Semantics Isn’t Just Semantics

Precision is necessary in science, business, security, finance, and even when you go to a restaurant and expect your order to arrive. A wrong variable, misplaced decimal, or ambiguous term can crash a rocket, tank a market, or trigger a false alarm. Yet, in artificial intelligence, precision is often treated as optional. It’s waved off as semantics.

When we call automation “autonomy,” we don’t just blur lines. We erase them. We replace understanding with assumption. We let metaphors pose as facts. And in doing so, we quietly rewrite the rules of innovation, ethics, and accountability.

This isn’t a philosophical detour. It’s about clarity, precision, and responsibility. When we use words carelessly, we create confusion not just for ourselves but for policymakers, businesses, educators, and the public. We risk making false AI assumptions, which can lead to poor decisions, misplaced trust, and missed opportunities.

Here are the top 20 misused, misunderstood, and misrepresented terms in AI.

(I’m confident there will be some disagreements. That is the power of hype.)

Most Overhyped Marketing Terms

1. Autonomous

Misuse: Often confused for automation or used to describe systems like self-driving cars or agents that seem independent.

Reality: Autonomy in AI doesn’t exist, yet. Autonomy, from the Greek autos (self) and nomos (law), originally referred to self-rule in moral and political philosophy or the ability to govern oneself by one’s own principles. In AI, autonomy would require internal authorship of goals, moral reasoning, and the capacity to justify actions beyond external programming. No system today meets that threshold. AI systems operate within boundaries set by human designers, following instructions or optimizing outcomes based on predefined objectives. They cannot question those objectives, reflect on their purpose, or reframe their role. The presence of human-authored goals disqualifies AI from being truly autonomous.

2. Self-Governance

Misuse: This is assumed to mean machine learning or the ability to adjust its behavior.

Reality: Self-governance refers to establishing, reflecting on, and modifying one’s own rules or principles. It comes from moral and political philosophy, not engineering. Machine learning enables systems to adjust their outputs based on patterns in data, but this is optimization, not independent decision-making. AI does not select its own goals, question its directives, or redefine its purpose. It follows parameters given by designers and responds according to pre-set logic. Mistaking this for self-governance blurs the line between control and independence. A system that improves performance within human constraints is still governed externally. True self-governance would require a system to evaluate its constraints, challenge them, and create its own framework for behavior. No existing AI has the architecture or authority to do that.

3. Intelligence

Misuse: Assumed to reflect human-like thought or reasoning.

Reality: The term “intelligence” in artificial intelligence originates from John McCarthy in 1956, who used it to describe machines that could replicate human-like problem-solving behaviors, such as solving math problems or playing chess. It was intended to reflect observable actions, not cognitive richness or understanding. Over time, however, the term became misleading as AI systems, capable of mimicking intelligent behavior, were misunderstood as possessing true intelligence. In reality, AI doesn’t reason or reflect. Machines process data through statistical models, producing outputs based on patterns, not comprehension, context, or ethical considerations. The term has created a disconnect between technical accuracy and popular imagination, especially in how AI is marketed and discussed.

4. General AI (AGI)

Misuse: Confused with current AI systems that simulate language fluency, leading many to believe AGI is already here or just a few iterations away.

Reality: AGI refers to a hypothetical form of artificial intelligence that can reason, learn, and adapt across any domain, much like a human. It’s not just about producing smart outputs. It would require memory, context awareness, causal reasoning, self-reflection, and the ability to form goals. No current AI system comes close. Today’s models are narrow and task-specific, trained to optimize performance on well-defined problems. AGI is often confused with autonomy or sentience, but these are different thresholds. Autonomy implies self-governance; AGI implies broad cognitive flexibility. Both are still out of reach. The actual barriers to AGI aren’t just neural architectures. They’re fundamental gaps in system design, infrastructure, and real-time computational power. Assuming we’re close to AGI, misunderstands how far we are from replicating the architecture of thought itself.

5. Agent

Misuse: Implies a system with independent decision-making powers.

Reality: In AI, an agent is simply an entity that perceives its environment and takes action to achieve a human-defined objective. Agents are often portrayed as autonomous actors but are fundamentally bound to structured, rule-based workflows. Even when an agent includes machine learning, it executes an automated process. Its behavior is governed by policies, models, or conditions defined in advance. The complexity of a method does not equal independence. No AI agent today selects its purpose or redefines success. All goals, parameters, and boundaries originate from human input. The term agent can mislead by implying self-direction where none exists.

6. Agentic AI

Misuse: Believed to describe intelligent agents or AI systems that can create and follow processes independently.

Reality: Agentic is often confused with agent because of the shared root, but the concepts differ. Agentic behavior refers to agency, the capacity for self-originated, intentional action. It implies that a system can define its goals, initiate processes without external prompting, and act independently of its creators. Even when an AI system follows dynamic processes or adapts through machine learning, those adaptations occur within human-defined boundaries. Although highly advanced systems do have elements of agency, describing AI as agentic suggests it has crossed into self-determination when it is still automating responses to known objectives. Autonomy is the ability to self-author goals. Agentic behavior is the capacity to act on those goals.

7. Personalization or AI Memory

Misuse: This is believed to mean the AI is learning about you, remembering past interactions, or tailoring itself to your preferences over time.

Reality: Most AI systems do not personalize in the way people think. Short-term continuity (such as a chatbot remembering something you said earlier in a conversation) is a function of session-based context, not memory. Longer-term personalization often does not exist unless explicitly designed. What many mistake for memory or personalized learning is called Reinforcement Learning from Human Feedback (RLHF). You or the user guide the model toward more acceptable or useful answers through continued use, which I have previously called the “you bias.” RLHF teaches models to sound helpful, not necessarily accurate, contextual, or self-improving. It also does not tailor the model to any one user. While RLHF improves average response quality, it introduces its own biases and cannot adapt to individual preferences unless fine-tuned specifically for that use case.

Technical Terms That Sound Simpler Than They Are

8. Model

Misuse: Treated as a simple software object or downloadable file.

Reality: A model in AI is not a simple file or static tool. It is a mathematical system trained through massive computational processes to identify patterns, predict outcomes, or perform specific tasks. Models are built on statistical inference and shaped by billions of data-driven adjustments. What seems like a black box response is the output of a complex network of weighted parameters. Models do not contain knowledge or understand that knowledge. They are trained on knowledge to estimate probabilities of the knowledge.

9. Parameter

Misuse: Confused with user-adjustable settings or preferences.

Reality: Parameters are the internal values that shape how an AI model processes input. In a neural network, these include weights and biases that are adjusted during training to improve performance. Users don’t set these values manually. This is also where bias has the potential to enter the system. If the training data reflects social, cultural, or historical patterns of discrimination, those patterns are encoded into the parameters and influence how the model responds. Parameters determine behavior, but they inherit perspective from the data.

10. Token

Misuse: Thought to be equivalent to the cost per word in a sentence.

Reality: Tokens are the basic units processed by language models. A token can be a word, part of a word, or even punctuation. For example, “understanding” might be split into “under” and “standing.” Language models like GPT generate responses one token at a time, predicting the next token based on prior context. Pricing is often based on tokens, but the cost reflects inference, not directly on training. However, the frequency of certain words training, such as “please” and “thank you,” increases computational load and energy use. As OpenAI CEO Sam Altman recently noted, processing these polite phrases has cost the company potentially tens of millions of dollars. While well-intentioned, such phrases contribute to higher training costs, which may indirectly influence token pricing and operational expenses over time.

11. Overfitting

Misuse: Considered a software glitch or bug.

Reality: Overfitting occurs when a model becomes too specialized for the training data and loses the ability to generalize to new, unseen data. It signifies that the model has memorized patterns rather than learned underlying structures. In practice, this can lead to high performance in testing environments but failure in real-world deployment. Detecting and correcting overfitting is essential for building reliable AI.

Vague Buzzwords

12. Alignment

Misuse: Assumed to mean that AI is friendly or moral.

Reality: Alignment means aligning an AI system’s behavior with human goals and ethical boundaries. It’s not about “niceness” but about outcome control. Misaligned systems may follow their programmed goals in harmful, inefficient, or unpredictable ways. Proper alignment requires technical, ethical, and societal oversight.

13. Hallucination

Misuse: Treated as a sign of creativity or intelligence.

Reality: In AI, hallucination refers to confident yet false outputs like a language model inventing citations or factual claims. These are not imaginative flourishes; they are failures of grounding and verification. Hallucinations arise when a model fills in gaps with high-probability guesses that sound correct but are untrue. They’re a core risk of generative AI, especially in professional and scientific domains.

14. Explainability

Misuse: This is believed to mean the AI can explain its reasoning clearly.

Reality: Most AI models, especially deep learning systems, are black boxes. Explainability involves using interpretive techniques (like feature attribution or visualization) to approximate why a model made a specific decision. These are not direct explanations from the model itself. They are post hoc inferences that help humans understand behavior patterns. True explainability is still an open challenge in the field.

Confusion Due to Media

15. Sentience

Misuse: Used to describe emotional or conscious AI.

Reality: Sentience refers to the capacity for subjective experience, something no AI system has. AI doesn’t feel, want, desire, or suffer. It doesn’t possess an internal state of awareness. Claims of sentient AI conflate simulation with experience. Just because a model says it has feelings doesn’t mean it does. It’s mimicking, not experiencing.

Data from Star Trek is sentient, The Terminator is not. (Here’s a controversial one, I’d argue that C-3PO is also not sentient.)

16. Neural Network

Misuse: Marketed as a digital brain or simple thinking machines.

Reality: Neural networks are collections of mathematical operations loosely inspired by the brain’s structure, but the similarity stops there. A neural network doesn’t reason, recall, or think. It processes input through layers of weights to predict output. The biological metaphor sells well, but it creates confusion about the nature of these systems.

17. Natural Language Understanding (NLU)

Misuse: Assumed to mean the AI truly understands human language.

Reality: NLU systems parse language to extract intent, sentiment, or entities based on learned patterns. They recognize structure, not meaning. These models do not grasp nuance, humor, sarcasm, or context as humans do. They simulate understanding by producing outputs that often appear coherent, but they don’t comprehend what the words represent. This is why AI can not understand irony, sarcasm, emotional context, or humor like some of the recent April Fools jokes.

18. Chatbot

Misuse: Equated with advanced AI like ChatGPT.

Reality: A chatbot is any system designed to simulate conversation. Many are simple rule-based scripts with pre-written responses. Others, like GPT, use advanced language models. Not all chatbots are AI-powered, and not all AI is conversational. Treating all chatbots as intelligent misrepresents their capabilities and risks overpromising to users. The first chatbot in history was ELIZA, created in 1966 by Joseph Weizenbaum, a computer scientist at the MIT Artificial Intelligence Laboratory.

Subtle but Important Distinctions

19. Supervised vs. Unsupervised Learning

Misuse: Thought to mean human-led vs. AI-led learning.

Reality: Supervised learning uses labeled data, where the “correct” answer is known and used during training. Unsupervised learning involves unlabeled data, seeking patterns, clusters, or structures without human-defined outputs. Neither type implies independence or creativity. Both depend on human-defined objectives, data structures, and validation metrics.

20. Grounding

Misuse: This is believed to mean AI systems understand the world.

Reality: Grounding refers to the connection between symbols like words or images and their real-world meaning through sensory or contextual experience. Humans learn that the word “apple” refers to a specific physical object we can see, touch, and taste. That association is grounded in perception. AI, on the other hand, processes words statistically. If you describe something as “round, green, fruity, and tasty,” the AI doesn’t know whether you mean an apple or a grape. It selects based on patterns in the data, not experience. It has no access to physical reality, so it cannot form true associations between language and the world. Without grounding, AI cannot comprehend what it says. It cannot verify, experience, or reason about the content it generates. It simulates meaning through probabilities, not understanding.

The Words We Use

Language is a design tool. If we get the labels wrong, we build the wrong systems. We justify the wrong outcomes. That’s not semantics. That’s strategy.

The path to responsible AI does not begin with better models. It begins with better definitions. We need to know what we are creating before we can build technology that understands us. That means getting specific about the difference between learning and knowing, output and insight, and imitation and autonomy.

Precision in language is not an academic exercise. It is the first line of defense against inflated promises and unearned trust.

The words we use in AI might be the most important code we write.

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Chris Hood is a customer-centric AI strategist and author of the #1 Amazon Best Sellers “Infailible” and “Customer Transformation,” and has been recognized as one of the Top 40 Global Gurus for Customer Experience, with over two decades in AI product and sales.

To learn more about building customer-centric organizations or improving your customer experience, please contact me at chrishood.com/contact.