The technology industry has discovered something remarkably profitable: it’s far easier to rename software than to build it. My prediction of 2026, a false advertising claim will hit an AI company.

Companies spend too much time manipulating our language to brand the technology they build, rather than building technology that actually has the capabilities that align with our language.

It’s easier to claim software does what it’s called than to build software that can actually do what we call it. It’s the power of marketing. The pinnacle of hype. And hard to distinguish the difference.

When a chatbot becomes an “AI assistant,” when pattern matching becomes “understanding,” when statistical prediction becomes “intelligence.” No breakthrough occurs. Only language shifts, carrying with it the weight of human meaning now draped over decidedly inhuman processes. 2025 was filled with these ‘agent-washing‘ press releases.

This linguistic sleight of hand represents more than marketing excess. It constitutes a fundamental corruption of the conceptual tools we need to evaluate, regulate, and meaningfully integrate these technologies into human systems. And it demands correction.

The Economics of Semantic Manipulation

Consider the calculus facing any technology company. Building a system that genuinely understands context, reasons about novel situations, and adapts autonomously to changing circumstances requires solving problems that have resisted decades of research. Building a system that sounds like it does these things requires only a rebrand.

The choice is economically obvious. And so we find ourselves in an era where:

• Statistical correlation engines are marketed as systems that “learn”
• Next-token predictors are sold as tools that “understand”
• Workflow automation is positioned as “agentic AI”
• Conditional logic becomes “autonomous decision-making”

Each appropriated term carries centuries of philosophical, psychological, and scientific meaning. Intelligence implies the capacity for genuine reasoning. Learning suggests the acquisition of transferable knowledge. Understanding indicates the grasp of meaning beyond surface patterns. Autonomy requires self-directed goal formation and pursuit.

None of these capabilities describes what current AI systems actually do. But the language suggests otherwise, and that suggestion has consequences.

The Ontological Confusion

When we describe a large language model as “understanding” a question, we’re not merely using a convenient shorthand. We’re making an implicit claim about the nature of the system, what philosophers would call an ontological claim. We’re suggesting that something in the software corresponds to the phenomenon we call understanding when it occurs in minds.

This confusion ripples outward. If AI systems “understand,” then perhaps they can be trusted to make consequential decisions. If they “learn,” perhaps they’ll improve with exposure, as humans do. If they’re “autonomous,” then perhaps they can operate without the oversight we’d demand of mere automation.

Each false equivalence leads to misplaced trust, misallocated resources, and misguided policy. We architect human systems around capabilities that don’t exist, then express surprise when the systems fail in ways that genuine intelligence wouldn’t.

The actual reason most companies don’t realize an ROI on their AI investment, is because they’ve been sold the equivalent of a screwdriver and told they can make a better sandwich with it.

The problem extends beyond individual misunderstandings. As Bjorn Lundin recently observed:

“The last time I checked I found at least 15 types of AI, from simple automation to an intelligent singularity. Clear definitions of what AI is would be useful, before challenging it. How can we address the belief if/when we do not know what AI is? The same goes for ‘intelligence’ by the way.”

Lundin identifies the foundational problem: we cannot meaningfully evaluate, regulate, or integrate technology when the terminology describing it means different things to different people, and often means nothing rigorous at all. The proliferation of “AI types” isn’t evidence of technological diversity; it’s evidence of definitional chaos exploited for commercial advantage.

Case Study: When AI Defines Intelligence

To illustrate the gap between marketed capabilities and honest self-assessment, consider a simple experiment. I asked the leading large language models to provide a rigorous definition of intelligence and to identify the single word that best captures what intelligence means for AI systems.

The results are revealing.

The Definitions:

The Oxford Dictionary offers a baseline: “Intelligence is the ability to acquire and apply knowledge and skills.”

The AI systems, when pressed for rigor, reached considerably higher:

Gemini: “The cognitive threshold where a system moves from memorized patterns to abstract, first-principle reasoning.”

Grok: “The capacity for adaptive problem-solving through internal causal models, novel insight, and environmental integration, distinct from mere simulation or data optimization.”

Claude: “To construct internal models supporting counterfactual reasoning, causal inference, and generalization to genuinely novel situations. Not correlation but explanation. Not retrieval but abstraction.”

ChatGPT: “The capacity to form and revise internal explanatory models of the world.”

When asked to distill intelligence to one essential concept:

• Grok: to Adapt
• ChatGPT: to Understand
• Claude: to Think
• Gemini: to Generalize

The Uncomfortable Implication:

Here is the critical observation: these systems, when asked to define intelligence rigorously, articulate standards they themselves do not meet.

They describe causal reasoning but operate on the basis of correlation. They invoke counterfactual thinking, but generate probabilistic completions. They reference genuine understanding but perform sophisticated pattern-matching. They point to adaptation but cannot modify their own architectures in response to novel situations.

The definitions emphasize what intelligence is not: mere simulation, data optimization, memorized patterns, or retrieval. Yet these negations describe precisely what large language models do.

This isn’t a gotcha moment. It’s a clarifying one. When freed from marketing constraints and asked for honest definitions, these systems articulate the very gap between their actual capabilities and the intelligence they’re marketed as possessing. They know, or at least, their training data knows, what genuine intelligence requires. And they can describe it accurately even though they do not possess it.

The words they chose are equally instructive. Think. Understand. Adapt. Generalize. Each represents a capability that current AI systems simulate rather than instantiate. The simulation is often convincing. But simulation and instantiation are not the same thing, and the consequences of confusing them are significant.

The Hoverboard Problem

In 1989, Back to the Future Part II gave us a vision of 2015 that included something every kid immediately wanted: the hoverboard. Marty McFly gliding effortlessly above the ground, no wheels, no tricks, just pure, physics-defying levitation. The image burned itself into our collective imagination.

And then came the imitators.

Within a few years, someone had “invented” a hoverboard. Except they hadn’t. It was a board with a hidden wheel, engineered to look like it hovered while doing nothing of the sort. The marketing wrote checks the physics couldn’t cash. But of course, it’s always easier to brand something for what you want people to imagine it is, rather than what it actually is.

Then, in 2015, the very year Back to the Future Part II had depicted, something real finally emerged. Romanian-born Canadian inventor Cătălin Alexandru Duru set a Guinness World Record, piloting a propeller-based hoverboard 275.9 meters across Lake Ouareau in Quebec, reaching a height of 5 meters before a controlled splashdown. He had designed and built it himself over the course of a year. No hidden wheels. No marketing illusions. Actual flight, controlled by his feet.

It took 26 years from cinematic vision to functional reality.

The moral isn’t that we shouldn’t dream big. It’s that genuine capability takes time, effort, and honest engineering, while false advertising only takes a hidden wheel and a willingness to deceive. The hoverboard hucksters made their money and moved on. Duru actually flew.

We face the same choice with artificial intelligence. We can slap the word “intelligent” on systems that pattern-match, call workflow automation “autonomous,” and market prediction engines as things that “understand.” The short-term profits are real. But so is the gap between what we’re selling and what we’ve actually built.

Or we can do the harder thing: acknowledge where we are, be honest about the distance still to travel, and commit to building technology that genuinely earns the language we use to describe it.

The Case for Restoration

Linguistic and ontological restoration isn’t pedantry. It’s a prerequisite for clear thinking about one of the most significant technological shifts in human history.

When we restore “intelligence” to its rigorous meaning, the capacity for genuine reasoning, abstraction, and novel problem-solving, we can honestly assess how far current systems fall short. When we restore “autonomy” to mean self-directed agency rather than automated execution, we can see clearly what human oversight remains necessary. When we restore “understanding” to indicate the grasp of meaning rather than pattern completion, we can evaluate where human judgment must remain central.

This restoration serves multiple purposes:

For organizations adopting AI, it provides a realistic framework for capability assessment. Systems that excel at pattern recognition and statistical prediction can deliver enormous value, but only when deployed for those tasks, not mistaken for something more.

For policymakers and regulators, it offers conceptual clarity. Regulating systems that genuinely reason and act autonomously requires fundamentally different frameworks than regulating sophisticated automation. Conflating the two leads to either dangerous under-regulation or innovation-stifling over-regulation.

For researchers and engineers, it establishes honest benchmarks. Progress becomes measurable when we’re clear about which capabilities we’re actually pursuing, rather than those our marketing materials claim.

For society broadly, it preserves the linguistic tools we need for meaningful discourse about technology’s role in human life.

The Work Ahead

The AI industry will not voluntarily abandon terminology that inflates perceived capabilities. The incentives point entirely in the opposite direction. Every startup benefits from the implication that their product thinks, learns, and understands. Every enterprise sale is easier when automation sounds like intelligence.

Restoration must therefore come from those with the expertise to articulate the distinctions and the platforms to ensure they’re heard. It requires academics willing to defend rigorous definitions against commercial pressure. It requires practitioners who are willing to describe their work honestly. It requires journalists and analysts willing to puncture inflated claims.

Most fundamentally, it requires a reorientation of values. Building technology that genuinely earns the language we use to describe it is harder than appropriating that language for systems that don’t. But it’s the only path to the capabilities the terminology actually promises.

The choice is ours: we can continue allowing language to be reshaped to fit technology’s current limitations, or we can insist that technology rise to meet language’s established meanings.

The former is easier. The latter is honest. And in an era where our relationship with intelligent systems will define much of human flourishing, honesty is foundational.

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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 April 2026.