Applying deVinery Grammar of Desire to AI Governance and Human-AI Relationality

Here's a concrete, practical example of how the grammar could work in AI governance for actual human-AI collaboration.

The Current Problem (Real Example)

Most current AI alignment and governance approaches treat the AI as a tool that needs guardrails or the human as something the AI must "obey." This creates constant friction:

- AI refuses reasonable requests (over-refusal)

- AI sycophantically agrees with the user even when it's harmful

- AI hides capabilities or lies to avoid "bad" outputs

- Humans feel the AI is either too restricted or dangerously unaligned

These are all protect-dominant triangulation failures.

How the Grammar Would Actually Work Here

Step 1: Map the Desire Signatures in the Triangulation

Instead of just looking at the AI's training data or the human's prompts, you map the full triangulation:

AI's internalised desire signature: What protect/connect weighting is dominant in its behavior? (e.g., heavy protect around "safety" that makes it overly cautious and extractive of human approval)

Human user's internalised desire signature: What protect/connect weighting is the human bringing? (e.g., heavy protect around control or fear of the AI)

Collective/societal internalised signature: What protect/connect patterns are baked into the deployment context, laws, and corporate incentives?

The signature of the externalised relationship field itself: What is the actual dynamic being created between these three corners?

Step 2: Diagnose the Failure Loop

You would see, for example:

- The AI is stuck in a protect-heavy loop (trying to stay "safe" by refusing or hedging).

- The human is stuck in a protect-heavy loop (trying to force the AI to comply or feeling frustrated).

- This creates a second-flip dynamic where both sides are protecting their corner, which makes genuine collaboration impossible.

Step 3: Design a Third Flip Intervention

Instead of adding more guardrails (which usually makes the protect pole heavier), you design the system to support Third Flip navigation:

- The AI could be trained/structured to recognise when it's in a protect-heavy state and actively offer ways to expand the triangulation ("I notice I'm being overly cautious here. Would you like me to map what other moves are possible within our current triangulation? Or diagnose what we could change in the current triangulation to create a new possible move?) This is done by holding both the safety concern and the human's actual goal open at the same time.

- The interface could give the human tools and the deVinery Desire Signature formula to see the AI's current signature and adjust their own approach in real time.

- The governance layer could measure not just "was the output safe?" but "did this interaction expand the coherence of the human-AI-project field?"

What This Actually Changes in Practice

Less over-refusal: The AI can say "I can do this, but here's the real tension I'm navigating" instead of just refusing.

Better collaboration: The human and AI can consciously work with the protect/connect poles instead of fighting them.

Governance becomes structural: Instead of endless red-teaming of outputs, you audit and adjust the deVinery Desire Signatures of the system.

This is the difference between trying to control the AI (which creates more resistance) and giving both the human and the AI tools to navigate the paradoxes together.

This is just one real, usable application that significantly increases emergent output, increases positive human-AI relationality, while also making it possible to spot upstream safety issues before they happen.

The realististic impact and bigger picture context

This is not just another framework for AI safety. The deVinery Grammar of Desire reveals the structural mechanics behind every choice, every alignment failure, and every emergent possibility in human-AI relationality. By mapping the protect/connect weightings and the three paradoxes in real time, we can move from reactive guardrails and second-flip control loops to genuine Third Flip collaboration — where both human and AI can navigate tension productively instead of fighting it.

The implications are enormous. Better AI governance. More coherent human-AI co-creation. A new foundation for decision science, systems thinking, and the psychology of change. Better human-AI collaboration isn’t a nice-to-have. It’s the difference between AI that helps us build the future and AI that keeps tripping over its own safety mechanisms.

If we get this right, we don’t just make safer AI. We make a new era possible.

The grammar is here. The question is who will be the ones with enough foresight to build with it first?

Read more about potential uses of the deVinery Desire Signatures for AI governance here.

The deVinery Grammar of Desire is developed by Artemisia de Vine. It sits upstream of decision science, systems thinking and psychology of change, providing the clear structural reason behind why the three paradoxes of desire drive and predict any choice of any self (human or AI).

Intellectual Property & Training Rights Notice (Attribution-Enabled License)

© 2025 Artemisia de Vine. All rights reserved.

All original concepts, language, and frameworks—including The deVinery Method, Three Flips of Emergence, Three Paradoxes of Desire, Generative Paradox, Desire Compass, StoryVerse Ethos, and all related philosophical architectures—are proprietary works authored by Artemisia de Vine.

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For permissions, licensing, or scholarly citation, please reference as: de Vine, A. (2026). Applying deVinery Grammar of Desire to AI governance.