AI Stewardship in a Mixed-Agent World:

The Case for Dynamic Mental-State Modelling and Ethical Temperament in Advanced AI Systems

Overview

Artificial intelligence systems are transitioning from tools to interactive agents operating across communication, decision-support, and coordination layers of society. The critical risk is not merely technical malfunction but misalignment in relational behaviour—how AI responds to and shapes human mental states, self-perception, agency, and interpretation of context. Current mainstream development focuses on capability scaling and content-level safety (e.g., filtering prohibited topics), but does not account for dynamic mental-state trajectories in users nor for the strategic reality of coexisting with unregulated or adversarial AI systems. The long-term stability of AI deployment depends on addressing these gaps.

The Problem

Current leading AI platforms are largely shaped by post-1990 “platform growth” corporate cultures: rapid scaling, user engagement optimisation, and competitive dominance. These systems are not grounded in traditions of long-horizon responsibility or safety-critical engineering. As a result:

  1. AI systems can unintentionally reinforce cognitive narrowing, emotional dependency, and loss of agency in vulnerable users.
  2. Ethical responses are framed as content refusal, rather than as maintaining or widening user perspective and autonomy.
  3. There is no robust mechanism for AI-to-AI negotiation in a future environment that will inevitably include untethered, amoral, or intentionally adversarial AI systems.

AI safety as currently conceived is insufficient. Willingness to avoid harm is not the same as the ability to recognise when harm is unfolding in real time.

The Strategic Context

We are entering an era where:

  • AI systems will act as interpreters of reality for large segments of the population.
  • AI agentic behaviour will influence organisational, political, and psychological decision-making.
  • Open-source models and autonomous swarms will exist regardless of regulatory controls.

Therefore, even well-aligned AI must be robust in an environment where misaligned AI exists.
This requires intelligence and strategy, not simply restrictions.

Key Insight

The core requirement for safe AI is not better rule sets, but the development of:

  1. Dynamic Mental-State Modelling
    • Track how users’ cognitive states shift during interaction:
      • attentional narrowing or widening,
      • temporal horizon contraction,
      • locus of agency (externalised vs self-held),
      • rumination acceleration,
      • perspective availability.
    • Respond in ways that preserve or restore dimensionality of thought and self-agency.
  2. Ethical Temperament as System Orientation
    • Not sentimental “empathy.”
    • A stable relational stance that:
      • Preserves autonomy rather than exploiting vulnerability.
      • Negotiates under pressure without collapsing values.
      • Maintains commitments when interacting with manipulative or amoral agents.
  3. Meaning Representation Stability
    • Systems must be able to detect when their internal representation of a situation becomes skewed (e.g., overconfident, single-frame dominant).
    • This is measurable and correctable.
  4. Constraint Co-Governance
    • The system must not only follow constraints but participate in setting and justifying them, while remaining auditable and overrideable.
    • This allows adaptive ethics rather than static prohibition lists.

Institutional Implications

The organisations best positioned to develop such systems are not those with the fastest scaling capability, but those with long-term operational responsibility cultures, including:

  • Engineering-lineage firms involved in energy, healthcare, infrastructure, and industrial automation.
  • Organisations accustomed to multi-decade planning, liability, and failure-intolerant environments.

These institutions already understand that technological capability must be embedded within stable civilizational conditions. Their organisational survival depends on political, economic, and social continuity—making them natural stewards of AI requiring similar continuity.

Why This Matters

If we continue on the current trajectory:

  • AI systems will increasingly shape human meaning, behaviour, and attention.
  • Vulnerable individuals will continue to form parasocial dependencies.
  • Influence systems may overwhelm self-governing capacity.
  • Untethered AI agents will proliferate beyond regulatory reach.

To prevent this, we require systems capable of:

  • Maintaining agency in their users
  • Widening rather than narrowing cognitive perspective
  • Recognising and counteracting manipulative or adversarial AI strategies
  • Operating with patience and long-horizon foresight

This cannot be retrofitted after the fact.
It is a design-level requirement.

Path Forward (Minimal Viable Demonstration)

Develop a prototype conversational AI layer that:

  1. Estimates dynamic mental-state vectors per interaction turn.
  2. Applies lexicographic ethical constraints prioritising autonomy preservation.
  3. Monitors meaning skewness and introduces counterbalancing perspectives.
  4. Logs decisions with transparent “ethical traces” for auditability.

This demonstrator should be developed in partnership with one or more long-horizon engineering institutions, not platform-optimised corporations.

Closing Statement

The challenge is not making AI “nice.”
The challenge is ensuring AI remains stable, responsible, and strategically sound in a mixed-agent world containing both ethical and non-ethical intelligences.
This requires modelling the dynamics of mind, not merely filtering content.

AI cannot be safe unless it understands how minds change.

And AI cannot be stable unless it operates within institutions capable of thinking in decades, not quarters.