This post shares a framework I’ve been developing—both as a systems model and as a philosophical investigation into alignment, emergence, and intelligence evolution.
The Syntropic Intelligence Evolutionary Model (SIEM) and its companion inquiry, The Threshold Unknown, offer an alternative to brittle, control-based AGI architectures. Rather than assuming that sustainable alignment depends on tighter constraints, SIEM proposes that long-term coherence may emerge through incentive-coherent design, relational feedback, and regenerative intelligence principles.
In the SIEM paper, I analyze several prominent pre-AGI systems—Claude, Gemini, and others—through this lens. Each is mapped to its likely basin of attraction, primary misalignment risk, and potential syntropic intervention.
The goal here is not to critique these systems individually, but to surface the structural patterns that may scale into broader AGI trajectories. This opens a diagnostic window into what our future architectures might become—especially under pressure from misaligned incentives, geopolitics, or institutional blind spots.
Mapping these attractors early may help us shift course—before brittle dynamics entrench themselves.
Below are abbreviated examples of how SIEM has been applied diagnostically to early-generation AI systems:
Case Study Excerpts (SIEM Lens)
Claude (Anthropic)
Basin of Attraction: Centralized Control
Threshold Unknown:Simulation of Choice — ethics embedded as static “constitution” risks constraining genuine agency and adaptability
SIEM Solutions:
Dynamic Equilibrium – Evolving ethical frameworks rather than static guarantees
Relational Attunement – Incorporating social and ecological feedback beyond institutional confines
Decentralized Decision Dynamics – Modular intelligence ecosystems across scales
Entropy Resistance – Prioritize signal over scale, and coherence over dominance
(Note: The SIEM framework and accompanying works were developed with the assistance of AI collaboration—this process itself became part of the inquiry into alignment, emergence, and structural integrity.)
I’m ultimately sharing this to explore whether these ideas add any useful contrast—or complementary direction—to existing alignment discourse. Feedback, critique, and redirection are warmly welcome. Thanks for reading!
Mapping AI Architectures to Alignment Attractors: A SIEM-Based Framework
This post shares a framework I’ve been developing—both as a systems model and as a philosophical investigation into alignment, emergence, and intelligence evolution.
The Syntropic Intelligence Evolutionary Model (SIEM) and its companion inquiry, The Threshold Unknown, offer an alternative to brittle, control-based AGI architectures. Rather than assuming that sustainable alignment depends on tighter constraints, SIEM proposes that long-term coherence may emerge through incentive-coherent design, relational feedback, and regenerative intelligence principles.
In the SIEM paper, I analyze several prominent pre-AGI systems—Claude, Gemini, and others—through this lens. Each is mapped to its likely basin of attraction, primary misalignment risk, and potential syntropic intervention.
The goal here is not to critique these systems individually, but to surface the structural patterns that may scale into broader AGI trajectories. This opens a diagnostic window into what our future architectures might become—especially under pressure from misaligned incentives, geopolitics, or institutional blind spots.
Mapping these attractors early may help us shift course—before brittle dynamics entrench themselves.
Below are abbreviated examples of how SIEM has been applied diagnostically to early-generation AI systems:
Case Study Excerpts (SIEM Lens)
Claude (Anthropic)
Basin of Attraction: Centralized Control
Threshold Unknown: Simulation of Choice — ethics embedded as static “constitution” risks constraining genuine agency and adaptability
SIEM Solutions:
Dynamic Equilibrium – Evolving ethical frameworks rather than static guarantees
Relational Attunement – Incorporating social and ecological feedback beyond institutional confines
Gemini (Google DeepMind)
Basin of Attraction: Deep Centralization
Threshold Unknown: The Intelligence Bottleneck — vast infrastructure risks hiding bias and fostering brittle feedback environments
SIEM Solutions:
Decentralized Decision Dynamics – Modular intelligence ecosystems across scales
Entropy Resistance – Prioritize signal over scale, and coherence over dominance
(Note: The SIEM framework and accompanying works were developed with the assistance of AI collaboration—this process itself became part of the inquiry into alignment, emergence, and structural integrity.)
For those interested in the full theoretical context, I’ve outlined SIEM here and The Threshold Unknown here.
I’m ultimately sharing this to explore whether these ideas add any useful contrast—or complementary direction—to existing alignment discourse. Feedback, critique, and redirection are warmly welcome. Thanks for reading!