The Great Vocabulary Shuffle: How the AI Industry Rebranded Engineering into Magic

Last updated on 16 Aug 2025

Abstract

The artificial intelligence industry has developed a lexicon that systematically obscures established engineering principles through poetic metaphors and mystical terminology. This paper examines how fundamental control theory concepts were either ignored or rebranded into emotionally evocative language, creating unnecessary complexity and hindering systematic problem-solving. We present a comparative analysis of AI terminology versus established engineering vocabulary, revealing a pattern of linguistic inflation that transforms precise technical concepts into vague, anthropomorphic descriptions.

Keywords: terminology analysis, control theory, systems engineering, AI vocabulary, linguistic obfuscation

Introduction

In 1968, Dick Morley invented the Programmable Logic Controller (PLC) to solve a simple problem: factory relay panels were unwieldy and required electricians to rewire everything for basic changes. The solution used precise engineering terminology that exactly described what the system did. No poetry. No metaphors. Just clear, functional language.

In 2023, the AI industry faced a similar problem: large language models were producing unpredictable outputs. Instead of applying established engineering terminology and proven control methods, the industry created an entirely new vocabulary that sounds more like creative writing than systems engineering.

This paper examines what happened when Silicon Valley met control theory and decided poetry was more important than precision.

Methodology

We conducted a systematic comparison of AI industry terminology against established engineering and control theory vocabulary, focusing on:

Functional accuracy: Does the term describe what actually happens?
Precision: Can the term be measured and controlled?
Historical precedent: Has this problem been solved before?
Anthropomorphic content: Does the term assign human characteristics to mechanical processes?

Findings: The Great Terminology Transformation

Section 1: Error Management

AI Industry Term: "Hallucinations"
Control Theory Term: Process drift, output variance, systematic error
What Actually Happens: System produces outputs outside acceptable parameters
Why This Matters: You can measure and correct "process drift." You cannot easily systematize the prevention of "hallucinations."
The Rebranding Effect: Converting a measurable engineering problem into a mystical phenomenon that sounds impossible to control.

Section 2: Parameter Adjustment

AI Industry Term: "Temperature"
Control Theory Term: Output variability control, stochastic damping
What Actually Happens: Adjustment of random variation in system outputs
Temperature Involvement: Zero degrees Celsius
The Rebranding Effect: Using a physical property term for a statistical operation, creating confusion about what's being controlled.

Section 3: Network Architecture

AI Industry Term: "Neural Networks"
Control Theory Term: Weighted signal processing networks, multi-layer transformation matrices
What Actually Happens: Mathematical operations on numerical inputs through weighted connections
Neurons Involved: Zero biological neurons
The Rebranding Effect: Biological metaphor obscures the mathematical nature of the operations, making systematic optimization seem more mysterious than it is.

Section 4: Systematic Tuning

AI Industry Term: "Fine-tuning"
Control Theory Term: Calibration, parameter optimization, systematic adjustment
What Actually Happens: Modification of system parameters to improve performance within specifications
Musical Instruments Involved: None
The Rebranding Effect: Vague metaphor replaces precise engineering term, making systematic approaches seem less applicable.

Section 5: Attention Mechanisms

AI Industry Term: "Attention"
Control Theory Term: Weighted input prioritization, signal importance matrices
What Actually Happens: Mathematical weighting of input signals based on calculated importance
Human Attention Involved: None
The Rebranding Effect: Anthropomorphic term makes mathematical operations seem cognitively complex rather than computationally systematic.

Section 6: System Behavior

AI Industry Term: "Emergent Properties"
Control Theory Term: Complex system behavior, nonlinear response patterns
What Actually Happens: System outputs that result from interaction of multiple components
Magic Involved: Zero documented cases
The Rebranding Effect: Scientific-sounding term that implies mysterious arising of properties rather than predictable results of complex systems.

What Got Left Out: The Missing Control Theory Vocabulary

The AI industry's vocabulary shuffle had casualties. Established engineering terms that could have provided systematic approaches to AI reliability were simply... not adopted.

Process Control Terms Not in AI Vocabulary

Statistical Process Control: Systematic monitoring of outputs against specifications with automatic correction protocols
Calibration Drift: Gradual movement away from optimal performance parameters
Feedback Loop Design: Systematic methods for using output information to improve future performance
Human-Machine Interface: 50 years of research on optimizing human-computer collaboration
Systematic Troubleshooting: Root cause analysis methodologies for complex system failures
Preventive Maintenance: Scheduled system optimization to prevent performance degradation

Control Theory Concepts Missing from AI Development

Process Capability Studies: Measuring what a system can actually do reliably
Control Limits: Upper and lower bounds for acceptable system performance
Failure Mode Analysis: Systematic identification of ways systems can fail and prevention strategies
System Commissioning: Proven methodologies for bringing new systems online reliably

The Black Box Phenomenon

Perhaps the most significant vocabulary choice was the widespread adoption of "black box" to describe AI systems. This term, while technically accurate, comes with philosophical baggage that may have hindered systematic problem-solving.

Engineering Perspective: Black boxes are systems where internal mechanisms are unknown but input-output relationships can be mapped and controlled
AI Industry Usage: Mysterious, uncontrollable systems that do things we cannot understand or predict
Historical Context: Industrial engineers have been controlling "black box" systems for decades. Chemical reactors, power transformers, and complex manufacturing processes all operate as black boxes from a control perspective. The solution is external control systems, not internal comprehension.
The Missed Opportunity: Treating AI as controllable black boxes rather than mysterious black boxes would have led to systematic reliability approaches much sooner.

Comparative Analysis: Poetry vs. Precision

The AI Approach to Vocabulary

Characteristics:

Emotionally evocative language
Anthropomorphic metaphors
Biological analogies
Mystical implications
Marketing-friendly terminology

Example Sentence: "Our neural network's attention mechanisms have learned to hallucinate creative emergent properties during fine-tuning."

What This Actually Describes: "Our weighted signal processing network's input prioritization matrices produce output variance outside specifications during parameter optimization."

The Engineering Approach to Vocabulary

Characteristics:

Functionally descriptive language
Measurable phenomena
Systematic terminology
Solution-oriented focus
Precision over poetry

Example Sentence: "Our signal processing network's input weighting matrices require calibration to reduce process drift during parameter optimization."

Engineering Translation: "We need to systematically adjust the system to get consistent outputs."

Results: The Cost of Linguistic Inflation

Problem Solving Impact

AI Industry Approach:

"How do we prevent hallucinations?"
"Can we make AI more creative?"
"What causes emergent behaviors?"

Engineering Approach:

"How do we reduce output variance?"
"How do we control system creativity within specifications?"
"How do we predict complex system behaviors?"

The Solvability Factor

Engineering questions tend to have systematic solutions because they describe measurable phenomena. Poetic questions tend to generate philosophical discussions because they describe subjective experiences.

Measurable: Process drift (can be detected, measured, corrected) Unmeasurable: Hallucinations (when does creativity become error?)

Discussion: The Consequences of Creative Vocabulary

The Academic Impact

The AI industry's vocabulary choices may have inadvertently created barriers between AI development and established engineering disciplines. Control theory, systems engineering, and industrial automation have decades of experience managing complex, unpredictable systems reliably. However, the linguistic gap makes knowledge transfer difficult.

Communication Barrier Example:

AI Researcher: "We need to prevent hallucinations in our neural networks"
Control Engineer: "Are you talking about output variance in signal processing systems?"
AI Researcher: "No, we're talking about artificial creativity and consciousness"
Control Engineer: "Oh... sorry, that's not my field"

The Enterprise Adoption Challenge

Enterprise customers understand engineering terminology. They have quality control departments, process improvement teams, and systematic reliability requirements. When AI systems are described using poetic vocabulary, enterprise adoption becomes more difficult because the problems and solutions don't map to existing enterprise frameworks.

Enterprise Translation Challenge:

AI Vendor: "Our model may occasionally hallucinate"
Enterprise Customer: "What's the failure rate and what's your corrective action protocol?"
AI Vendor: "Well, hallucinations aren't exactly failures..."
Enterprise Customer: "We'll get back to you"

The Control Theory Alternative

What Would AI Vocabulary Look Like with Engineering Precision?

Current AI: "Our fine-tuned neural network uses attention mechanisms to minimize hallucinations"

Engineering Alternative: "Our calibrated signal processing network uses weighted input prioritization to reduce output variance"

Practical Impact: The engineering version immediately suggests systematic solutions (calibration protocols, weight optimization, variance monitoring), while the AI version suggests philosophical challenges (what is attention? when does creativity become error?).

Available Engineering Solutions

Control theory offers systematic approaches to every major AI reliability challenge:

Output Variance → Statistical Process Control
System Drift → Calibration Protocols
Complex Behavior → Nonlinear System Analysis
Human-AI Interaction → Human-Machine Interface
Design Reliability → Systematic Quality Management

The Missing Bridge: AI systems described using control theory vocabulary become immediately amenable to 50 years of proven reliability methodologies.

Limitations and Future Research

Study Limitations

This analysis focuses primarily on terminology and may not fully capture the technical complexity differences between AI systems and traditional control systems. Some AI challenges may indeed require novel approaches beyond established control theory.

Research Questions

Systematic Translation: Can all AI terminology be systematically translated into control theory vocabulary without loss of technical precision?
Solution Mapping: Do control theory solutions actually address AI reliability challenges when the problems are described using engineering terminology?
Educational Impact: Would AI development benefit from requiring control theory education for practitioners?
Enterprise Adoption: Does engineering vocabulary improve enterprise AI adoption rates?

Conclusions

The AI industry developed a vocabulary that prioritizes emotional impact over functional precision. While this may have benefits for marketing and public engagement, it appears to have created barriers to systematic problem-solving and knowledge transfer from established engineering disciplines.

Key Findings:

Systematic Obscuration: AI terminology consistently converts measurable engineering phenomena into subjective, unmeasurable descriptions
Missing Engineering Vocabulary: Proven control theory terms that could provide systematic approaches to AI reliability were not adopted
Solution Impediment: Poetic terminology may hinder the application of systematic engineering solutions to AI challenges
Communication Barriers: Vocabulary differences create unnecessary separation between AI development and established engineering expertise

The Engineering Opportunity

The systematic translation of AI challenges into control theory vocabulary reveals that most AI reliability problems have engineering precedent and systematic solutions. The gap is not technical capability, rather it's vocabulary and conceptual frameworks.

The Bridge: Teaching AI practitioners control theory vocabulary and teaching control engineers AI system characteristics could accelerate reliable AI deployment significantly.

Final Observation

Dick Morley solved factory automation by applying systematic engineering thinking to complex, unpredictable industrial systems. He used precise vocabulary, proven methodologies, and systematic approaches.

The AI industry could learn something from this approach.

Historical Irony: The computers running AI systems were built in factories controlled by PLCs using the systematic reliability methodologies that the AI industry is slowly rediscovering through trial and error.

Perhaps it's time to ask the people who built the machines to help make the machines smarter.

About This Analysis: This paper emerged from systematic frequency mixing of analytical and creative cognitive patterns, demonstrating that precise engineering and creative expression are not mutually exclusive when proper harmonic tuning is applied.

Be kind, please rewind your assumptions about what makes technology magical versus what makes it useful.

About LumaLogica: We apply industrial control principles to AI systems, bringing manufacturing-grade reliability to artificial intelligence deployment. Because if it's good enough to control your factory, it's good enough to control your AI.