Six words are currently wandering around boardrooms, policy meetings, and engineering diagrams wearing lab coats they did not earn.

Every day, very serious people make very expensive decisions using vocabulary that sounds authoritative, scientific, and reassuring, rather like a man explaining nuclear physics while holding a banana. The words feel precise. This is largely because they contain syllables. Unfortunately, they do not mean what everyone thinks they mean, and the moment language stops meaning what we think it means, reality starts charging late fees.

When words fail, everything built on them doesn’t collapse immediately. It wobbles first. Which is worse?

Before we continue, we should dispose of the ceremonial objections, which arrive with remarkable consistency:

“Well, not in the human sense.”

“In AI, the definition is different.”

“In engineering, this term means…”

No.

What we have done is not redefine language; we have mugged it in a poorly lit alley and forced it to hand over its wallet so marketing could buy a nicer slide deck. These are not alternative definitions. They are justifications. “Autonomous agents,” for instance, is easier to sell. “Heteronomous statistical machinery doing exactly what it was told” sounds like a product that needs adult supervision.

Language has become less a tool for accuracy and more a form of aspirational fiction.

This isn’t pedantic wordplay, despite what anyone who has never been bankrupted by a bad abstraction might tell you. The categories we use to describe AI determine how we build it, regulate it, and panic about it.

Philosophers have a word for this sort of thing, ontology, which is just a formal way of saying, “What do we think exists, and how badly are we about to misunderstand it?”

When our mental models fall behind reality, the universe does not slow down to help us catch up. It simply allows us to make confident mistakes at scale. What we need is ontological recalibration: a polite but firm update to how we classify machine intelligence before it keeps being mistaken for something it isn’t.

Let’s examine six words that deserve scrutiny.

Intelligence → Capability

The term “artificial intelligence” was coined in 1956 when researchers gathered at Dartmouth College to explore whether machines could think. Nearly seven decades later, we’re still using their vocabulary to describe systems they couldn’t have imagined.

Here’s the problem: we’ve never agreed on what intelligence actually means. Is it problem-solving? Pattern recognition? Adaptability? Creativity? The ability to pass tests designed for humans? When we call something “intelligent,” we’re smuggling in assumptions we haven’t examined.

Current AI systems exhibit remarkable capabilities in narrow domains while failing at tasks any four-year-old handles effortlessly. They can generate sophisticated legal arguments but struggle with basic physical reasoning. Calling this “intelligence” invites comparisons to human cognition that mislead more than they illuminate. Recalibrating this. Today’s AI = Capability / No Intelligence AGI = Intelligence ASI = Sapience

Better term: Capability. This word describes what systems actually do without making claims about what they are. A system has capabilities: it can generate text, classify images, and predict outcomes. Capability is measurable and specific. Intelligence mostly gets people into arguments on social media.

Thinking → Processing

Saying AI “thinks” is comforting in the way that saying a toaster “wants” bread is comforting. It gives us the illusion of familiarity while explaining absolutely nothing.

Human thinking comes with deliberation, doubt, and the occasional existential spiral in the shower. What happens inside a large language model involves matrices, probabilities, and an alarming amount of arithmetic, all at a speed that suggests enthusiasm rather than contemplation.

Calling this “thinking” makes us feel like we understand it. We don’t. We’ve simply used a warm, human word to cover a cold, mechanical process and hoped it wouldn’t notice.

Better term: Processing. It’s mechanistic and accurate. The system processes inputs and generates outputs through computation. No claims about inner experience, no borrowed credibility from human cognition. Just a description of what’s actually occurring.

Reasoning → Pattern Completion

“Reasoning” carries even more philosophical baggage. It implies logical inference, the ability to move from premises to conclusions through valid steps. When AI companies tout their models’ “reasoning capabilities,” they’re borrowing credibility from centuries of logical tradition.

But what these systems do might be better described as sophisticated pattern completion. They’ve processed vast amounts of text containing reasoning, and they generate outputs that pattern-match to reasoning-shaped discourse. Sometimes the results are genuinely useful. Sometimes they’re confident nonsense dressed in the syntax of logic.

The distinction matters because reasoning implies reliability. If a system truly reasons, we can trust its conclusions when its premises are sound. Pattern completion offers no such guarantee.

Better term: Pattern completion. This describes the actual mechanism: the system completes patterns based on statistical relationships learned from training data. It might produce output that appears to be reasoning, but the underlying process is prediction and simulation, not deduction.

Understands → Correlates

To say an AI system “understands” a question is to wander directly into one of philosophy’s oldest bar fights. Understanding implies meaning, reference, and a connection between symbols and the world.

AI systems manipulate symbols brilliantly. Whether those symbols mean anything to the system itself is a question best left to philosophers and people who enjoy being unhappy.

Practically speaking, the system correlates inputs with outputs based on patterns learned from data. That is observable. Understanding is not.

Better term: Correlates. The system correlates inputs with likely outputs based on patterns in training data. It maps relationships between symbols without necessarily grasping what those symbols refer to. Correlation is demonstrable. Understanding is not.

Hallucinates → Confabulates

“Hallucination” is a deeply unhelpful word here. Hallucinations imply perception gone wrong. AI systems do not perceive anything. They have no baseline reality from which to deviate.

When an AI produces false information, it is not malfunctioning. It is behaving exactly as designed: generating the most statistically plausible continuation of a prompt, whether or not reality approves.

Calling this a hallucination makes it sound like a bug. It is not. It is a feature that becomes inconvenient when truth matters.

Better term: Confabulates. In psychology, confabulation is the production of fabricated information to fill gaps, without intent to deceive. The person believes what they’re saying. This maps precisely to what AI systems do: they generate plausible-sounding content to complete a pattern, with no mechanism to distinguish fact from fabrication.

Autonomy → Heteronomy

Perhaps no word causes more confusion than “autonomy.” In human contexts, autonomy implies self-governance, the capacity to set one’s own goals and act on one’s own values. When we describe AI systems as “autonomous,” we suggest they possess something like agency.

What we usually mean is something narrower: the system can take actions without immediate human approval for each step. An “autonomous” vehicle follows traffic laws; it doesn’t decide to take a road trip. An “autonomous” trading algorithm executes within parameters; it doesn’t set investment philosophy.

The most annoying part of this wordplay is that the core missing component is “self.” But if you’re genuinely interested, here are 13 other arguments to demonstrate why AI is not autonomous.

Conflating operational independence with genuine autonomy creates governance gaps. We build oversight for tools that follow rules while deploying systems that increasingly set their own intermediate goals. The vocabulary hasn’t kept pace with the capability.

Better term: Heteronomy. Rule by others. Which is, inconveniently, the truth.

The Work of Recalibration

Ontological recalibration isn’t about finding perfect terminology. Language is always imperfect. It’s about choosing words that clarify rather than obscure, that describe what’s actually happening rather than what we imagine, fear, or hope.

This matters because words shape decisions. Organizations that think in terms of “intelligent” systems over-trust AI outputs. Leaders who believe AI “reasons” skip verification steps. Engineers who frame errors as “hallucinations” treat symptoms rather than causes. Better vocabulary leads to better judgment.

The technology is changing faster than our language. Until we recalibrate how we talk about AI, we’ll keep making decisions based on words that were never quite right and are becoming less right by the day.

Which of these six words has been shaping your assumptions without you noticing?

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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.