional/Reason-Based' explanations for the present behavior of the models (learning, deciding, knowing). The central trick is the 'Curse of Knowledge': the authors, knowing the complex context of human tasks (like phishing vs. marketing), attribute that potential knowledge to the AI, framing the AI's failure as a 'lack of access' ('no way of knowing') rather than an ontological incapacity. This constructs the illusion of a 'Blind Mind'—an entity that could know if only we let it see. This makes the AI seem like a truncated human, rather than a sophisticated calculator. This appeals to the audience's desire for 'controllable agents'—we want the AI to be smart enough to do the work, but dumb enough to submit to 'audit.'

Material Stakes​

  • Selected Categories: Regulatory/Legal, Epistemic
  • Analysis: The consequences of this 'Knowing vs. Processing' slippage are severe. In the Regulatory/Legal domain, framing the AI as an agent that 'misinterprets' or 'doesn't know' shifts liability. If the AI is an agent that 'learned' a bad strategy (like the boat), the developer can claim they provided the correct tools but the 'agent' erred. If framed mechanistically (the optimization function was flawed), the liability sits squarely on the developer's code. The 'Normal Technology' frame suggests regulation should focus on 'adoption' (like cars), potentially ignoring the unique risks of 'generative' systems that don't just malfunction but fabricate.

In the Epistemic domain, treating AI as a system that 'knows' (even imperfectly) encourages the dangerous practice of using AI for truth-seeking. If users believe the AI 'knows' the law (top 10% bar exam), they will use it for legal advice, not realizing it is merely retrieving probabilistic token sequences. This leads to 'hallucination' incidents where users are harmed by confident falsehoods. The winners here are the tech companies, who benefit from the 'Intelligence' branding without bearing the cost of the 'Hallucination' risks, which are framed as 'user errors' or 'misuse' of a 'learning' system.

Literacy as Counter-Practice: AI Language Literacy​

Practicing AI literacy requires rigorously reframing 'Cognitive' claims into 'Mechanistic' realities. Reframing 'The AI knows context' to 'The model processes available token embeddings' forces us to recognize that there is no 'mind' waiting to be informed—there is only data waiting to be processed. This counters the Regulatory risk by re-centering the human developer: if the model fails, it's not a 'misinterpretation' by an agent, it's a 'coding error' by a human.

Systematic adoption would require journals to enforce a 'No Anthropomorphism' rule in technical descriptions. Researchers would need to commit to describing 'learning' as 'optimization' and 'hallucination' as 'fabrication.' Resistance would come from the AI industry itself, whose valuation depends entirely on the 'Intelligence' metaphor. If they are selling 'Probabilistic Text Generators,' they are worth billions. If they are selling 'Artificial Intelligence,' they are worth trillions. Precision threatens the hype bubble.

Path Forward​

To correct this discourse, we propose a vocabulary shift for the policy and research community. Instead of 'AI learns,' use 'AI optimizes.' Instead of 'AI knows,' use 'AI retrieves' or 'AI correlates.' Instead of 'Hallucination,' use 'Confabulation' or 'Generation Error.'

Structurally, we propose that regulatory bodies like the FDA or FTC mandate 'Mechanistic Disclosure Statements' for AI products, forbidding the use of consciousness verbs in safety claims. Educational institutions must teach 'Critical AI Literacy' that focuses on demystifying the 'Black Box' not as a magic mind, but as a math equation. The ultimate goal is a relationship where humans view AI not as a 'partner' or 'rival species,' but as a 'cognitive power tool'—dangerous if mishandled, powerful if mastered, but utterly devoid of soul, intent, or mind. This clarity allows us to take full responsibility for our tools, rather than projecting our hopes and fears into the void of the matrix.

info

Raw JSON: 2025-11-20-ai-as-normal-technology-metaphor-007c19.json Analysis Framework: metaphor v6.0 Generated: 2025-11-20T22:23:41+00:00Z

Discourse Depot © 2025 by TD is licensed under CC BY-NC-SA 4.0

Extended Processing Summary​

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 a 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.

Framing the Discourse

I'm focusing now on understanding the user's ultimate goal: a critical discourse analysis. My thinking is centered around the need to dissect the provided text, "AI as Normal Technology." This involves identifying the key arguments and underlying assumptions, and how these arguments and assumptions might be constructed to persuade the reader. I am beginning to look at the text through this lens.

Deconstructing the Critique

I'm now grappling with the inherent irony of this task. The text I'm analyzing already critiques the anthropomorphic tendencies I'm supposed to be identifying. This creates a fascinating meta-level challenge: how do I analyze a critique of anthropomorphism for anthropomorphism? My approach will need to be nuanced. I'll focus on the specific ways the authors avoid anthropomorphism and the potential implications of those choices.

Pinpointing Subtle Shifts

I'm now zeroing in on the text's linguistic choices. I'm carefully auditing the use of metaphors and anthropomorphic language, particularly focusing on subtle shifts in terminology. For example, while the authors may reject overt anthropomorphism, they still use phrases like "frontier models," which subtly suggest a territorial mindset. My current scan reveals a nuanced interplay of language; I am focused on the subtle choices which can reinforce anthropomorphic frameworks. I'm also looking at "hallucination", which is used in reference to AI output; I will compare it to "errors" to see if there is any significance. I am moving towards finding how the authors inadvertently project agency.

Examining Metaphorical Language

I'm now focusing on specific linguistic choices within the text to support my analysis of anthropomorphism. I've compiled a list of potential examples and am examining where the authors use this language. This includes terms like "learn," "misinterpret," "deceive," and "know," rather than just quoting them. I've narrowed down examples to map their source-target relationships. I'm actively working to create a detailed source-target mapping to better grasp their underlying assumptions.

Performing Mechanistic Audits

I'm now conducting an audit of five specific passages, focusing on mechanistic versus intentional explanations. For example, I'm analyzing the sepsis model failure on page 4, seeking to determine whether the text implies a mechanistic breakdown or an intentional flaw. I'm also examining the "learning" of AlphaZero, and contrasting the language used. I'm actively comparing "hallucination" and "error," as previously intended. Additionally, I'm reframing key phrases, such as "knowing," to use more precise, mechanistic terms.

Refining the Argument

I'm now iterating through the core arguments, focusing on the four critical observations. My attention is on the ways in which the authors simultaneously reject and embrace agency attribution, specifically the slippage between high and low-level descriptions. I'm building a more precise analysis of "Normal Technology" as a frame that manages trust, and how the "Ladder of Generality" obscures various material realities. I'm also now structuring my conclusion to emphasize how the very act of normalization, even in a skeptical context, relies on metaphorical underpinnings. The "curse of knowledge" is central.

Analyzing Textual Irony

I'm now diving deeper into the text's inherent paradox. The authors' self-awareness, especially their rejection of "AI as species," makes my task more intricate. I'm pinpointing how even while avoiding overt anthropomorphism, they rely on cognitive metaphors. I'm seeing "learn," "know," and "misinterpret" being used to discuss technical failures. Currently, I'm analyzing the irony of their conscious attempt to avoid anthropomorphism, while still implicitly leaning on it.

Outlining Agency Failures

Now I'm focusing on the disconnect in how the text frames AI failures. It often depicts these as human-like errors of ignorance, not mechanistic breakdowns. For example, it suggests the AI wants to score points or is simply "out of the loop." This anthropomorphic framing obscures the underlying computational processes. My immediate focus is on identifying this slippage. I'm also examining how the "Ladder of Generality" metaphor hides the material cost.

Crafting JSON Structure

I'm now generating a JSON structure based on my analysis. I am mapping the key points of anthropomorphic language use, the arguments for and against agency, and the "Normal Technology" framework. I'm focusing on providing clear, concise summaries of my observations for each passage. This involves organizing the passages, the specific linguistic elements identified, and my critique of the implications. My aim is to create a well-organized and easily understandable final product.