Tag Archives: LLMs

Hard-Wired Wetware IV: The Case Against Rebalancing: Why The Asymmetrical Integration Model (AIM) May Be Self-Correcting

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This paper interrogates the normative extension of the Asymmetric Integration Model by examining whether asymmetrical integration may represent a dynamically stabilised equilibrium rather than a structural failure. It explores market feedback, legitimacy constraints, optimisation adaptation, and functional specialisation as endogenous corrective mechanisms, arguing that asymmetry may be constrained by competitive and economic forces rather than requiring deliberate architectural rebalancing.

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Hard-Wired Wetware III: Rebalancing The Asymmetric Integration Model (AIM)

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This paper introduces the Asymmetric Integration Model (AIM), arguing that in post-LLM digital environments, automation generates conversational scale while humans supply consequence-bearing legitimacy. As optimisation regimes prioritise engagement density and persistence, affective cost is distributed to participants while control remains centralised. The proposed framework shifts debate from content moderation to architectural design, outlining pathways to rebalance asymmetry without rejecting human–machine integration.

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Hard-Wired Wetware II: the Post-LLM Web Asymmetric Integration Model (AIM) Defined

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The post-LLM web is not replacing humans with machines. It is integrating humans into machine-generated scale. This paper formalises the Asymmetric Integration Model (AIM), arguing that as synthetic systems produce abundant conversational substrate, human participants supply the scarce resource of consequence-bearing legitimacy. Contemporary platforms are shifting from attention extraction toward asymmetrical affective integration.

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Hard-Wired Wetware I: From Attention Extraction to Human Integration

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As automation surpasses human traffic and synthetic actors permeate public, semi-private, and gaming ecosystems, the web is reorganising around a new extraction layer. Large language models collapse the cost of human emulation, shifting platforms from attention capture to human integration. The next phase of the internet does not replace people with machines. It recruits them as psychological infrastructure: wetware that supplies legitimacy, empathy, and consequence to autonomous systems.

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Temporal RAG: Retrieving “State as Known on Date X” for LLMs in Financial Services

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This article explains why standard Retrieval-Augmented Generation (RAG) silently corrupts history in Financial Services by answering past questions with present-day truth. It introduces Temporal RAG: a regulator-defensible retrieval pattern that conditions every query on an explicit as_of timestamp and retrieves only from Point-in-Time (PIT) slices governed by SCD2 validity, precedence rules, and repair policies. Using concrete implementation patterns and audit reconstruction examples, it shows how to make LLM retrieval reproducible, evidential, and safe for complaints, remediation, AML, and conduct-risk use cases.

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Integrating AI and LLMs into Regulated Financial Services Data Platforms

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How AI fits into Bronze/Silver/Gold without breaking lineage, PIT, or SMCR: This article sets out a regulator-defensible approach to integrating AI and LLMs into UK Financial Services data platforms (structurally accurate for 2025/2026). It argues that AI must operate as a governed consumer and orchestrator of a temporal medallion architecture, not a parallel system. By defining four permitted integration patterns, PIT-aware RAG, controlled Bronze embeddings, anonymised fine-tuning, and agentic orchestration, it shows how to preserve lineage, point-in-time truth, and SMCR accountability while enabling practical AI use under PRA/FCA scrutiny.

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