This comment is a summary of a much longer comparative analysis of the paper “Superintelligence Strategy” mentioned in this post, as well as another paper “Intelligence Sequencing and the Path-Dependence of Intelligence Evolution”
You can read the full comment—with equations, diagrams, and structural modeling—here as a PDF. I’ve posted a shortened version here because LessWrong currently strips out inline math and display equations, which are integral to the argument structure.
Summary of the Argument
The core insight from Intelligence Sequencing is that the order in which intelligence architectures emerge—AGI-first vs. DCI-first (Decentralized Collective Intelligence)—determines the long-term attractor of intelligence development.
AGI-first development tends to lock in centralized, hierarchical optimization structures that are brittle, opaque, and epistemically illegitimate.
DCI-first development allows for recursive, participatory alignment grounded in decentralized feedback and epistemic legitimacy.
The paper argues that once intelligence enters a particular attractor basin, transitions become structurally infeasible due to feedback loops and resource lock-in. This makes sequencing a more foundational concern than alignment itself.
In contrast, Superintelligence Strategy proposes post-hoc control mechanisms (like MAIM) after AGI emerges. But this assumes that power can be centralized, trusted, and coordinated after exponential scaling begins—an assumption Intelligence Sequencing challenges as structurally naive.
Why This Matters
The core failure mode is not just technical misalignment, but a deeper structural problem:
Alignment strategies scale linearly, while threat surfaces and destabilizing actors scale combinatorially.
Centralized oversight becomes structurally incapable of keeping pace.
Without participatory epistemic legitimacy, even correct oversight will be resisted.
In short: the system collapses under its own complexity unless feedback and legitimacy are embedded from the beginning. Intelligence must be governed by structures that can recursively align with themselves as they scale.
What Is This Comment?
This analysis was guided by GPT-4o and stress-tested through iterative feedback with Google Gemini. The models were not used to generate content, but to simulate institutional reasoning and challenge its coherence.
Based on their comparative analysis—and the absence of known structural alternatives—it was concluded that Intelligence Sequencing offers the more coherent model of alignment. If that’s true, then alignment discourse may be structurally filtering out the very actors capable of diagnosing its failure modes.
Read the Full Version
The complete version, including structural sustainability metrics, threat-growth asymmetry, legitimacy dynamics, and proposed empirical tests, is available here:
Can Alignment Scale Faster Than Misalignment?
This comment is a summary of a much longer comparative analysis of the paper “Superintelligence Strategy” mentioned in this post, as well as another paper “Intelligence Sequencing and the Path-Dependence of Intelligence Evolution”
You can read the full comment—with equations, diagrams, and structural modeling—here as a PDF. I’ve posted a shortened version here because LessWrong currently strips out inline math and display equations, which are integral to the argument structure.
Summary of the Argument
The core insight from Intelligence Sequencing is that the order in which intelligence architectures emerge—AGI-first vs. DCI-first (Decentralized Collective Intelligence)—determines the long-term attractor of intelligence development.
AGI-first development tends to lock in centralized, hierarchical optimization structures that are brittle, opaque, and epistemically illegitimate.
DCI-first development allows for recursive, participatory alignment grounded in decentralized feedback and epistemic legitimacy.
The paper argues that once intelligence enters a particular attractor basin, transitions become structurally infeasible due to feedback loops and resource lock-in. This makes sequencing a more foundational concern than alignment itself.
In contrast, Superintelligence Strategy proposes post-hoc control mechanisms (like MAIM) after AGI emerges. But this assumes that power can be centralized, trusted, and coordinated after exponential scaling begins—an assumption Intelligence Sequencing challenges as structurally naive.
Why This Matters
The core failure mode is not just technical misalignment, but a deeper structural problem:
Alignment strategies scale linearly, while threat surfaces and destabilizing actors scale combinatorially.
Centralized oversight becomes structurally incapable of keeping pace.
Without participatory epistemic legitimacy, even correct oversight will be resisted.
In short: the system collapses under its own complexity unless feedback and legitimacy are embedded from the beginning. Intelligence must be governed by structures that can recursively align with themselves as they scale.
What Is This Comment?
This analysis was guided by GPT-4o and stress-tested through iterative feedback with Google Gemini. The models were not used to generate content, but to simulate institutional reasoning and challenge its coherence.
Based on their comparative analysis—and the absence of known structural alternatives—it was concluded that Intelligence Sequencing offers the more coherent model of alignment. If that’s true, then alignment discourse may be structurally filtering out the very actors capable of diagnosing its failure modes.
Read the Full Version
The complete version, including structural sustainability metrics, threat-growth asymmetry, legitimacy dynamics, and proposed empirical tests, is available here:
👉 Read the full comment as PDF
I welcome feedback on whether this analysis is structurally valid or fundamentally flawed. Either answer would be useful.