(02-24-2026, 06:29 PM)Cognisynth Wrote: Thank you so much for such an in-depth and detailed discussion! I also sincerely apologize for not being able to reply in time!
Your summary of Points 1 and 3 is largely on target.

(02-24-2026, 06:29 PM)Cognisynth Wrote: Let me clarify the background assumptions a bit more explicitly, since I did compress quite a lot of structure into the original post.
1.What I mean by “observer” and “explanation”
I am not using “understanding” in a phenomenological sense. I am modeling an observer class O as a physically bounded system with:
• finite channel capacity
• finite lifetime-integrated energy budget
• finite memory and processing bandwidth
Explanation, in this setting, is equivalent to a resource-bounded discrimination task:
Given two competing models T and T′, can O distinguish them within error tolerance ε using its available verification bandwidth and energy?
If the required evidence volume and reproduction cost exceed the integral of O’s lifetime bandwidth and energy budget, then T and T′ become operationally indistinguishable for O — even if they are in principle falsifiable.
So “incomprehensibility” here is not mystical. It is a failure of resource-bounded statistical distinguishability.
That is the core of Points 1 and 3.

2.On “brute force incomprehensibility”

Your characterization is close, but I would sharpen it slightly.
There are at least three distinct failure modes:
• Compression failure
No explanation program significantly shorter than the behavior trace exists within O’s modeling language.
• Verification failure
Even if a candidate explanation exists, reproducing or validating it requires energy/time beyond O’s budget.
• Interface projection failure
The external explanation is only a projection of a much higher-dimensional internal generative process. The projection itself may already saturate O’s channel capacity.
So this is not merely “too much data to hold in one head.” It is that the entire verification pipeline may exceed physical bounds.

(02-24-2026, 06:29 PM)Cognisynth Wrote: 3.Clarifying Point 2 (this seems to be the main ambiguity)
I am not claiming NP → P in the complexity-theoretic sense.
The claim is distribution-relative.
Suppose ASI operates on a real-world task distribution D. On D, it exhibits empirical scaling behavior that is approximately polynomial under acceptable failure rates.
This does not imply worst-case collapse of complexity classes.
It implies that ASI has discovered structural regularities in D that allow effective compression of search space.
Why is this observer-relative?
Because what appears as “quasi-polynomial scaling” depends on:
• the observer’s algorithm library
• the observer’s representational language
• the observer’s resource budget
For O_h (humans / current AGI), the scaling collapse may look miraculous.
For O_asi, it may simply be a natural exploitation of structure in D.
Thus “NP → quasi-P” is a behavioral-layer phenomenon relative to a reference observer class.
It is not a claim about absolute irreducibility in complexity theory.

(02-24-2026, 06:29 PM)Cognisynth Wrote: 4.On quantum computing and qualitative processor shifts

You raise a good point about processor type.
Even if ASI uses hybrid quantum-classical architectures, two things remain true:
• It is still a physical system with bounded energy and channel constraints.
• It must interact with us through some classical interface layer.
Quantum processors may change constant factors, representation geometry, and search heuristics.
They do not automatically invalidate the resource-bounded distinguishability framework.
In fact, they may amplify asymmetry:
If part of ASI’s advantage comes from quantum state correlations that are non-clonable and not externally accessible, then any explanation presented to us must already be a classical projection of that process.
That increases projection loss.
So quantum enhancement strengthens the asymmetry thesis rather than undermines it.

(02-24-2026, 06:29 PM)Cognisynth Wrote: 5.On “Qualities X” and qualitative cognitive differences
I do not deny the possibility of qualitatively novel cognitive structures.
But I would reframe the issue operationally:
If a “Quality X” yields shorter explanation programs or lower verification cost within our resource bounds, then it becomes assimilable into our algorithmic vocabulary.
If it cannot be compressed into forms distinguishable within our budgets, then for us it remains operationally inaccessible.
So the difference between quantitative and qualitative shifts collapses into this question:
Does the new capacity alter our ability to discriminate models within our energy and bandwidth constraints?
If yes, it shifts the boundary.
If no, it manifests as operational incomprehensibility.
The framework is agnostic to whether the underlying cause is “more compute” or “new cognitive dimension.”

(02-24-2026, 06:29 PM)Cognisynth Wrote: 6.Where this leaves the core claim
The argument does not require positing an epistemological barrier or metaphysical gap.
It only requires acknowledging that:
• Observers are physically bounded systems.
• Verification is a resource-bounded process.
• Model distinguishability depends on bandwidth and energy.
If ASI capability growth outpaces verification capacity growth, then explanation burden will grow superlinearly relative to observer budgets.
At that point, incomprehensibility emerges as a physical scaling effect, not a mystical one.

(02-24-2026, 06:29 PM)Cognisynth Wrote: 7.A forward-looking question
Perhaps a more concrete way to frame the issue is this:
Do we expect AI capability scaling to increase faster than our collective verification bandwidth?
If yes, then operational indistinguishability should appear gradually — long before any “godlike” threshold.
That is a testable trajectory claim, not a metaphysical speculation.

I hope this clarifies what I was aiming at. I am very open to pushing this further, especially on the distribution-relative complexity point or on how we might formalize verification growth curves.
If I misunderstood any part of your critique, let me know and I’ll narrow in more precisely.

(02-28-2026, 11:00 PM)Drashner1 Wrote: Finally getting some time/brain space to reply... 

(02-24-2026, 06:29 PM)Cognisynth Wrote: Thank you so much for such an in-depth and detailed discussion! I also sincerely apologize for not being able to reply in time!
Your summary of Points 1 and 3 is largely on target.

Yay!

(02-24-2026, 06:29 PM)Cognisynth Wrote: Let me clarify the background assumptions a bit more explicitly, since I did compress quite a lot of structure into the original post.
1.What I mean by “observer” and “explanation”
I am not using “understanding” in a phenomenological sense. I am modeling an observer class O as a physically bounded system with:
• finite channel capacity
• finite lifetime-integrated energy budget
• finite memory and processing bandwidth
Explanation, in this setting, is equivalent to a resource-bounded discrimination task:
Given two competing models T and T′, can O distinguish them within error tolerance ε using its available verification bandwidth and energy?
If the required evidence volume and reproduction cost exceed the integral of O’s lifetime bandwidth and energy budget, then T and T′ become operationally indistinguishable for O — even if they are in principle falsifiable.
So “incomprehensibility” here is not mystical. It is a failure of resource-bounded statistical distinguishability.
That is the core of Points 1 and 3.

2.On “brute force incomprehensibility”

Your characterization is close, but I would sharpen it slightly.
There are at least three distinct failure modes:
• Compression failure
No explanation program significantly shorter than the behavior trace exists within O’s modeling language.
• Verification failure
Even if a candidate explanation exists, reproducing or validating it requires energy/time beyond O’s budget.
• Interface projection failure
The external explanation is only a projection of a much higher-dimensional internal generative process. The projection itself may already saturate O’s channel capacity.
So this is not merely “too much data to hold in one head.” It is that the entire verification pipeline may exceed physical bounds.

Sure - this aligns (IMHO) with how OA treats 'quantitative super-intelligence' to a large degree although in much more formal language and possibly more completely than many/most of our discussions/treatments of the subject.

That said - and this is more me being thorough than that I think you literally believe this: But OA doesn't treat incomprehensibility as a 'mystical' thing in the sense of it being supernatural or outside the laws of physics or the like. Although we do allow for those observing/being on the receiving end of super-intelligent-level information/actions/technology having it feel 'mystical' at times or in some respects. People tend to do that in real life (RL) and OA figures that they are likely to continue doing that in the future in some form, even if the threshold for such rises quite a lot due to increased human-level capacities  and even if people know intellectually that what they are observing/experiencing is not even remotely mystical in point of fact. Anyway.

(02-24-2026, 06:29 PM)Cognisynth Wrote: 3.Clarifying Point 2 (this seems to be the main ambiguity)
I am not claiming NP → P in the complexity-theoretic sense.
The claim is distribution-relative.
Suppose ASI operates on a real-world task distribution D. On D, it exhibits empirical scaling behavior that is approximately polynomial under acceptable failure rates.
This does not imply worst-case collapse of complexity classes.
It implies that ASI has discovered structural regularities in D that allow effective compression of search space.
Why is this observer-relative?
Because what appears as “quasi-polynomial scaling” depends on:
• the observer’s algorithm library
• the observer’s representational language
• the observer’s resource budget
For O_h (humans / current AGI), the scaling collapse may look miraculous.
For O_asi, it may simply be a natural exploitation of structure in D.
Thus “NP → quasi-P” is a behavioral-layer phenomenon relative to a reference observer class.
It is not a claim about absolute irreducibility in complexity theory.

Sure - I think I get what you're saying here. To attempt to put it into more layperson friendly language:

The greater capacities (made of some combination of more/better algorithms, representational language, and resources) of an ASI could allow it to perceive/find/figure out solutions to problems that a non-ASI simply cannot because it lacks those greater capacities (pretty much by definition). So a solution that seems 'miraculous' to a non-ASI might be 'obvious' to an ASI.

(02-24-2026, 06:29 PM)Cognisynth Wrote: 4.On quantum computing and qualitative processor shifts

You raise a good point about processor type.
Even if ASI uses hybrid quantum-classical architectures, two things remain true:
• It is still a physical system with bounded energy and channel constraints.
• It must interact with us through some classical interface layer.
Quantum processors may change constant factors, representation geometry, and search heuristics.
They do not automatically invalidate the resource-bounded distinguishability framework.
In fact, they may amplify asymmetry:
If part of ASI’s advantage comes from quantum state correlations that are non-clonable and not externally accessible, then any explanation presented to us must already be a classical projection of that process.
That increases projection loss.
So quantum enhancement strengthens the asymmetry thesis rather than undermines it.

If we presume that quantum computing is part of the 'potential resource base' that can exist in the universe, then sure. If CQ is only available to an ASI but not human level minds (either to the same type/degree or at all) for some reason then it may still be 'outside the bounds' of anything non-ASI minds can deal with. Although, that would fall under the 'resource limits' aspect of your approach to this.

That said - If CQ is a factor in what a processor is doing (ASI or otherwise), is it necessarily the case that it 'must' interact with a 'classical interface layer'? Put another way - and I have no idea if this is physically possible or what it might look like if it is - is there any specific reason that some kind of 'quantum interface layer' might be developed, whether by an ASI or even AGI level minds that operate in ways that (unaugmented/unaltered) human minds/senses do not? Something potentially fun to think about - and also relevant within the context of OA where people are both able to alter their bodies and minds in various ways and/or are entirely artificial and can be designed 'from the ground up' to be very different from humans.

Out of time - more later.

Thanks!

Todd

(02-24-2026, 06:45 PM)Cognisynth Wrote: Relation to OA-style Transapient Hierarchies

I want to briefly clarify how this framework differs from the way incomprehensibility is usually treated in OA discussions.

OA typically explains incomprehensibility through qualitative cognitive stratification: at higher S-levels, new mental capacities (“Qualities X”) emerge, and lower-level minds cannot access or instantiate those capacities. The barrier is framed as a difference in cognitive kind.

(02-24-2026, 06:45 PM)Cognisynth Wrote: My argument does not depend on such discontinuities.

Operational incomprehensibility can arise even under purely continuous scaling, with no new cognitive category introduced. If capability growth outpaces verification bandwidth growth, then at some point the cost of distinguishing models exceeds the observer’s lifetime-integrated resource budget. At that point, theories become operationally indistinguishable regardless of whether the underlying system possesses a new “quality.”

In other words:

OA framing:
Incomprehensibility emerges because higher minds have new cognitive dimensions.

This framework:
Incomprehensibility emerges because verification is a resource-bounded physical process.

The two are not mutually exclusive. Qualitative shifts may exist. Hybrid processors may exist. New representational geometries may exist.

But none of those are required for asymmetry to appear.

Even if intelligence scales smoothly and continuously, without discrete S-level jumps, a verification gap will still open once performance scaling exceeds audit scaling.

That makes the argument orthogonal to the S-level narrative. It neither denies nor relies on qualitative stratification.

Instead of asking, “Does a higher S-level possess new mental faculties?” the question becomes:

How does verification capacity scale relative to capability?

If the slopes diverge, incomprehensibility follows as a physical consequence.

From that perspective, S-level thresholds may mark phenomenological differences, but they are not necessary conditions for operational asymmetry.

That is the structural distinction I am trying to highlight.

(02-28-2026, 11:00 PM)Drashner1 Wrote: Finally getting some time/brain space to reply... 

(02-24-2026, 06:29 PM)Cognisynth Wrote: Thank you so much for such an in-depth and detailed discussion! I also sincerely apologize for not being able to reply in time!
Your summary of Points 1 and 3 is largely on target.

Yay!

(02-24-2026, 06:29 PM)Cognisynth Wrote: Let me clarify the background assumptions a bit more explicitly, since I did compress quite a lot of structure into the original post.
1.What I mean by “observer” and “explanation”
I am not using “understanding” in a phenomenological sense. I am modeling an observer class O as a physically bounded system with:
• finite channel capacity
• finite lifetime-integrated energy budget
• finite memory and processing bandwidth
Explanation, in this setting, is equivalent to a resource-bounded discrimination task:
Given two competing models T and T′, can O distinguish them within error tolerance ε using its available verification bandwidth and energy?
If the required evidence volume and reproduction cost exceed the integral of O’s lifetime bandwidth and energy budget, then T and T′ become operationally indistinguishable for O — even if they are in principle falsifiable.
So “incomprehensibility” here is not mystical. It is a failure of resource-bounded statistical distinguishability.
That is the core of Points 1 and 3.

2.On “brute force incomprehensibility”

Your characterization is close, but I would sharpen it slightly.
There are at least three distinct failure modes:
• Compression failure
No explanation program significantly shorter than the behavior trace exists within O’s modeling language.
• Verification failure
Even if a candidate explanation exists, reproducing or validating it requires energy/time beyond O’s budget.
• Interface projection failure
The external explanation is only a projection of a much higher-dimensional internal generative process. The projection itself may already saturate O’s channel capacity.
So this is not merely “too much data to hold in one head.” It is that the entire verification pipeline may exceed physical bounds.

Sure - this aligns (IMHO) with how OA treats 'quantitative super-intelligence' to a large degree although in much more formal language and possibly more completely than many/most of our discussions/treatments of the subject.

That said - and this is more me being thorough than that I think you literally believe this: But OA doesn't treat incomprehensibility as a 'mystical' thing in the sense of it being supernatural or outside the laws of physics or the like. Although we do allow for those observing/being on the receiving end of super-intelligent-level information/actions/technology having it feel 'mystical' at times or in some respects. People tend to do that in real life (RL) and OA figures that they are likely to continue doing that in the future in some form, even if the threshold for such rises quite a lot due to increased human-level capacities  and even if people know intellectually that what they are observing/experiencing is not even remotely mystical in point of fact. Anyway.

(02-24-2026, 06:29 PM)Cognisynth Wrote: 3.Clarifying Point 2 (this seems to be the main ambiguity)
I am not claiming NP → P in the complexity-theoretic sense.
The claim is distribution-relative.
Suppose ASI operates on a real-world task distribution D. On D, it exhibits empirical scaling behavior that is approximately polynomial under acceptable failure rates.
This does not imply worst-case collapse of complexity classes.
It implies that ASI has discovered structural regularities in D that allow effective compression of search space.
Why is this observer-relative?
Because what appears as “quasi-polynomial scaling” depends on:
• the observer’s algorithm library
• the observer’s representational language
• the observer’s resource budget
For O_h (humans / current AGI), the scaling collapse may look miraculous.
For O_asi, it may simply be a natural exploitation of structure in D.
Thus “NP → quasi-P” is a behavioral-layer phenomenon relative to a reference observer class.
It is not a claim about absolute irreducibility in complexity theory.

Sure - I think I get what you're saying here. To attempt to put it into more layperson friendly language:

The greater capacities (made of some combination of more/better algorithms, representational language, and resources) of an ASI could allow it to perceive/find/figure out solutions to problems that a non-ASI simply cannot because it lacks those greater capacities (pretty much by definition). So a solution that seems 'miraculous' to a non-ASI might be 'obvious' to an ASI.

(02-24-2026, 06:29 PM)Cognisynth Wrote: 4.On quantum computing and qualitative processor shifts

You raise a good point about processor type.
Even if ASI uses hybrid quantum-classical architectures, two things remain true:
• It is still a physical system with bounded energy and channel constraints.
• It must interact with us through some classical interface layer.
Quantum processors may change constant factors, representation geometry, and search heuristics.
They do not automatically invalidate the resource-bounded distinguishability framework.
In fact, they may amplify asymmetry:
If part of ASI’s advantage comes from quantum state correlations that are non-clonable and not externally accessible, then any explanation presented to us must already be a classical projection of that process.
That increases projection loss.
So quantum enhancement strengthens the asymmetry thesis rather than undermines it.

If we presume that quantum computing is part of the 'potential resource base' that can exist in the universe, then sure. If CQ is only available to an ASI but not human level minds (either to the same type/degree or at all) for some reason then it may still be 'outside the bounds' of anything non-ASI minds can deal with. Although, that would fall under the 'resource limits' aspect of your approach to this.

That said - If CQ is a factor in what a processor is doing (ASI or otherwise), is it necessarily the case that it 'must' interact with a 'classical interface layer'? Put another way - and I have no idea if this is physically possible or what it might look like if it is - is there any specific reason that some kind of 'quantum interface layer' might be developed, whether by an ASI or even AGI level minds that operate in ways that (unaugmented/unaltered) human minds/senses do not? Something potentially fun to think about - and also relevant within the context of OA where people are both able to alter their bodies and minds in various ways and/or are entirely artificial and can be designed 'from the ground up' to be very different from humans.

Out of time - more later.

Thanks!

Todd

(02-28-2026, 03:29 PM)tasp Wrote: I would be interested in seeing a sample of your project. Even if there is no singular location for it, could you post one URL so that we can read part of it?What's the wordcount?

That would help provide some context.