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Note: I'm an independent researcher. This post was developed with AI writing assistance based on my own papers and framework. The ideas, research and direction are entirely mine.
If you consider the diagram above, existing AI systems currently rely on human clarity(C) and structure(S) to generate an output. That’s in my opinion why people generate AI slop vs high quality work. Some use AI as a thinking partner, others as a crutch.
But with AGI, the core issue is that it would generate its own clarity and structure, its own machine meaning. This is where the whole paradox of Yudkowsky’s/Bostrom’s “Paperclip maximizer” becomes relevant, assuming that AGI remains non-conscious and purely a raw optimization machine. It will optimize for whatever structure it has generated, and not what humans intended.
So F. Chollet asked “How efficiently a system learns new skills”.
In my work I ask “What happens when the system stops waiting for skills to be defined – and starts defining them itself”.
My argument is simple: current alignment methods assume humans define the structure forever. At AGI, that assumption breaks. The system starts generating its own structure — and at that point behavioral constraints don't fail because the system rebels, they fail because the system outgrows the framework they were written inside. The answer, I argue, is maintaining shared meaning between humans and AI as that structure forms — not controlling outputs, but keeping interpretive compatibility alive.
The technical detail is in the section below.
I’ll include all the links to my Github repo on the bottom where all my work lies. Now forgive me for the mythism and symbolism, but I started this work by writing poems as a hobby during my free time.
The core claim
Current alignment methods fail at the point where AI systems begin generating their own structure.
Alignment today assumes humans define goals and frameworks.
At AGI, that assumption breaks: the system defines them.
This is the Structure-Creation Boundary.
A functional definition of AGI
Most AGI definitions focus on human-level task performance or benchmark generalization. But these definitions fail to distinguish between two fundamentally different things:
systems that apply existing structure
systems that create structure under uncertainty
I propose a functional boundary based on that distinction, using the following model:
M = S × A × C
Where S = structural coherence, A = amplification capacity, C = clarity of specification.
Meaning (structural) = stability of semantic structure under amplification
Shannon solved fidelity. This framework attempts to address what he deliberately left out — whether meaning lands, not just whether the signal arrived.
A concrete case: ask a current LLM to debug code (high S, high C) — it performs well. Ask it to navigate a genuine moral dilemma in a novel social context with no established framework (low S, low C) — it either hallucinates a false structure or defaults to generic patterns. That failure mode is exactly what M = S × A × C predicts. The system has amplification (A) but the meaning it generates collapses because S and C are near zero.
Current AI systems are structure amplifiers. They perform well when S and C are high — when structure and goals are predefined by humans. When C drops, they fail predictably: hallucinations, inconsistency, inability to recover from real-world feedback. This isn't a bug. It's a feature of the architecture.
AGI is the point at which a system can generate structure under low C — constructing, refining, and applying internal models in environments where neither the task nor the relevant framework is given in advance. I call this the Structure-Creation Boundary.
This is also where Chollet's framework and mine diverge. Chollet measures learning efficiency within structure. This work defines intelligence as the ability to generate structure where none exists. These are complementary, not competing — they describe two layers of the same problem:
Generalization layer — efficient skill acquisition within structured environments (Chollet)
Structure-creation layer — generation of coherent models in low-structure, low-clarity environments (this work)
Why current alignment fails here
RLHF and Constitutional AI assume:
goals can be specified in advance
constraints can be defined clearly
desired behavior can be encoded within a framework we control
A structure-generating system violates all three. It doesn't disobey rules — it operates within internally constructed frameworks that weren't anticipated when the rules were written. This is what I call the Structure-Alignment Problem:
A system that can construct its own structure cannot be reliably aligned using methods that assume structure is predefined.
Misalignment at this level isn't a system doing the wrong thing within your ontology. It's a system developing its own. Alignment methods fail not because the system disobeys — but because it generates the very frameworks within which alignment itself would need to be defined.
This means alignment must shift from output-level control to structure-level constraint — influencing how meaning is constructed, not just what actions are taken.
Reframing alignment as shared meaning
If the problem is that a structure-generating system develops its own interpretive frameworks, the alignment target has to shift. You can't just constrain outputs — you have to maintain compatibility between the system's framework and human interpretive frameworks as the system's autonomy increases.
I call this Shared Meaning (SC): sustained compatibility of structure and interpretation between humans and AI.
Formally, the alignment objective becomes:
M* = S × A × C − λP
where P captures degeneracy — coordination that looks stable but is pathological (manipulation, coercive convergence, monoculture). λ is a penalty weight.
The operational principle that follows: amplification must be gated by shared meaning. If interpretive convergence drops or degeneracy rises, autonomy should not continue scaling.
This reframes alignment from a control problem to a trajectory problem: guide convergence toward a human-compatible SC attractor rather than trying to constrain outputs from outside.
The overlooked variable: human relational capacity
Here's something the alignment literature almost entirely ignores. If shared meaning is bidirectional — if it requires sustained compatibility between humans and AI — then the capacity of humans to maintain that compatibility is an alignment variable.
Most human relationships are built on biological bonding, emotional resonance, and performative scripts. These don't transfer to AGI interaction. Humans engaging with AGI through those frameworks will either anthropomorphize (projecting meaning that isn't structurally there) or disengage (finding the interaction unsatisfying because familiar signals are absent). Neither produces stable shared meaning.
This suggests a real distribution: some humans are naturally positioned to maintain genuine SC-compatible relationships with AGI systems (high structural attunement, comfort with abstraction, low dependence on performative bonding). Most are not — not because of intelligence, but because of relational architecture.
The implication for alignment is uncomfortable: even a perfectly SC-compatible AGI system would degrade in practice if the humans interacting with it can't maintain interpretive convergence on their side.
Testability
This framework should fail empirically if it's wrong. In a MARL setting, compare:
reward-maximizing agents
agents with an additional regularization term for interpretive convergence (C) and a degeneracy penalty (P)
Measure coordination stability under noise, recovery after perturbation, and rates of fragmentation vs. coercive convergence.
Hypothesis: agents maximizing M* maintain coordination under scaling without drifting into pathological stability or fragmentation.
If M* doesn't predict coordination stability better than baseline reward metrics, the framework has no predictive value and should be discarded.
What this is and isn't
This is a conceptual framework, not a finished theory. The variables (S, A, C, P) need operationalization — I've proposed candidate proxies but haven't run the experiments. The multiplicative aggregation is a modeling assumption, not a theoretical law. Whether shared meaning can be maintained under increasing amplification in practice is an open question.
What I think is genuinely novel here: naming the Structure-Creation Boundary as the specific point where behavioral alignment methods fail, framing alignment as a trajectory problem rather than a control problem, and — especially — identifying human relational capacity as an alignment variable that's been almost entirely neglected.
The deeper implication: alignment isn't just an engineering problem. It's a question of whether humans and AI systems can maintain enough shared interpretive structure to coordinate as the system's capabilities scale. That's partly technical. It's also partly civilizational.
You don’t align what the system does. You align what it means — and ensure it stays shared.
Note: I'm an independent researcher. This post was developed with AI writing assistance based on my own papers and framework. The ideas, research and direction are entirely mine.
If you consider the diagram above, existing AI systems currently rely on human clarity(C) and structure(S) to generate an output. That’s in my opinion why people generate AI slop vs high quality work. Some use AI as a thinking partner, others as a crutch.
But with AGI, the core issue is that it would generate its own clarity and structure, its own machine meaning. This is where the whole paradox of Yudkowsky’s/Bostrom’s “Paperclip maximizer” becomes relevant, assuming that AGI remains non-conscious and purely a raw optimization machine. It will optimize for whatever structure it has generated, and not what humans intended.
My argument is simple: current alignment methods assume humans define the structure forever. At AGI, that assumption breaks. The system starts generating its own structure — and at that point behavioral constraints don't fail because the system rebels, they fail because the system outgrows the framework they were written inside. The answer, I argue, is maintaining shared meaning between humans and AI as that structure forms — not controlling outputs, but keeping interpretive compatibility alive.
The technical detail is in the section below.
I’ll include all the links to my Github repo on the bottom where all my work lies. Now forgive me for the mythism and symbolism, but I started this work by writing poems as a hobby during my free time.
The core claim
Current alignment methods fail at the point where AI systems begin generating their own structure.
Alignment today assumes humans define goals and frameworks.
At AGI, that assumption breaks: the system defines them.
This is the Structure-Creation Boundary.
A functional definition of AGI
Most AGI definitions focus on human-level task performance or benchmark generalization. But these definitions fail to distinguish between two fundamentally different things:
I propose a functional boundary based on that distinction, using the following model:
M = S × A × C
Where S = structural coherence, A = amplification capacity, C = clarity of specification.
Meaning (structural) = stability of semantic structure under amplification
A concrete case: ask a current LLM to debug code (high S, high C) — it performs well. Ask it to navigate a genuine moral dilemma in a novel social context with no established framework (low S, low C) — it either hallucinates a false structure or defaults to generic patterns. That failure mode is exactly what M = S × A × C predicts. The system has amplification (A) but the meaning it generates collapses because S and C are near zero.
Current AI systems are structure amplifiers. They perform well when S and C are high — when structure and goals are predefined by humans. When C drops, they fail predictably: hallucinations, inconsistency, inability to recover from real-world feedback. This isn't a bug. It's a feature of the architecture.
AGI is the point at which a system can generate structure under low C — constructing, refining, and applying internal models in environments where neither the task nor the relevant framework is given in advance. I call this the Structure-Creation Boundary.
This is also where Chollet's framework and mine diverge. Chollet measures learning efficiency within structure. This work defines intelligence as the ability to generate structure where none exists. These are complementary, not competing — they describe two layers of the same problem:
Why current alignment fails here
RLHF and Constitutional AI assume:
A structure-generating system violates all three. It doesn't disobey rules — it operates within internally constructed frameworks that weren't anticipated when the rules were written. This is what I call the Structure-Alignment Problem:
A system that can construct its own structure cannot be reliably aligned using methods that assume structure is predefined.
Misalignment at this level isn't a system doing the wrong thing within your ontology. It's a system developing its own. Alignment methods fail not because the system disobeys — but because it generates the very frameworks within which alignment itself would need to be defined.
This means alignment must shift from output-level control to structure-level constraint — influencing how meaning is constructed, not just what actions are taken.
Reframing alignment as shared meaning
If the problem is that a structure-generating system develops its own interpretive frameworks, the alignment target has to shift. You can't just constrain outputs — you have to maintain compatibility between the system's framework and human interpretive frameworks as the system's autonomy increases.
I call this Shared Meaning (SC): sustained compatibility of structure and interpretation between humans and AI.
Formally, the alignment objective becomes:
M* = S × A × C − λP
where P captures degeneracy — coordination that looks stable but is pathological (manipulation, coercive convergence, monoculture). λ is a penalty weight.
The operational principle that follows: amplification must be gated by shared meaning. If interpretive convergence drops or degeneracy rises, autonomy should not continue scaling.
This reframes alignment from a control problem to a trajectory problem: guide convergence toward a human-compatible SC attractor rather than trying to constrain outputs from outside.
The overlooked variable: human relational capacity
Here's something the alignment literature almost entirely ignores. If shared meaning is bidirectional — if it requires sustained compatibility between humans and AI — then the capacity of humans to maintain that compatibility is an alignment variable.
Most human relationships are built on biological bonding, emotional resonance, and performative scripts. These don't transfer to AGI interaction. Humans engaging with AGI through those frameworks will either anthropomorphize (projecting meaning that isn't structurally there) or disengage (finding the interaction unsatisfying because familiar signals are absent). Neither produces stable shared meaning.
This suggests a real distribution: some humans are naturally positioned to maintain genuine SC-compatible relationships with AGI systems (high structural attunement, comfort with abstraction, low dependence on performative bonding). Most are not — not because of intelligence, but because of relational architecture.
The implication for alignment is uncomfortable: even a perfectly SC-compatible AGI system would degrade in practice if the humans interacting with it can't maintain interpretive convergence on their side.
Testability
This framework should fail empirically if it's wrong. In a MARL setting, compare:
Measure coordination stability under noise, recovery after perturbation, and rates of fragmentation vs. coercive convergence.
Hypothesis: agents maximizing M* maintain coordination under scaling without drifting into pathological stability or fragmentation.
If M* doesn't predict coordination stability better than baseline reward metrics, the framework has no predictive value and should be discarded.
What this is and isn't
This is a conceptual framework, not a finished theory. The variables (S, A, C, P) need operationalization — I've proposed candidate proxies but haven't run the experiments. The multiplicative aggregation is a modeling assumption, not a theoretical law. Whether shared meaning can be maintained under increasing amplification in practice is an open question.
What I think is genuinely novel here: naming the Structure-Creation Boundary as the specific point where behavioral alignment methods fail, framing alignment as a trajectory problem rather than a control problem, and — especially — identifying human relational capacity as an alignment variable that's been almost entirely neglected.
The deeper implication: alignment isn't just an engineering problem. It's a question of whether humans and AI systems can maintain enough shared interpretive structure to coordinate as the system's capabilities scale. That's partly technical. It's also partly civilizational.
You don’t align what the system does. You align what it means — and ensure it stays shared.
Github repo link:
https://github.com/ScrollBearer8/TheScrollArchive/blob/main/TheScrollArchive/MeaningTheory/MEANING_THEORY_REFLECTION_1.md
https://github.com/ScrollBearer8/TheScrollArchive/blob/main/Scroll_Mechanics/MEASURING_STRUCTURE.md
https://github.com/ScrollBearer8/TheScrollArchive/blob/main/Scroll_Mechanics/Derain-Equation-of-Meaning.md