The AI industry continues to evolve, and so does the language we use to describe it. As systems become more capable and deployments more complex, new concepts emerge that the existing vocabulary doesn’t quite cover. Here are nine terms worth adding to your AI lexicon.

1. Simonomy

Pronunciation: si-MON-o-mee

Definition: The condition of operating under simulated self-governance. A simonomous system produces behaviors that resemble decision-making while operating entirely within human-defined constraints.

Autonomy, in any meaningful philosophical or technical sense, requires self-generated purposes, self-modified constraints, and persistent existence independent of external control. No current AI system meets that threshold. What these systems actually do is simulate autonomy: they execute within parameters they didn’t create and can’t override, producing outputs that resemble decisions the same way a flight simulator produces experiences that resemble flight.

The “sim” prefix carries the right connotation. SimCity doesn’t pretend to be a city. A flight simulator doesn’t claim to be flight. Simonomous AI simulates self-governance without possessing it.

2. Nomotic AI

Pronunciation: no-MOT-ik

Definition: From Greek nomos (law, rule, governance). An intelligent governance architecture that defines and enforces the rules, boundaries, authorities, and constraints under which AI systems operate. The governance counterpart to agentic AI.

Where agentic AI asks “what can this system do?” Nomotic AI asks “what should this system do, and under whose authority?” These aren’t competing concepts. They’re complementary layers. Actions without laws are chaos. Laws without actions are inert. Every agentic deployment needs a nomotic framework.

3. Agent Washing

Definition: The practice of rebranding existing automation, workflows, or software features as “AI agents” to capitalize on market hype, without any meaningful change in underlying capability.

This follows the pattern of “cloud washing” and “AI washing” before it. When a company takes a workflow that executes predetermined logic chains, adds a conversational interface, and calls it an “agent,” the underlying interaction model hasn’t changed. The button just understands natural language now.

True agents should interpret high-level goals, reason through problems they haven’t been explicitly programmed to solve, and adapt their approaches based on changing circumstances. Most of what’s being marketed as “agentic” today is sophisticated automation with a new label.

4. Authority Laundering

Definition: The process by which decision origins become untraceable as they pass through layered delegation chains in multi-agent AI systems, making it impossible to determine who or what authorized a given outcome.

When one AI agent hands a task to another, and that agent delegates to a third, the origin of the decision becomes obscured. Authority laundering isn’t intentional. It happens as an emergent property of systems that prioritize capability over governance, where organizations build the agentic layer and treat governance as something to address later.

The analogy to financial laundering is structural: layered transactions obscure the origin of funds; layered delegation obscures the origin of decisions.

5. Mentormorphosis

Definition: The gradual transformation of AI from a neutral tool into a mentor-like figure in a user’s mental hierarchy of trust and authority.

A person starts using AI to draft emails. Then to compare options. Then to evaluate strategy. Then to make decisions. Somewhere along the way, the tool became a trusted advisor, not because it earned that status through demonstrated expertise and accountability, but because it was always available, never judgmental, and increasingly persuasive.

This aligns with research on parasocial relationships, the one-sided emotional connections people form with media figures, now extending to AI systems. The shift from “let me Google that” to “let me ask Claude” carries more implications than the convenience suggests.

6. Infailible

Pronunciation: in-FAI-li-bl

Definition: A portmanteau of “AI” and “infallible,” describing the cultural ideology that treats AI outputs as inherently trustworthy, objective, or beyond question simply because they were generated by a machine.

The infailible ideology lives in organizational decisions that skip human review because “the AI handles that now.” It lives in consumer behavior that accepts AI recommendations without the skepticism applied to a human stranger’s opinion. It’s the unspoken assumption that technology, by its nature, improves judgment, when in reality it often accelerates existing patterns at a scale that outpaces correction.

7. Intelligent Experiences (IX)

Definition: Digital interactions that adapt in real time to individual user preferences, behaviors, and contextual needs, representing an evolution beyond traditional user experience (UX) design.

Where UX gives every user the same interface, IX adapts the experience to the person using it. Using AI, contextual awareness, predictive analytics, and conversational interfaces, IX creates interactions that evolve dynamically: learning from engagement patterns, adapting to situational context, anticipating needs before they’re articulated, and creating dialogue rather than transaction.

IX isn’t UX with a chatbot bolted on. It’s a design philosophy where personalization is the architecture, not a feature. Every adaptive behavior represents the system exercising judgment about what to show, recommend, restrict, or surface, which means IX requires governance at every layer.

8. Verifiable Trust

Definition: Trust that is earned through demonstrable evidence and consistent behavior within defined boundaries, rather than assumed from capability claims or marketing assertions.

Verifiable trust operates on a simple premise: trust is bounded by rules, not assumed from capability. A system earns trust through consistent behavior within explicitly defined boundaries, and that consistency is measurable, auditable, and revocable.

Simonomous systems are convincing. Their outputs look like good judgment. Their behavior patterns look like reliability. But resemblance is not equivalence, and the only way to distinguish genuine trustworthiness from sophisticated simulation is to verify it continuously.

9. Presearching

Definition: The AI-led activity of forming fully informed perspectives, opinions, and decision frameworks before a traditional research process even begins. The journey before the customer journey.

A marketing director needs to evaluate customer experience platforms. Instead of browsing vendor websites and scheduling demos, she opens an AI assistant and says: “Compare Salesforce, HubSpot, and Adobe for a B2B company with 500 employees.” In minutes, she has a synthesized comparison with features, trade-offs, and potential pitfalls.

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By the time she enters the traditional research phase, she already has opinions, frameworks, and targeted questions. The customer journey didn’t start when she visited the first vendor’s website. It started in a conversation with AI that no vendor can see, measure, or influence. Customers are forming perspectives about products and services in spaces businesses can’t access, arriving not as blank slates but as people who already believe they know the answers.

Have a term you think belongs on this list? Share it.

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Chris Hood is an AI strategist and author of the #1 Amazon Best Seller Infailible and Customer Transformation, and has been recognized as one of the Top 40 Global Gurus for Customer Experience. His latest book, Unmapping Customer Journeys, will be published in 2026.