AI Isn’t Intelligent — It’s a Very Expensive Illusion

For the past few years, “AI” has been marketed as the dawn of a new intelligence. We’re told it thinks, understands, reasons, creates, and will soon replace vast swaths of human labor. CEOs speak in breathless terms about digital minds and world-changing breakthroughs. Investors pour trillions into companies positioned as the infrastructure of this new era.

But strip away the hype, and what remains looks far less revolutionary — and far more familiar.

Large Language Models (LLMs), the stars of the current AI boom, are not thinking machines. They do not understand language, ideas, or the world. They are statistical systems trained to predict the most likely next token in a sequence based on massive amounts of data. That’s it. No beliefs, no goals, no comprehension — just probability distributions over text.

Calling this “intelligence” is not a scientific claim. It’s a marketing strategy.

The Language Trick: How “Prediction Machines” Became “Minds”

The most effective sleight of hand in the AI boom has been linguistic.

LLMs are described as:

• thinking
• reasoning
• hallucinating
• understanding
• deciding

These words are borrowed directly from human cognition, even though the systems themselves have none of the underlying properties those words imply. When a model outputs a fluent explanation, people intuitively assume there must be an internal understanding behind it — because that’s how humans work.

But LLMs don’t know what they’re saying. They don’t check facts. They don’t hold concepts. They don’t reason in the way humans or even classical AI systems do. They generate text that looks like reasoning because the training data contains examples of reasoning-shaped text.

This isn’t a minor semantic issue. It’s the foundation of the hype. By encouraging anthropomorphic interpretations, AI vendors let users and investors overestimate what the technology actually is.

Energy, Scale, and the Hidden Costs

What LLMs do have is scale — and scale costs money. A lot of it.

Training and running modern AI models requires:

• Vast data centers
• Enormous GPU clusters
• Huge amounts of electricity and water
• Constant hardware refresh cycles

This is not a “software revolution” in the traditional sense. It’s an industrial operation, closer to mining or heavy manufacturing than to writing code. The environmental footprint is growing rapidly, and the marginal gains are increasingly expensive.

Yet the public narrative insists this is an inevitable, exponential march toward artificial general intelligence — conveniently justifying ever-larger capital expenditures.

The Bubble Economics

Every tech bubble needs three ingredients:

• A compelling story
• Uncertain but enormous future returns
• Infrastructure companies positioned as “picks and shovels”

The AI boom has all three.

NVIDIA: The Toll Booth of the Gold Rush

NVIDIA has become the most obvious beneficiary of AI hype. Its GPUs are essential for training and inference, and demand has exploded. But this creates a dangerous feedback loop.

• AI companies promise world-changing breakthroughs
• Investors fund them aggressively
• That money flows directly into NVIDIA hardware
• Rising GPU demand is cited as proof that AI is “working”
• Which attracts more investment

Whether the end products are economically viable becomes almost secondary. The infrastructure sells regardless — a classic gold-rush dynamic.

Cloud Giants: Renting the Future by the Hour

Companies like Oracle, Amazon, Microsoft, and Google position themselves as indispensable AI platforms. Their pitch is simple: the future runs in the cloud, and AI needs the cloud.

This allows them to monetize AI enthusiasm immediately, even if downstream applications never deliver the promised productivity revolution. Training experiments, failed startups, and overhyped pilots still generate compute bills.

The risk is socialized among startups and investors, while the revenue is privatized by infrastructure providers.

OpenAI and the Narrative Economy

OpenAI occupies a unique role: part research lab, part product company, part storytelling engine. Its leaders speak confidently about imminent breakthroughs, existential risks, and transformative power — often in the same breath.

The message is remarkably consistent:

• AI is powerful enough to reshape civilization
• AI is dangerous enough to require enormous investment and centralized control
• AI is inevitable, so resistance is irrational

None of this requires proving that current models actually understand anything. The promise alone sustains the valuation.

CEO Promises as Persuasion

AI executives rarely make falsifiable claims. Instead, they rely on vague timelines, selective demos, hypothetical scenarios, appeals to inevitability, and fear of being left behind.

This isn’t accidental. It’s persuasion under uncertainty. When outcomes are unclear, confidence becomes a substitute for evidence.

Saying “this is advanced statistical pattern matching” doesn’t raise capital. Saying “this will change everything” does.

Productivity Gains: Where Are They?

Despite the hype, measurable economy-wide productivity gains remain modest. Many deployments struggle with reliability, verification costs, legal risk, security concerns, and maintenance overhead.

Humans often spend significant time correcting, validating, or constraining AI outputs. That time is rarely counted when return-on-investment claims are made.

A Familiar Ending

The story being sold is one of machine intelligence, inevitable dominance, and limitless growth. That story justifies extraordinary valuations, massive energy consumption, and frantic investment — even as the underlying technology shows diminishing returns.

If history is any guide, what follows is not the end of AI, but the end of the illusion:

• Expectations reset
• Capital consolidates
• Many companies vanish
• The technology finds narrower, more realistic uses

The scam isn’t that LLMs exist. It’s that we were encouraged to believe they were something they’re not — and to pay any price to be part of the future they promised.

Yes, this text was created by an LLM.