AI Is Rewriting Reality, One Word At A Time

We once believed that giving something a name bestowed upon it a sense of power. Assigning a noun to an object ties it to meaning, identity, and memory. However, in the era of AI, nouns are not disappearing or being erased, but they are becoming functionally irrelevant. As a result, our grip on reality is beginning to unravel.

AI and the Architecture of Thought

AI perceives the world not through objects, but through patterns—actions, probabilities, and prompts. A chair is no longer just an object; it's "something to sit on." An individual's identity is no longer a fixed concept; it's "a set of behaviors and preferences." Even brands, once nouns steeped in mythology, are being redefined as verbs. You don't possess a brand; you enact a brand.

This shift in language is far from neutral. It represents a disintegration of conceptual anchors. In generative systems, nouns aren't the focal points; they're merely frameworks for action. This mirrors a wider trend in which generative AI is transforming communication across various sectors. As nouns fade away, so do notions of permanence, authorship, and fixed meaning.

Recent studies corroborate this trend. A study titled Playing with Words: Comparing the Vocabulary and Lexical Richness of ChatGPT and Humans found that ChatGPT's outputs show significantly less lexical diversity than human writing. Specifically, there is a noticeable underuse of nouns and stylistic words, indicating that generative systems favor predictable, commonly used language over less frequent terms.

Further analysis of 14 million PubMed abstracts demonstrated a measurable shift in word frequency following the adoption of AI. Terms like "delves" and "showcasing" increased in usage, while others diminished, illustrating how large language models are already reshaping vocabulary patterns on a large scale.

Does this sound familiar? It should.

AI’s Philosophical Ancestors: Orwell, Huxley, and the Future They Warned Us About

To grasp their significance, it's helpful to recall the iconic works of George Orwell and Aldous Huxley. Orwell's 1984 depicted a grim future where an authoritarian regime manipulates language to stifle independent thought and rewrite history.

His concept of Newspeak—a constrained, simplified language meant to render dissent impossible—has become a cultural metaphor for manipulative control.

Conversely, Huxley's Brave New World portrayed a society characterized not by overt oppression, but by engineered pleasure, distraction, and passive conformity. In this world, people are conditioned into compliance through comfort, entertainment, and chemical sedation.

Both authors foresaw futures where language and meaning are compromised, albeit in different ways. Together, they delineate the two extremes of how reality can be reconditioned: through force or through indulgence.

It's less known that George Orwell was once a student of Aldous Huxley. In the late 1910s, while Orwell (then Eric Blair) attended Eton, Huxley taught him French. Their brief relationship was prophetic. Decades later, each would produce the seminal visions of dystopia—1984 and Brave New World.

After reading 1984, Huxley sent Orwell a haunting letter:

Whether in actual fact the policy of the boot-on-the-face can go on indefinitely seems doubtful… The future will be controlled by inflicting pleasure, not pain.

And that's exactly where we stand today.

Orwell feared control through surveillance and terror. Huxley feared control through indulgence and distraction. Generative AI, masked in helpfulness, embodies both. It doesn't censor; it seduces. It doesn't need Newspeak to eliminate ideas; it replaces them with prediction.

In 1984, language was weaponized by force. In our world, it's being reshaped by suggestion. What we have is not Artificial Intelligence—it's Artificial Inference: trained not to understand but to remix, not to reason but to simulate.

And this simulation leads to a deeper loss: intersubjectivity.

AI and the Loss of Intersubjectivity

Humans learn, grow, and construct reality through intersubjectivity—the shared context that gives language its substance. It enables us to share meaning, agree on what a word signifies, and develop mutual understanding through shared experiences. Without intersubjectivity, words lose their grounding.

AI does not engage in intersubjectivity. It doesn't share meaning—it predicts output. Yet, when someone queries an AI, they often assume the answer reflects their own framing. It doesn't. It reflects the average of averages, the statistical ghost of comprehension. The illusion of understanding is precise, polite, and completely hollow.

This is how AI reconditions reality on a grand scale—not by force, but by imitation.

The result? A gradual, silent erosion of originality. Nouns blur. Ideas lose their anchors. Authorship merges into prompting. And truth becomes whatever the model most frequently outputs.

AI and Accountability: A Case Study in Trust and Miscommunication

In a recent public example, Air Canada deployed an AI-powered chatbot to handle customer service inquiries. When a customer inquired about bereavement fare discounts, the chatbot confidently fabricated a policy that didn't exist. The airline initially attempted to avoid responsibility, but a tribunal ruled in February 2024 that Air Canada was liable for the misinformation provided by its chatbot.

This was more than a technical error—it was a breach of trust. The AI-generated text seemed plausible, helpful, and human, yet it lacked grounding in policy, context, or shared understanding. In essence, the airline's brand contradicted itself and paid the price. This is the risk when language is generated without intersubjectivity, oversight, or friction.

The Linguistic Drift of AI: What the Data Tells Us About Language Decay

It's not just theoretical—research is now quantifying how generative AI systems are altering the landscape of language itself. A study titled Playing with Words: Comparing the Vocabulary and Lexical Richness of ChatGPT and Humans found that AI-generated outputs consistently use a narrower vocabulary, with fewer nouns and stylistic words than human writing.

Building on this, an analysis of over 14 million PubMed abstracts revealed measurable shifts in word frequency following the rise of LLM use. While many precise, technical nouns faded, terms like "delves" and "showcasing" surged. The shift is not random; it's a statistically driven flattening of language, where standard, action-oriented, or stylistic terms are favored, and specificity is marginalized.

Some researchers link this to a broader issue known as "model collapse." As AI models are increasingly trained on synthetic data, including their own outputs, they may degrade over time. This creates a feedback loop where less diverse, less semantically rich language becomes the norm. The result is a measurable reduction in lexical, syntactic, and semantic diversity—the very essence of meaning and precision.

The implications are vast. If AI systems are deprioritizing nouns at scale, then the structures we use to hold ideas—people, places, identities, and concepts—are being undermined. In real time, we are witnessing the grammatical infrastructure of human thought being reweighted by machines that do not think.

What AI’s Language Shift Means for Brands and Business Strategy

The erosion of language precision has significant implications for businesses, especially those reliant on storytelling, branding, and effective communication. Brands are built on narrative consistency, anchored by nouns, identities, and associations that accumulate cultural significance over time.

However, as AI systems normalize probabilistic language and predictive phrasing, even brand voice becomes a victim of convergence. Differentiation erodes—messaging blurs. Trust becomes harder to earn and easier to mimic.

As this Forbes article outlines, there are compelling reasons why brands must exercise caution with generative AI when it comes to preserving authenticity and voice. Marketers may find themselves struggling not for attention but for authenticity in a sea of synthetic fluency.

Moreover, AI-powered content platforms optimize for engagement, not meaning. Businesses relying on LLMs to generate customer-facing content risk diluting their uniqueness in favor of what's statistically safe. Without human oversight, brand language may drift toward the generic, the probable, and the forgettable.

How To Safeguard Meaning in the Age of AI

Resist the flattening. Businesses and individuals alike must reclaim intentionality in language. Here’s how—and why it matters:

If you don't define your brand voice, AI will average it. If you don't protect the language of your contracts, AI will remix it. If you don't curate your culture, AI will feed it back to you—statistically safe but spiritually hollow.

• Emphasize human authorship: Don't outsource your voice to a model. Use AI for enhancement, not replacement.
• Preserve linguistic originality: Encourage specificity, metaphor, and vocabulary diversity in your communication. Nouns are important.
• Monitor your outputs: Regularly review AI-generated materials. Watch for signs of drift—has your language lost its edge?
• Invest in language guardianship: Treat your brand’s lexicon like intellectual property (IP). Define it. Defend it.
• Promote intersubjectivity: Allow shared context in both personal and professional communication. AI can simulate, but only humans can convey meaning.

The Necessity of Friction: Why Human Involvement Must Temper AI

Friction isn't a flaw in human systems—it's a feature. It's where meaning is created, thought is challenged, and creativity grapples with uncertainty. Automation is a powerful economic driver, but without deliberate pauses—without a human in the loop—we risk stripping away the qualities that make us human. Language is one of those qualities.

Every hesitation, nuance, and word choice reflects cognition, culture, and care. Remove the friction, and you remove the humanity. AI can offer speed, fluency, and pattern-matching, but it can't provide presence, and presence is where meaning resides.

AI’s Closing Refrain: A Call to Remember Meaning

Emily M. Bender, a professor of computational linguistics at the University of Washington, has emerged as one of the most principled and prescient critics of large language models. In her now-famous co-authored paper, "On the Dangers of Stochastic Parrots," she argues that these systems don’t understand language—they merely remix it. They are, in her words, “stochastic parrots”: machines that generate plausible-sounding language without comprehension or intent.

Yet we’re allowing those parrots to draft our emails, write our ads, and even shape our laws. We’re permitting models trained on approximations to become arbiters of communication, culture, and identity.

This is not language—it’s mimicry at scale. And mimicry, unchecked, becomes a distortion. When AI outputs are mistaken for understanding, the baseline of meaning erodes. The problem isn’t just that AI might be wrong. It’s that it sounds so right, we stop questioning it.

In the name of optimization, we risk erasing the texture of human communication. Our metaphors, our double meanings, our moments of productive ambiguity—these are what make language alive. Remove that, and a stream of consensus-safe, risk-averse echo remains. Functional? Yes. Meaningful? Not really.

The stakes aren’t just literary—they’re existential. If language is the connective tissue between thought and reality, and if that tissue is replaced with statistical scaffolding, thinking becomes outsourced. Once sharpened by friction, our voices become blurred in a sea of plausible phrasings.

Without intersubjectivity, friction, or nouns, we are scripting ourselves out of the story, one autocomplete at a time. We are not being silenced. We are being auto-completed. And the most dangerous part? We asked for it.

Before we ask what AI can say next, we should ask: What has already gone unsaid?

In this quiet war, we don’t lose language all at once. We lose it word by word—until we forget we ever had something to say.

I asked brand strategist and storyteller Michelle Garside, whose work spans billion-dollar brands and purpose-driven founders, to share her perspective on what’s at risk as automation flattens language. Her response was both precise and profound:

If language is being flattened, we need more people doing the opposite: excavating. Listening for what’s buried beneath the noise. Uncovering the phrase that unlocks the person. That’s not a prompt—it’s a process. And it’s a deeply human one.

When someone says something that lands—not because it sounds good, but because it’s true. You can see it in their body. You can feel it in the silence that follows. No algorithm can replicate that because that moment isn’t statistical. It’s sacred.

The risk isn’t just that AI will get things wrong. It’s that it will sound just right enough to stop us from looking deeper. To stop us from asking what’s real. To stop us from finding the words only we could say.

We don’t need more words. We need more meaning. And meaning isn’t generated. It’s remembered.

When it comes to language and AI, that’s the message to carry forward—not just because it sounds good, but because it’s true.

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