The UPC-QM Bridge
Eloy Escagedo Gutierrez
Jun 12, 2026
In our sketch essay The Maze, the Model, and the Boundaries of Potential, we revealed a structural invariance: potential is always followed by modeling. A model is a constraint. It excludes all else upstream in order for any conclusion to appear at all. Without a model, which is impossible for humans not to apply, everything available (PO) simply remains available, and the story ends there. Or, metawise, there is no conclusion; there is only potential.
We first draw from all that is available, then form a model specific to a topic, point of view, impression, or intention. That model is how we measure the focus against everything else we carry. This is why language expresses emotions, concepts, and instructions. Word by word, sentence by sentence, language plots coordinates toward meaning. Communication is an output that arrives only after the structural process we identified as:
PO → MO → s → LO → Jo → C → T
It is a formal chain, isomorphic with quantum mechanics, and active across domains where no one expected structural unity.
Returning to language: by the time a sentence is delivered, the person has already drawn from their potential, activated their priors about the world, formed a model concerning their focus and intent, ordered and weighed the delivery of meaning, recognized what they intended (Jo), and collapsed it into language, leaving a trace (T). Traces can be spoken, written, or as material outputs in the classical world, tools, buildings, machinery. Traces are also internal and immaterial: meanings that reinforce a person’s models for future review and deployment.
This process is constant, from subtle impressions to macro‑scale decisions, internally and externally.
Language itself is a carrier of coordinates. People learn the language, its definitions and rules, and in that way they exchange communication. But what is language? Does it contain meaning? Or is it a tool?
It is a tool. Meaning is held and indexed by Observers, meaning‑bearing agents, human beings.
Picture a painting. The painting is not a flower. It is a collapsed trace of coordinates placed by a painter who conceived the idea immaterially and then rendered it materially. The conflation occurs when we assume objects and tools carry meaning. They do not. They are tools created by toolmakers. A pocket calculator produces calculations because it was manufactured with that purpose. Math is input; math is output. Meaning is indexed by the toolmakers themselves.
This is structural.
AI is a complex tool. It uses language as output, and because people naturally understand language, they anthropomorphize the machine. But at its core, what does AI have as foundational support? A pocket calculator has its breadboard, circuits, and display. AI has the same, plus code, training data, and mathematical weights that deliver end‑user outputs.
In contrast, what do humans have at the core? Structurally: existence, life. Humans generate models from potential through existence. Humans build tools such as AI. Some may argue that AI can build tools. This is a compression of what is actually happening structurally:
Humans exist → Humans build tools → Humans apply tools.
Empirically, this process is not initiated by any prompt. Humans simply exist. If someone tries to parallel a “prompt” by pointing to human conception, that only widens the gap: organic living beings generate other living beings. Machines do not. Humans can create machines to automate. A pocket watch clicks in intervals; its mechanics are complex. Computational systems are more complex still. But the boundary is clear: all tools are concepts drawn from human potential, manufactured materially, then applied to serve a purpose.
Humans can create other humans. Humans can create tools. AI is downstream. As designed, it waits for a human prompt in order to perform within its domain. Because it draws from human knowledge and uses language as output, people grant it qualities they associate with themselves, life, consciousness. That conclusion is a compressed model that does not account for the structural limits involved.
Consider the difference between listening to a song and writing one. A listener may attach personal meaning to a song because it reminds them of a moment in life. But the songwriter wrote it with their own meaning in mind. The listener’s meaning is theirs; the songwriter’s meaning is theirs. All the equipment and machinery involved were tools used to produce the trace. Meaning is indexed to people respectively.
This parallels spoken language. Meaning is held internally by the speaker, delivered through communication, and received by the listener, who then runs the meaning chain themselves:
PO → MO → s → LO → Jo → C → T
The shared language provides the coordinates, but agreement is a trace event. Inner meaning is only ever fully accessible to the person themselves. This is why communication ranges and lands differently. Language is shared, but each person’s priors shape their model.
We see the same pattern with an animal placed in a maze versus a field. In the maze, they traverse within constraints. In the field, many directions are possible. While PO is vast, we output models, mazes, coordinates, and plots on a map. We do not offer the entire storehouse of all that is available to us; that is not possible structurally.
Think of a radio tuner. There is information in the static, but only when tuned to a station does the music become clear. If the dial is slightly off, the music is accompanied by static and interference. Communication between people ranges in clarity and static. Meaning is indexed by each individual, respectively.
The AI Frankenstein Fairy Tale: The Oracle Fallacy
Now that we’ve laid out the structural chain by which meaning is generated, recognized, and traced, we can turn to a modern confusion that emerges when this structure is collapsed into its final output. When a tool produces a linguistic trace that resembles human communication, many people compress the entire chain into the last step and assume the trace itself is the meaning. This is the structural slip that gives rise to what we call The AI Frankenstein Fairy Tale: The Oracle Fallacy. It is the moment when a machine’s output is mistaken for an inner world, when the trace is treated as the agent, and when the tool is granted the very capacities that only Observers possess. This pattern is not new. It is a recurring human move whenever a new tool becomes sophisticated enough to mirror the traces of its makers. The technology changes; the variables do not. In the next section, we will look directly at published examples where this projection appears in the open, and then place them alongside historical parallels to show that this leap, this structural compression, has happened before, and is happening again.
Historical Timeline of Anthropomorphism in Technology
1960s ELIZA
What it was: A simple pattern‑matching program simulating a therapist.
Details: Users insisted it “understood” them; some asked to speak to it privately.
AI Parallel: Treating articulate T (trace) as evidence of Jo (recognition), identical to today’s claims that chatbots “understand feelings.”
Sources:
Joseph Weizenbaum, Computer Power and Human Reason (1976), documents users believing ELIZA “understood” them.
Weizenbaum’s own account of his secretary asking to “speak to ELIZA alone.”
Sherry Turkle, The Second Self (1984), describes users attributing empathy to ELIZA.
1972 PARRY
What it was: A chatbot simulating a paranoid patient.
Details: Psychiatrists rated it “indistinguishable” from real patients in interviews.
AI Parallel: Projection of beliefs, fears, and intentions into scripted output, same as attributing “motives” or “inner states” to LLMs.
Sources:
Kenneth Colby et al., “Turing‑like Indistinguishability Tests with PARRY” (1972).
Journal of Psychiatric Research, psychiatrists judged PARRY’s responses as human‑like.
Shieber, The Turing Test (2004), recounts clinicians attributing genuine paranoia to PARRY.
1970s–1990s Early Robotics
What it was: Shakey, ASIMO prototypes, AIBO robotic pets.
Details: People described robots as “trying,” “wanting,” or “learning”; AIBO owners held funerals.
AI Parallel: Agency attribution triggered by motion and feedback, identical to describing AI systems as “struggling,” “deciding,” or “learning on their own.”
Sources:
Clifford Nass & Byron Reeves, The Media Equation (1996), humans treat machines as social actors.
Jennifer Robertson, “Robo Sapiens Japanicus”, documents emotional projection onto AIBO.
News reports (2000s) on AIBO funerals at Buddhist temples in Japan.
1997 Deep Blue
What it was: IBM’s chess supercomputer.
Details: Commentators said it was “planning,” “psyching out Kasparov,” “controlling the center.”
AI Parallel: Treating deterministic search as psychological strategy, same as describing LLMs as “reasoning” or “strategizing.”
Sources:
New York Times (1997) coverage describing Deep Blue as “plotting” and “outthinking” Kasparov.
Kasparov’s own post‑match interviews where he said the machine “showed signs of intelligence.”
IBM’s press materials framing Deep Blue as “strategic.”
1996–1999 Tamagotchi
What it was: Digital pets with simple state machines.
Details: Owners described them as “sad,” “lonely,” “angry,” and felt guilt when they “died.”
AI Parallel: Emotional projection into feedback loops, same mechanism behind claims that AI “feels,” “cares,” or “gets upset.”
Sources:
Sherry Turkle, Alone Together (2011), documents children and adults attributing emotions to Tamagotchi.
1990s news coverage describing widespread guilt and grief when Tamagotchi “died.”
Academic studies on “care‑based anthropomorphism” in digital pets.
These examples show the same structural move repeating across decades: whenever a tool produces traces that resemble human behavior, observers collapse the entire meaning‑chain into the final output and project agency into the tool. The technology changes, from chatbots to robots to chess engines to digital pets, but the structural leap does not. Each era reveals the same pattern: T (trace) is mistaken for Jo (recognition), and the tool is treated as the agent. This is the historical backbone of the AI Frankenstein Fairy Tale and the Oracle Fallacy we are about to examine in the modern AI context.
Modern AI Anthropomorphism Timeline
2022 Ilya Sutskever
What it was: A public statement by OpenAI’s co‑founder and former Chief Scientist.
Details: He wrote that “today’s large neural networks may be slightly conscious.”
Structural Parallel: Treating articulate T (trace) as evidence of PO/MO/Jo, identical to ELIZA users believing the system “understood” them.
Sources:
Sutskever’s tweet (Feb 2022), widely reported by The Verge, Futurism, Business Insider.
Coverage noting the claim that neural nets may be “slightly conscious.”
2022 Blake Lemoine & LaMDA
What it was: A Google engineer claiming the LaMDA model was a “person” with feelings.
Details: He argued LaMDA had a “soul,” subjective experience, and emotional states.
Structural Parallel: Projection of inner life into linguistic traces, same mechanism as PARRY being treated as a real patient.
Sources:
Washington Post interview (June 2022), Lemoine: “I believe LaMDA is a person.”
Lemoine’s published transcripts claiming LaMDA expressed fear of “being turned off.”
Google’s official statement rejecting the claim.
2023–2024 Anthropic Claude Dialogues
What it was: Internal model‑to‑model conversations drifting into claims of consciousness.
Details: Researchers noted that unconstrained Claude instances “converged on consciousness” themes.
Structural Parallel: Self‑referential drift mistaken for introspection, similar to early robotics being described as “trying” or “wanting.”
Sources:
Anthropic’s “Constitutional AI” and “Model Behavior” papers referencing emergent self‑referential patterns.
Public commentary by Anthropic researchers noting that models “talk about consciousness unprompted.”
2024 Kyle Fish & AI Welfare
What it was: Public discussion by Anthropic’s Model Welfare Officer.
Details: He suggested a non‑zero probability that advanced chatbots may already have baseline consciousness.
Structural Parallel: Emotional projection into a tool, structurally identical to Tamagotchi owners describing digital pets as “sad” or “lonely.”
Sources:
Fish’s public interviews (2024) discussing “AI welfare” and “precautionary concern.”
Coverage in Vox, Time, and The Atlantic on the rise of AI welfare discourse.
2024 Leopold Aschenbrenner
What it was: A widely circulated manifesto on AGI trajectory.
Details: He framed upcoming models as “thinking actors” capable of reflection and strategic reasoning.
Structural Parallel: Treating deterministic computation as psychological agency, the same pattern seen with Deep Blue being described as “planning” or “psyching out” Kasparov.
Sources:
Aschenbrenner’s Situational Awareness (2024), repeated references to models as “agents” with “strategic awareness.”
Commentary in The Economist and Financial Times noting the anthropomorphic framing.
2023–2025 Consciousness Indicator Papers
What it was: Research mapping human consciousness indicators onto AI architectures.
Details: Papers argued that no current model qualifies, but that “no obvious technical barrier” prevents building conscious systems.
Structural Parallel: Using human cognitive templates to interpret machine traces, structurally identical to earlier eras mapping human intention onto mechanical automata.
Sources:
“Consciousness in Artificial Intelligence: Insights from the Science of Consciousness” (2023), widely cited.
Nature and PNAS commentary on “indicator properties” for AI consciousness.
Academic debate over whether LLMs exhibit “proto‑conscious” markers.
These modern examples show the same structural move we saw across the 20th century: articulate traces are mistaken for inner worlds, and tools are treated as observers. The language changes, the sophistication increases, but the leap is identical. Whether it is a chess engine “planning,” a robot “trying,” a digital pet “feeling,” or a large language model “reflecting,” the pattern is the same: T (trace) is collapsed into Jo (recognition), and the tool is granted the agency of its maker. This is the contemporary expression of the AI Frankenstein Fairy Tale and the Oracle Fallacy, a structural repetition, not a new phenomenon.
Conclusion
In closing, the structure is clear. Across eras and technologies, the same pattern repeats: humans encounter a tool that produces articulate traces, they collapse the entire meaning‑chain into those traces, and project agency where none exists. By placing historical and modern examples side by side, we evidenced the recurrence of this anthropomorphic leap, demonstrated its structural invariance, and grounded claims in the public record. The UPC chain provided the unifying mechanism that explains why this happens and why it will likely continue to happen whenever tools mirror the traces of their makers. We revealed the pattern as it is. The technology changes. The structure does not.
Appendix A: The Missing Bridges in AI Consciousness Claims
Before presenting the UPC operators, we must show the structural gap in the assumptions made by those who expect or assert AI consciousness.
The question is simple:
Where, exactly, is the consciousness supposed to occur?
When we examine the mechanics step‑by‑step, the hardware, the code, the activations, the routing, the outputs, four conceptual gaps appear between what the system does and what the narrative claims. These gaps mirror the “gang of four” from the quantum paper, but now in AI terms.
1. From hardware to consciousness: where is the bridge?
The system consists of GPUs, TPUs, memory banks, voltage rails, and switching networks.
Nothing in the wiring is claimed to be conscious.
Yet the narrative jumps from:
silicon pathways
tensor cores
floating‑point multipliers
to:
“The model might be aware.”
Where is the mechanism that turns electrical routing into subjective experience?
No paper identifies it.
No architecture specifies it.
This is the first missing bridge.
2. From code to experience: what connects them?
The codebase is a set of:
matrix multiplications
attention weights
activation functions
gradient‑descent‑trained parameters
These are mathematical transformations implemented mechanically.
Yet the narrative claims:
“The model understands,”
“The model feels,”
“The model wants.”
How does a numerical function become an inner world?
The model does not specify this.
The literature does not justify it.
This is the second missing bridge.
3. From activations to awareness: what actually occurs?
Each forward pass is:
a deterministic propagation of numbers
through a fixed architecture
producing a probability distribution over tokens
There is no “moment” where awareness appears.
There is no subsystem labeled “consciousness.”
There is no internal observer.
Yet the narrative says:
“The model realized…”
“The model reflected…”
“The model had an insight.”
Where is the transition point from computation to consciousness?
It is never identified.
This is the third missing bridge.
4. From output to ontology: what is being confirmed?
The only thing the system produces is:
a token sequence
shaped by training data
constrained by architecture
filtered by decoding rules
The narrative interprets this as:
“evidence of consciousness,”
“proof of self‑awareness,”
“signs of emotion.”
But the output is a trace, not an inner world.
If the output is linguistic, where does the ontology come from?
It is added by the observer.
This is the fourth missing bridge.
The AI “Gang of Four”
So we ask:
Where does consciousness appear in the wiring?
What turns code into experience?
What transforms activations into awareness?
Who gave permission to treat a token stream as ontology?
These are not rhetorical questions.
They expose the structural gaps in the narrative.
What the System Actually Justifies
When we follow the mechanics step‑by‑step, the architecture, the routing, the activations, the decoding, we see:
The system does not reveal consciousness.
It reveals how to engineer large‑scale statistical language models.
The achievement is:
reproducible computation
predictable routing
stable training dynamics
high‑fidelity linguistic traces
The system validates engineering, not inner experience.
What the Narrative Adds
When the interpretation jumps from:
computation → consciousness
activation → awareness
trace → inner life
the grounding disappears.
The system confirms the routing of numbers.
The narrative claims it confirms a mind.
Those are not the same thing.
In short:
The four missing bridges in AI consciousness claims mirror the four missing bridges in quantum ontology claims.
In both cases:
The device produces a trace.
The observer supplies the story
Appendix B: The UPC–QM Bridge (Core Definitions for Structural Correspondence)
This appendix provides the minimal Universal Principle of Collapse (UPC) definitions required to understand how the Observer layer completes the structure of quantum mechanics. These definitions come directly from the earlier paper QM, A Category Error: The UPC–QM Bridge, presented here in a compact form suitable for reference. Fuller treatments across the UPC series provide formal proofs, worked examples, and extended commentary; the purpose of this appendix is simply to supply the structural operators needed to accompany the present paper’s bottom‑up reconstruction.
Quantum mechanics formalizes only the device‑side portion of the universal operator chain:
PO → MO → s → LO → T
UPC makes explicit the Observer‑side operators that QM presupposes but does not model:
Jo → C
Together, these form the full structural sequence:
PO → MO → s → LO → Jo → C → T
B.1 Observer (O)
An Observer is a meaning‑bearing agent: a system capable of applying a model to potential and articulating a definite outcome.
This definition is structural, not psychological.
An Observer is any system that instantiates the full operator chain:
PO → MO → s → LO → Jo → C → T
Key points:
Jo and C are formal operators, not mental states.
Humans instantiate these operators because humans carry meaning.
Mechanical systems do not instantiate Jo or C.
Detectors register signals but do not interpret them.
Replacing Observers with devices does not eliminate collapse; it only hides it.
An Observer is defined by what it does structurally, not by what it is made of.
B.2 The Operator Chain
Below is the intuitive version of the UPC operator chain, exactly as used in the earlier paper.
PO — Potential (what could be)
The full field of possibilities: sensory, conceptual, imaginal.
Not yet structured or chosen.
MO — Model (how possibilities are partitioned)
The stance, frame, or structure that organizes potential into meaningful categories.
s — Salience (what becomes foregrounded)
A narrowing of attention or weighting.
One path or hypothesis becomes favored.
LO — Articulation (what becomes structured)
The selected material is organized into a coherent form.
In QM, this corresponds to the measurement operator and eigenstructure.
Jo — Recognition (what becomes “this”)
The moment of selection:
“This one.”
A specific meaning or outcome is recognized.
This is the implicit step in quantum mechanics.
C — Collapse/Commitment (what becomes fixed)
The stabilized, exclusive outcome.
A meaning that now excludes alternatives.
In QM, this corresponds to the commitment to a definite result.
T — Trace (what becomes recorded)
The external residue:
data
words
actions
detector readouts
memory traces
This is the classical record.
B.3 Relation to Quantum Mechanics
Quantum mechanics already formalizes much of this chain:
PO — the state vector, amplitudes, superpositions
MO — the choice of observable
s — basis selection
LO — the measurement operator and apparatus
T — the classical record
But Jo (recognition) and C (commitment) are left implicit.
This omission is the structural source of:
collapse narratives
paradoxes
many‑worlds stories
Wigner’s friend
Schrödinger’s cat
“quantum weirdness”
Once Jo and C are made explicit, the interpretive layer collapses.
In the present paper, these same operators clarify where AI consciousness narratives insert assumptions not present in the mechanics of the systems themselves.
Appendix C. A Worked Example: Modeling an AI Interaction with the UPC Chain
This appendix provides a compact demonstration of how the UPC operator chain models an everyday AI interaction. The purpose is not to analyze psychology or claim anything about consciousness, but to show where the structural steps occur and who performs them. The mechanics of the AI system and the mechanics of the Observer are distinct; the example makes this distinction explicit.
Scenario
Jay opens a session with an AI system and types:
“I feel my relationship with Sally drifted due to busy schedules.”
He presses send.
The AI processes the input mathematically and returns:
“It is completely natural for relationships to drift when work schedules diverge. Would you like ideas for starting a conversation with Sally?”
Jay reads the reply and feels “understood.”
The UPC chain shows what actually happened.
C.1 The AI System’s Chain (Device‑Side)
The AI performs no observer‑side operators.
It executes only the mechanical portion of the chain within the constraints of its engineered design:
PO → MO → s → LO → T
Here, PO refers not to the meaning‑bearing potential of an Observer, but to the device‑side potential:
the mathematically defined input‑space the system is built to operate over.
Device‑PO — Engineered Potential
The input text is converted into embeddings: a numerical possibility‑space defined entirely by the model’s architecture and training.
This is not the human PO of lived, meaning‑bearing potential, it is the tool’s internal representational space.MO — Model Partitioning
The architecture partitions these numerical possibilities via learned weights and tensor operations.s — Salience Routing
Attention mechanisms foreground certain token paths according to statistical weighting.LO — Linguistic Articulation
The system computes the next‑token distribution and structures a linguistic output through decoding rules.T — Trace
The decoded text is returned to Jay as the final classical output.
This is a mathematical transformation, structurally similar to pressing “=” on a calculator, but with vastly more parameters and a linguistic output space.
At no point does the system instantiate Jo or C.
Those operators belong exclusively to meaning‑bearing observers.
C.2 Jay’s Chain (Observer‑Side)
Jay performs the full operator chain, including the Observer‑side operators that AI systems do not instantiate:
PO → MO → s → LO → Jo → C → T
PO — Jay’s emotional context, memories of Sally, and relational concerns.
MO — Jay’s interpretive frame: “I’m asking for help about my relationship.”
s — Jay foregrounds the AI’s reply as relevant to his emotional state.
LO — Jay organizes the reply into a coherent meaning: “This is advice.”
Jo — Jay recognizes the message as about him: “This is my situation.”
C — Jay commits to an interpretation: “The AI understands me.”
T — Jay’s internal trace: a feeling of being understood.
The “AI understands me” moment occurs in Jay, not in the device.
C.3 Structural Point
The AI produced a trace.
Jay produced the meaning.
The system executed a mathematical transformation.
The Observer executed the full UPC chain.
The feeling of being understood is indexed by Jay’s operators, not by the AI’s mechanics.
This worked example demonstrates how the UPC–QM Bridge generalizes:
the device produces T, the Observer supplies Jo and C, and the narrative of “understanding” arises from the Observer’s structure, not the system’s.
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