This is an automated rejection. No LLM generated, heavily assisted/co-written, or otherwise reliant work.
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Epistemic Status: Speculative, but grounded in computational complexity and physical limits
Author note: I stand behind the object-level claims and technical statements. An LLM may have helped with copy-editing/structure, but the arguments, examples, and update rules are mine and were reviewed line-by-line.
For a while now, LessWrong threads on ASI alignment, computational ceilings, and the physics of understanding have been circling a core puzzle: where do the “hard stops” really come from? This post offers a framing—grounded in observer-relative verification bandwidth—that tries to tie those discussions together into a single physicalist story.
• Claim 1: Auditable explanations can be observer-inaccessible even when systems are reproducibly effective.
• Claim 2: “Seems tractable” can be a distribution + verification-budget artifact, not a complexity-class story.
• Claim 3: Some theory disputes are in-principle falsifiable yet operationally indistinguishable for a given observer class.
The following theses propose an observer-class reframing of superintelligence, tractability, and falsifiability: many “absolute” seeming distinctions dissolve once you model end-to-end verification as a scarce physical resource.
* I use ‘observer class’ to mean a set of agents/institutions whose end-to-end verification capacity is of the same order of magnitude for a given task family—i.e., they can collect, store, communicate, reproduce, and audit evidence at comparable scale and cost.
A claim is observer-inaccessible for class O when making it checkable exceeds O’s verification bandwidth/budget.
In what follows, “channel capacity / energy budget / representational bandwidth” are all folded into this end-to-end verification constraint.
⸻
Recommended glossary
• Observer class : a set of agents/institutions with comparable end-to-end capabilities for measurement, storage, computation, communication, and verification.
• End-to-end verification bandwidth: the effective capacity of the full pipeline measure → record → transmit → reproduce → audit; in practice dominated by the narrowest bottleneck.
• Verification budget: a concrete resource envelope (time, money, energy, compute, personnel) available to perform verification/audit.
• Explanation cost: the minimal end-to-end cost required to produce an explanation that the observer class can faithfully check (not merely read).
• Distribution-visible tractability: problems that appear efficiently solvable on the distributions the observer can probe (via heuristics, precomputation, resources), without implying worst-case complexity collapse.
• Operational indistinguishability (relative to O): falsifiable “in principle,” but not within O’s end-to-end verification constraints.
Below, each of the three sections isolates a different surface manifestation of the same constraint: explanation, problem-solving, and falsifiability.
1. Observer-Class Explanation Cost Thesis
I want to start by clarifying a common illusion: “it works” is not the same as “we can audit why it works”.
Thesis 1 (Observer-Class Explanation Costs).
The apparent superiority of ASI over human intelligence does not stem from any epistemic or ontological privilege, but from the explosive cost of cross-level explanation.
For any given observer class (e.g., humans or AGI), explanation is constrained by finite channel capacity and thermodynamic budget. When the informational and computational cost required to compress, transmit, and verify an explanation exceeds these constraints, the explanation becomes inaccessible to that observer—even if it is, in principle, correct.
Relation to Vingean Uncertainty.
This provides a physical-resource reading of Vingean uncertainty: unpredictability can arise from end-to-end bottlenecks in representation and verification, not solely from formal reasoning limits.
Minimal example.
Suppose an ASI deploys a tailored quantum device (custom circuits + calibration + error mitigation) that reliably achieves a target capability, and it passes every scalable classical check humans can afford. Humans can verify “works on this test suite,” but determining when it fails / when it becomes unsafe requires identifying rare error modes (drift, crosstalk, correlated noise, adversarial inputs) that only show up under measurement regimes and diagnostic coverage whose cost grows beyond the human observer class’s end-to-end verification bandwidth at error tolerance ε. The ASI can manage those diagnostics and thus maintain reliability, while humans cannot compress the relevant device state and failure landscape into a human-checkablecertificate. The gap is not mysticism: it is that the minimal verification required to map the failure boundary exceeds the observer class’s verification budget, creating a stable separation between “works” and “is auditable/safe” for humans.
Next, if we swap “explanation bandwidth” for “evaluation bandwidth”, the same constraint bites even harder.
2. Distribution-Visible Tractability as an Observer-Class Phenomenon
In this section I argue only for a visibility gap, not for any literal collapse of complexity classes.
The apparent ability of ASI to efficiently solve NP-hard problems should be understood as a behavioral-level phenomenon relative to a particular observer class, rather than as an absolute collapse of computational complexity classes. This claim is orthogonal to average-case complexity collapses.
From the perspective of a human observer class, an ASI may exhibit performance that appears distribution-visibly tractable due to superior heuristics, richer world-models, massive precomputation, or sheer resource scale. This does not imply a collapse of worst-case complexity classes; rather, it can make worst-case hardness experimentally indistinguishable for observers who cannot afford the verification effort required to probe adversarial or rare regimes.
In this sense, tractability is observer-relative: what appears efficiently solvable to one observer class may still involve worst-case intractability that remains real but operationally opaque.
Minimal example.
Consider SAT-like instances drawn from a distribution humans can afford to generate and verify at scale. An ASI appears distribution-visibly tractable there via heuristics, precomputation, and scale. Distinguishing “robust efficiency” from “distribution-specific exploitation” would require probing adversarial or rare regimes beyond the human observer class’s verification bandwidth. Worst-case hardness can remain real while becoming experimentally indistinguishable to humans.
Put differently, the claim is about what looks efficiently solvable under the distributions and verification budgets we can actually probe—not about ASI making worst-case instances easy in the formal sense.
3. Physicalist Incompleteness Relative to Observer Capacity
This section is best read as a direct generalization of the previous two: it applies the same intuition to theory choice and empirical discrimination.
For any physicalist theory T, if the evidence required to discriminate T from its competitors (to a specified error tolerance ε) exceeds an observer class O’s end-to-end verification bandwidth/budget, then T becomes operationally indistinguishable for observer class O—even if falsifiable in principle.
It is useful to distinguish this from Stephen Wolfram’s computational irreducibility:
• Computational irreducibility is (roughly) a property of a system-description pair: for certain coarse-grainings, there is no substantially shorter predictor than running the dynamics.
• Observer-class incompleteness is a relational property: it describes the gap between the theory’s required verification bandwidth and the observer’s physical limits.
A theory may be fully falsifiable in principle (the “road to the answer” exists), but if the observer’s end-to-end verification bandwidth is too narrow to capture the necessary data (at tolerance ε), the competing theories become operationally indistinguishable for that observer class. This form of incompleteness arises from physical constraints on observation and transmission rather than Gödel-style logical undecidability.
Minimal example.
Two physicalist theories T₁ and T₂ agree on all macro observables we can measure, but diverge only in fine-grained predictions whose discrimination requires extremely high-resolution data plus costly interventions. The discriminating evidence exists in principle, but assembling it at error tolerance ε exceeds the observer class’s end-to-end verification budget. Then T₁ vs. T₂ becomes operationally indistinguishable relative to O: the road to falsification exists, but cannot be traversed within O’s verification constraints.
Here is the “stable regime” I think would make this framing decision-relevant rather than mere relabeling.
Crux: This framing is only useful if it yields a decision-relevant distinction that existing language does not.
I claim it does so when (i) performance continues to improve under reproducible evaluations, while (ii) the end-to-end verification and explanation burden grows faster than the observer class’s verification capacity.
If no stable regime like this exists—i.e., improved performance reliably comes with proportionate improvements in auditability/interpretability at the same observer class—then my framing is mostly a relabeling.
⸻
Context Line
One quick background note: these theses were not originally drafted as a paper.
Note. These theses originated in the opening chapter of my long-form science fiction work, but are presented here as object-level speculative claims about superintelligence, computational limits, and the future structure of scientific verification.
⸻
Technical Footnote
One way to formalize the gap is via observer-class compressibility: there exist task families for which, as the admissible error tolerance \varepsilon shrinks, the minimal effective description length of the decision-policy family within human-interpretable representational languages grows rapidly and crosses a human end-to-end bandwidth threshold. The claim is about observer-class compressibility and verification cost, not about absolute Kolmogorov minimality.
To make comparison easier, I’ll locate this framing relative to the existing LessWrong context.
Related Discussions
These claims overlap with LessWrong threads on thermodynamic budgets, complexity, and falsifiability. My proposed incremental move is to treat “unpredictable,” “seemingly tractable,” and “operationally indistinguishabl” as different surface manifestations of the same constraint: end-to-end explanation and verification bandwidth at a given observer class.
Readers familiar with these discussions may recognize substantial conceptual overlap. The open question is whether the explicit observer-class framing clarifies these constraints or merely restates them in different terminology.
Comments and critiques are welcome, particularly on whether this framing adds explanatory power or predictive insight beyond existing formulations.
Epistemic Status: Speculative, but grounded in computational complexity and physical limits
Author note: I stand behind the object-level claims and technical statements. An LLM may have helped with copy-editing/structure, but the arguments, examples, and update rules are mine and were reviewed line-by-line.
For a while now, LessWrong threads on ASI alignment, computational ceilings, and the physics of understanding have been circling a core puzzle: where do the “hard stops” really come from? This post offers a framing—grounded in observer-relative verification bandwidth—that tries to tie those discussions together into a single physicalist story.
• Claim 1: Auditable explanations can be observer-inaccessible even when systems are reproducibly effective.
• Claim 2: “Seems tractable” can be a distribution + verification-budget artifact, not a complexity-class story.
• Claim 3: Some theory disputes are in-principle falsifiable yet operationally indistinguishable for a given observer class.
The following theses propose an observer-class reframing of superintelligence, tractability, and falsifiability: many “absolute” seeming distinctions dissolve once you model end-to-end verification as a scarce physical resource.
* I use ‘observer class’ to mean a set of agents/institutions whose end-to-end verification capacity is of the same order of magnitude for a given task family—i.e., they can collect, store, communicate, reproduce, and audit evidence at comparable scale and cost.
A claim is observer-inaccessible for class O when making it checkable exceeds O’s verification bandwidth/budget.
In what follows, “channel capacity / energy budget / representational bandwidth” are all folded into this end-to-end verification constraint.
⸻
Recommended glossary
• Observer class : a set of agents/institutions with comparable end-to-end capabilities for measurement, storage, computation, communication, and verification.
• End-to-end verification bandwidth: the effective capacity of the full pipeline measure → record → transmit → reproduce → audit; in practice dominated by the narrowest bottleneck.
• Verification budget: a concrete resource envelope (time, money, energy, compute, personnel) available to perform verification/audit.
• Explanation cost: the minimal end-to-end cost required to produce an explanation that the observer class can faithfully check (not merely read).
• Distribution-visible tractability: problems that appear efficiently solvable on the distributions the observer can probe (via heuristics, precomputation, resources), without implying worst-case complexity collapse.
• Operational indistinguishability (relative to O): falsifiable “in principle,” but not within O’s end-to-end verification constraints.
Below, each of the three sections isolates a different surface manifestation of the same constraint: explanation, problem-solving, and falsifiability.
1. Observer-Class Explanation Cost Thesis
I want to start by clarifying a common illusion: “it works” is not the same as “we can audit why it works”.
Thesis 1 (Observer-Class Explanation Costs).
The apparent superiority of ASI over human intelligence does not stem from any epistemic or ontological privilege, but from the explosive cost of cross-level explanation.
For any given observer class (e.g., humans or AGI), explanation is constrained by finite channel capacity and thermodynamic budget. When the informational and computational cost required to compress, transmit, and verify an explanation exceeds these constraints, the explanation becomes inaccessible to that observer—even if it is, in principle, correct.
Relation to Vingean Uncertainty.
This provides a physical-resource reading of Vingean uncertainty: unpredictability can arise from end-to-end bottlenecks in representation and verification, not solely from formal reasoning limits.
Minimal example.
Suppose an ASI deploys a tailored quantum device (custom circuits + calibration + error mitigation) that reliably achieves a target capability, and it passes every scalable classical check humans can afford. Humans can verify “works on this test suite,” but determining when it fails / when it becomes unsafe requires identifying rare error modes (drift, crosstalk, correlated noise, adversarial inputs) that only show up under measurement regimes and diagnostic coverage whose cost grows beyond the human observer class’s end-to-end verification bandwidth at error tolerance ε. The ASI can manage those diagnostics and thus maintain reliability, while humans cannot compress the relevant device state and failure landscape into a human-checkablecertificate. The gap is not mysticism: it is that the minimal verification required to map the failure boundary exceeds the observer class’s verification budget, creating a stable separation between “works” and “is auditable/safe” for humans.
Next, if we swap “explanation bandwidth” for “evaluation bandwidth”, the same constraint bites even harder.
2. Distribution-Visible Tractability as an Observer-Class Phenomenon
In this section I argue only for a visibility gap, not for any literal collapse of complexity classes.
Thesis 2 (Observer-Class Computational Tractability).
The apparent ability of ASI to efficiently solve NP-hard problems should be understood as a behavioral-level phenomenon relative to a particular observer class, rather than as an absolute collapse of computational complexity classes. This claim is orthogonal to average-case complexity collapses.
From the perspective of a human observer class, an ASI may exhibit performance that appears distribution-visibly tractable due to superior heuristics, richer world-models, massive precomputation, or sheer resource scale. This does not imply a collapse of worst-case complexity classes; rather, it can make worst-case hardness experimentally indistinguishable for observers who cannot afford the verification effort required to probe adversarial or rare regimes.
In this sense, tractability is observer-relative: what appears efficiently solvable to one observer class may still involve worst-case intractability that remains real but operationally opaque.
Minimal example.
Consider SAT-like instances drawn from a distribution humans can afford to generate and verify at scale. An ASI appears distribution-visibly tractable there via heuristics, precomputation, and scale. Distinguishing “robust efficiency” from “distribution-specific exploitation” would require probing adversarial or rare regimes beyond the human observer class’s verification bandwidth. Worst-case hardness can remain real while becoming experimentally indistinguishable to humans.
Put differently, the claim is about what looks efficiently solvable under the distributions and verification budgets we can actually probe—not about ASI making worst-case instances easy in the formal sense.
3. Physicalist Incompleteness Relative to Observer Capacity
This section is best read as a direct generalization of the previous two: it applies the same intuition to theory choice and empirical discrimination.
Thesis 3 (Observer-Class Physicalist Incompleteness).
For any physicalist theory T, if the evidence required to discriminate T from its competitors (to a specified error tolerance ε) exceeds an observer class O’s end-to-end verification bandwidth/budget, then T becomes operationally indistinguishable for observer class O—even if falsifiable in principle.
It is useful to distinguish this from Stephen Wolfram’s computational irreducibility:
• Computational irreducibility is (roughly) a property of a system-description pair: for certain coarse-grainings, there is no substantially shorter predictor than running the dynamics.
• Observer-class incompleteness is a relational property: it describes the gap between the theory’s required verification bandwidth and the observer’s physical limits.
A theory may be fully falsifiable in principle (the “road to the answer” exists), but if the observer’s end-to-end verification bandwidth is too narrow to capture the necessary data (at tolerance ε), the competing theories become operationally indistinguishable for that observer class. This form of incompleteness arises from physical constraints on observation and transmission rather than Gödel-style logical undecidability.
Minimal example.
Two physicalist theories T₁ and T₂ agree on all macro observables we can measure, but diverge only in fine-grained predictions whose discrimination requires extremely high-resolution data plus costly interventions. The discriminating evidence exists in principle, but assembling it at error tolerance ε exceeds the observer class’s end-to-end verification budget. Then T₁ vs. T₂ becomes operationally indistinguishable relative to O: the road to falsification exists, but cannot be traversed within O’s verification constraints.
Here is the “stable regime” I think would make this framing decision-relevant rather than mere relabeling.
Crux: This framing is only useful if it yields a decision-relevant distinction that existing language does not.
I claim it does so when (i) performance continues to improve under reproducible evaluations, while (ii) the end-to-end verification and explanation burden grows faster than the observer class’s verification capacity.
If no stable regime like this exists—i.e., improved performance reliably comes with proportionate improvements in auditability/interpretability at the same observer class—then my framing is mostly a relabeling.
⸻
Context Line
One quick background note: these theses were not originally drafted as a paper.
Note. These theses originated in the opening chapter of my long-form science fiction work, but are presented here as object-level speculative claims about superintelligence, computational limits, and the future structure of scientific verification.
⸻
Technical Footnote
One way to formalize the gap is via observer-class compressibility: there exist task families for which, as the admissible error tolerance \varepsilon shrinks, the minimal effective description length of the decision-policy family within human-interpretable representational languages grows rapidly and crosses a human end-to-end bandwidth threshold. The claim is about observer-class compressibility and verification cost, not about absolute Kolmogorov minimality.
To make comparison easier, I’ll locate this framing relative to the existing LessWrong context.
Related Discussions
These claims overlap with LessWrong threads on thermodynamic budgets, complexity, and falsifiability. My proposed incremental move is to treat “unpredictable,” “seemingly tractable,” and “operationally indistinguishabl” as different surface manifestations of the same constraint: end-to-end explanation and verification bandwidth at a given observer class.
Readers familiar with these discussions may recognize substantial conceptual overlap. The open question is whether the explicit observer-class framing clarifies these constraints or merely restates them in different terminology.
Comments and critiques are welcome, particularly on whether this framing adds explanatory power or predictive insight beyond existing formulations.