How Language Corruption Became a Business Model

The engineering community has a serious problem. It is not a lack of intelligence, or curiosity, or even caffeine. It is language.

Engineers have developed a habit of taking perfectly good words and bending them into shapes that would make a linguist quietly back out of the room. If something needs to sound impressive, it is called autonomous. If it needs gravitas, it becomes agentic, a word that once had a meaning and now has a LinkedIn profile. If it is workflow automation in a sensible hat, it is reintroduced as artificial general intelligence and sent out to raise a Series B.

This happens for a simple reason. Engineers are not trained to be creative in the narrative sense. They build inside constraints. They optimize. They solve. Naming things and telling stories are treated like optional accessories, much like cup holders or empathy.

Marketing, on the other hand, knows exactly what it is doing. It serves the bottom line with the calm dedication of a golden retriever retrieving profit.

The result is predictable.

Words stretched until they squeak. Capabilities that quietly disagree with their own descriptions. A systematic practice of adjusting language to match what we want to sell, rather than building something and calling it what it actually is.

The alternative approach, which involves building first and describing the result honestly, is widely regarded as inefficient and emotionally unsatisfying. It lacks sparkle. It does not trend.

And so the propaganda machine starts up, hums gently, and begins converting reality into PowerPoint.

At this point it is worth pausing to note that no one involved believes they are lying. This is important. Everyone believes they are merely “positioning.” Much like how a toaster might describe itself as a bread-based thermal enhancement platform, while remaining technically correct if you do not ask follow up questions.

The Misinformation Cycle

The cycle works with the soothing reliability of a washing machine that only has one setting.

Stage 1: The Press Release A document appears describing features that do not exist yet, or exist only during carefully supervised demos with friendly lighting. Words like revolutionary, autonomous, and intelligent are applied generously, like garnish. The claims are vague enough to avoid legal consequences and specific enough to trigger excitement in people who have not slept since 2019.

Stage 2: The Echo Chamber Journalists, analysts, and influencers repeat the claims. Most do not have access to test the technology. Fewer have the technical depth to evaluate it. There is an unspoken assumption that a company would not claim capabilities it does not have. This assumption survives despite overwhelming historical evidence to the contrary.

Stage 3: The Believers Repetition creates credibility. Once enough people repeat the same thing, it becomes true in the same way that saying “this meeting will be quick” becomes true through collective resignation.

Stage 4: The Amplification Others repost with their own interpretations. Each version drifts further from whatever reality existed at the start, like a message passed down a very excited corridor. Accuracy decreases. Confidence increases.

Stage 5: The FOMO Fear of missing out solidifies belief. If everyone else is convinced this technology will transform their industry, skepticism begins to feel reckless. Belief becomes the safer career move. Questioning starts to look like a personality flaw.

Stage 6: The Viral Moment The claims escape tech circles. Mainstream coverage treats them as established fact. At this stage, doubting the claims feels contrarian, or worse, unfun.

Stage 7: The Deflection When someone points out that the technology does not actually do what is claimed, defenders reference the original press release. “They said it does,” they explain patiently, as if citing gravity.

Why would a serious company, backed by serious investors, confidently repeat something that is not true.

An excellent question. One that will not be answered.

The Question You Should Be Asking

Someone once challenged me by asking whether I really believed big tech, venture capitalists, investors, and strategy consultants were all wrong.

Yes. Entirely. Enthusiastically.

But that is not the right question.

The real question is not whether they are wrong. It is, why you believe they would say otherwise.

The AI narratives defining 2025 and accelerating into 2026 are powered by money. Valuations need momentum. Venture capital needs exits. Consultants need relevance. Analysts need access. Influencers need engagement.

None of these incentives rewards precision. All of them reward excitement.

Every AI claim exists inside an economic context, even when it pretends to be a neutral observation about the future. Especially then.

A useful habit is to begin every AI claim with a quiet internal question. Why might this information not be accurate. If the company is selling it, the message has been enhanced. If an analyst covers it, consider the relationship. If an influencer posts about it daily, ask who benefits from the enthusiasm.

This is not cynicism. It is basic pattern recognition, which is ironic, considering how often AI is credited with it.

The Real Reason Your AI Projects Fail

There are countless posts explaining why most AI projects fail to deliver return on investment. Each offers a different explanation. Bad data. Bad teams. Poor execution. Weak leadership. Insufficient change management.

None of them mention the actual problem.

The capabilities being sold and the capabilities that exist are misaligned.

Organizations are buying one thing and expecting another. The technology does exactly what it can do, which is not what was promised. This is then framed as a failure of implementation rather than a failure of description.

In my book Infailible, I researched the gap between what people believe AI can do and what it can actually do. That gap is roughly seven years. We are about seven years away from AI reliably doing what people confidently claim it can do today on social media, usually next to a photo of a coffee.

I often explain it this way.

You are sold a screwdriver and told it will make an excellent sandwich.

When no sandwich appears, the vendor explains that your kitchen is not optimized. Your ingredients lack alignment. Your sandwich mindset needs work. They offer a workshop. They audit your bread readiness. They never mention that a screwdriver, however confident, has no opinion on mayonnaise.

At no point is the screwdriver blamed.

Why It Will Not Stop

The engineering community does need an awakening. Unfortunately, awakenings are unpopular when sleep is profitable.

As long as confusion generates revenue, the cycle will continue. A new term will emerge. It will be distorted, polished, and presented as inevitable. Organizations will buy it. Disappointment will follow. Consultants will arrive.

We have seen this before. Cloud. Big data. Digital transformation. Machine learning. Now agentic AI and autonomous systems. Next year it will be something else, possibly artificial general intelligence finally escaping the research lab and entering a keynote, despite remaining nowhere near reality.

Each cycle follows the same arc. Each cycle rewards the same people. Each cycle leaves organizations with expensive systems that never quite matched the story they were sold.

It is like buying a map where the dragons are real and the roads are theoretical.

What You Can Do

I do not have a solution for separating fact from fiction on the internet. That problem predates AI by decades and has defeated people with far better lighting and funding than me.

But awareness matters.

The next time you encounter an AI claim in a press release, a keynote, a sales pitch, or an enthusiastic LinkedIn post, pause. Ask where this truth originates.

Trace it backward. If the trail ends at marketing, that is not validation. That is ignition.

Look for alternative perspectives. Seek out people who build these systems rather than sell them. Prefer case studies with numbers over testimonials with feelings. Read criticism alongside celebration.

The AI industry will not regulate its own language. The incentives are misaligned, the profits are large, and the consequences are conveniently distributed across everyone else.

But you can choose what you believe. You can choose what you repeat. You can choose whether to amplify claims or interrogate them.

That choice, multiplied enough times, is the only mechanism that has ever forced accuracy into an industry that currently finds it inconvenient.

And if nothing else, it may prevent the next screwdriver from being marketed as a lunch preparation device.

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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.