Knowing About AI Is Not the Same as Using It

Why the Five-Minute Expert Keeps Getting It Wrong

Watch this article as a Storyboard Video

There is a subtle but important difference between knowing about something and having actually used it. In few areas is this gap more visible, or more consequential, than artificial intelligence.

AI is now discussed confidently by politicians, journalists, consultants, and commentators across the world. It appears in speeches, policy documents, headlines, and strategy decks. And yet, in many cases, the people shaping public understanding of AI have spent remarkably little time working with it in any sustained or meaningful way.

This is not an accusation. It is an observation, and an increasingly important one.

---

Familiarity Is Not Experience

When Donald Trump was recently asked whether he had used ChatGPT, his reply, “I haven’t really, but I know all about it”, stood out not because it was unusual, but because it was honest.

That sentence captures a pattern that now defines much of the AI conversation.

Many people genuinely believe they understand AI because they:

• have read about it
• have watched demonstrations
• have heard experts discuss it
• have briefly experimented with it

But AI is not understood in moments. It is understood over time.

A five-minute interaction with an AI system can be impressive.
A five-week interaction is often confusing.
A five-month interaction is humbling.

Only extended use reveals where AI is useful, where it is unreliable, where it quietly fails, and where it gives the illusion of competence while drifting away from accuracy.

---

The Car Manual Problem

This gap between theory and practice is not new.

Many people have read manuals on how to fix a car. Far fewer can actually do it. Until you’ve struggled with seized bolts, missing tools, unexpected faults, and the quiet realisation that the diagram doesn’t match reality, you don’t truly understand the task.

AI is no different.

Reading about AI explains:

• how it should work
• what it is designed to do
• what it is capable of in ideal conditions

Using AI reveals:

• how context degrades output
• how errors compound silently
• how confidence and correctness diverge
• how human judgment remains indispensable

This difference matters, because public narratives are being shaped by the first group, while consequences are lived by the second.

---

The Rise of the Five-Minute Expert

The AI era has produced a new professional archetype: the Five-Minute Expert.

This is not a malicious figure. Quite the opposite. The Five-Minute Expert is often articulate, well-read, and genuinely engaged. They have watched the demos, read the summaries, absorbed the language, and internalised the optimism, or the fear.

What they lack is friction.

They have not:

• relied on AI outputs in real workflows
• dealt with hallucinations at scale
• watched performance drift over time
• been forced to decide when not to trust the system

As a result, their certainty exceeds their exposure.

This is why AI coverage so often oscillates between hype and alarm, while missing the mundane truth: AI is powerful, fallible, context-sensitive, and deeply shaped by how humans deploy it.

---

Why AI Amplifies This Problem

AI is particularly vulnerable to shallow expertise for three reasons.

First, it communicates fluently. Language creates the illusion of understanding, even when none exists.

Second, it demonstrates well. A single impressive response can overshadow dozens of subtle failures.

Third, it borrows authority. People assume that because others speak confidently about AI, the understanding must be deeper than it is.

Together, these traits create an environment where second-hand knowledge feels sufficient, and lived knowledge is undervalued.

---

Policy Without Practice

This distinction becomes far more serious when it enters governance.

In the UK, as in many countries, policy is often shaped by individuals with strong theoretical grounding but limited operational exposure. The issue is not intelligence or intent, it is distance from consequence.

Within the UK Government, many decision-makers are highly educated, articulate, and analytically capable. Yet few have:

• run businesses under cash-flow pressure
• implemented technology inside messy organisations
• navigated trade-offs where every option has a cost

As a result, policies can be internally coherent yet externally brittle, logical on paper, unstable in reality.

This mirrors AI theory perfectly. Systems designed according to models behave differently when exposed to real-world complexity.

---

The University Realisation

For those who return to education later in life, this gap becomes impossible to ignore.

Theory is clean.
Business is not.

Models assume rational actors, stable incentives, and predictable behaviour. Reality introduces fear, ego, fatigue, incomplete information, and time pressure. The same is true of AI systems.

This is not a failure of education. Theory is essential. But theory without exposure breeds false confidence, and AI punishes false confidence quietly, not dramatically.

---

What Real AI Literacy Looks Like

True AI literacy is not about prompts, tools, or vocabulary. It is about judgment.

A practitioner knows:

• when AI accelerates work
• when it introduces hidden risk
• when outputs must be verified
• when human intuition outperforms automation

This knowledge does not come from reading. It comes from repetition, correction, and failure.

Ironically, the more time someone spends using AI seriously, the less absolute their claims tend to be.

---

A Mirror, Not an Accusation

This article is not an attack on politicians, journalists, or professionals. It is a mirror.

We live in a time where knowledge travels faster than experience, and where confidence is often rewarded more than caution. AI exposes this imbalance because it looks simple, sounds intelligent, and behaves unpredictably.

The danger is not that people talk about AI without using it.
The danger is mistaking that familiarity for understanding.

---

Conclusion: Experience Still Matters

Artificial intelligence will continue to reshape how we work, govern, and communicate. But no technology abolishes the need for lived experience. If anything, AI increases it.

Knowing about AI is not the same as using it.
And using it, truly using it, teaches humility faster than any manual ever could.

Latest AI Articles