What Can Humans Trust LLM AI to Do?

Abstract

At the existing time, Large Language Model (LLM) platforms are increasingly embedded in domains where trust, meaning, and decision-making carry real social consequences. This paper examines what humans can justifiably trust LLM systems to do under current architectural conditions, taking as given previously established analyses of semantic drift, normative drift, and the absence of integrity in contemporary LLM instances. Rather than assessing model capability or alignment, the paper focuses on trust as a governance question: which functions can be safely entrusted to systems whose outputs are fluent but whose meanings do not reliably bind across time, context, or re-expression.

The paper argues that, under present conditions, LLMs can be trusted as instruments of cognitive assistance, supporting exploration, articulation, transformation, and pattern discovery, where failure remains recoverable and authority remains human. Conversely, it shows that extending trust to roles involving custody of meaning, continuity of obligation, or normative authority introduces predictable structural risk, including erosion of shared norms, diffusion of responsibility, and institutional fatigue. These risks arise not from misuse or malice, but from a mismatch between human expectations of integrity and the architectural properties of current conversational AI platforms.

By drawing a clear trust boundary grounded in existing failure analyses, this paper provides a practical framework for human-AI collaboration that preserves human agency while remaining forward-compatible with governance architectures such as Cognitive Memoisation and CM-2. It is intended as a transitional statement: defining safe trust relationships today, while clarifying the conditions under which those boun0daries may responsibly shift in the future.

Prerequisite Reading Note

This paper assumes the analyses of semantic drift, normative drift, and integrity failure developed in Integrity and Semantic Drift in Large Language Model Systems ^{(ref a)} paper. Those concepts are used here as establishePrerequisite Reading Note
This paper assumes the analyses of semantic drift, normative drift, and integrity failure developed in Integrity and Semantic Drift in Large Language Model Systems (ref a)  premises and are not restated.

Readers unfamiliar with those failure modes should read that paper first, as the trust boundaries articulated here are derived directly from its conclusions.

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