What Is Artificial Intelligence? What Isn’t It?

What is artificial intelligence?

Artificial intelligence is an algorithm. It is an algorithm with higher functional power than the ones we have traditionally known — because modern chips have raised data read/write speed to an entirely new level.

Capabilities such as learning, reading, writing, analytic evaluation, logical comparison, and prediction have brought AI to a point where it can reshape this era.

What is AI not?

AI does not possess creative power. In fact, creativity may be where AI is weakest and least mature.

An environment where everything is produced by AI creates a narrow, originality-poor system. In this revolution, the human traits that stand out most — creativity, intent, and context — become an even more decisive muscle.

80% visibility, 20% security?

Even advanced consumer chatbots and tools often allocate more effort to visible features than to security, because competitive markets reward what the public can see. Put simply: one hour may go to security protocols while four hours go to visibility. That imbalance creates real downsides:

1. 1Security weakness: like building beautiful towers while underbuilding the sewage system — the surface looks premium while the foundation is fragile.
2. 2Competitive erosion: multi-year R&D used to create durable advantage. Short AI build cycles shrink that window as products become easier to copy.
3. 3Shared-infrastructure risk: with hundreds of thousands of server networks and few dominant AI platforms, a backdoor in the AI layer can threaten many products at once. Security investment often lags production investment.
4. 4Cost and sustainability: AI infrastructure is expensive. Payback can be long, and large capital may not wait while use cases are still being discovered.

Where can we use AI?

• Data analysis
• Logical comparisons
• Daily routine tasks
• Reducing staff workload
• Non-creative domains

How should you use AI? What is “AI training”?

Think of AI as a high-IQ model that reads and writes extremely fast. You get results based on the training you provide — you cannot hold it accountable for domains you never taught it.

A salesperson who has seen 4,000 customers will not match one who has seen 80. If you do not support your AI with parameters and data, it will think and act like the 3-month hire.

The originality trap: the “happy customer” algorithm

One of AI’s weakest muscles is generative originality. Standard images and standard websites often hide inside a “happy user” optimization. Ask a chatbot for an image and it will bias toward patterns that previously produced satisfaction — not uniqueness. The result: similar public assets and inverted competitive advantage.

Is my data at risk with AI?

“Reverse data security” may be one of the least useful panics. AI companies often keep data anonymous and optimize for statistical utility. Storing raw data from billions of people is both low-value and expensive compared with investing elsewhere.

What does statistical data retention mean?

Not “Ahmet Yılmaz likes art,” but “100,000 people in Turkey chat with AI about art.” That aggregate signal is what matters. Meanwhile, what you say, write, and search is already tracked by many apps under cookies and consent — and sold into advertising. Companies buy the chance to follow your need in real time, not merely your contact record.

Who opens a multi-billion-dollar investment for free?

This may be the most critical question. We know the answer: no one leaves it free forever. Early low-friction access builds habit, then adoption, then dependency. The real question now is how we will pay.

1. 1We shifted research from Google/Yandex toward AI — alongside SEO comes GEO (Generative Engine Optimization), reshaping which brands get cited.
2. 2AI that knows what you research will commercialize with sponsored slots, much like search ads.
3. 3Millions of web pages were consumed quickly; as human originality cannot match AI reading speed, repetitive block content will rise.
4. 4Jobs tied to old tech are transforming; new roles emerge — a new industrial wave.
5. 5Websites become content feeders for AI, not only human storefronts; revenue may correlate with AI visitation and citation stats.
6. 6Classic unicorn/startup copycats get harder to fund when equivalents can be rebuilt in months.
7. 7The personalized software era: custom CRM for firm A, custom ERP for firm B, personal assistants for individuals — not only rented one-size-fits-all tools.
8. 8Human contact in purchase, support, and service may shrink — social ties may weaken.
9. 9Psychological strain from reduced social contact may grow; solutions that restore human connection gain value.
10. 10Counter-movements will form, but cost-saving firms will keep pushing the shift.
11. 11Regulation may slow some areas, yet tax and oversight systems strengthened by AI keep states invested in the technology.
12. 12As human employment falls, extra taxes on robotic/AI labor may appear.
13. 13Good news: falling costs can intensify price competition — often to the buyer’s benefit.