AI DOESN’T UNDERSTAND ANYTHING<br>(AND 90% OF COMPANIES GET IT COMPLETELY WRONG)

whitedragoon — Thu, 09 Apr 2026 07:35:58 +0000

ChatGPT is impressive. It can write, code, and answer complex questions in seconds; yet it can also produce completely wrong answers with total confidence. And that’s exactly the problem: most companies believe they are using a form of intelligence that understands, analyzes, and reasons like a human.

In reality, that’s not what’s happening.

Today, more and more companies are integrating tools like ChatGPT into their workflows. But behind the excitement, there’s a fundamental misunderstanding: many assume they are interacting with a system that truly “understands” what it says.

In reality, a language model (LLM) like ChatGPT, Claude or Gemini, has no consciousness and no human-like understanding of the world. Its core function is much simpler: it predicts the most likely next piece of text based on context. Like a human brain anticipating the end of a sentence, the AI calculates probabilities, but at a massive scale, trained on vast amounts of text.

To do this, language is broken down into “tokens,” then converted into numerical data. A token can be a word, part of a word, or even punctuation. For example, “marketing” is often one token, while “extraordinary” may be split into several. A simple sentence like “Hello, how are you?” may contain multiple tokens depending on how it is processed. These tokens are then transformed into mathematical representations, allowing the model to work with relationships between concepts.

In this space, words that frequently appear together become close to each other. This means the AI doesn’t learn definitions. It learns patterns of usage.

Let’s take a first semantic cluster: food-related words.

apple = 1 token
fruit = 1 token
juice = 1 token
pie = 1 token
tree = 1 token

These words often appear together, so their representations become close in the model’s internal space.

Now, a completely different cluster: technology.

Apple = 1 token
iPhone = 1–2 tokens
MacBook = 1–2 tokens
software = 1 token
company = 1 token

Again, these words are strongly associated and form another cluster. And here’s the key point: the word “apple” belongs to both worlds.

In a food context: “apple” is close to “fruit,” “juice,” “pie”

In a technology context: “Apple” is close to “iPhone,” “MacBook,” “software”

So for the AI, “apple” doesn’t have a single fixed meaning. Its “position” changes depending on the context. You can imagine this as a map:

one cluster for food, one cluster for technology, and “apple” moving between them depending on the sentence.

For example:
“I eat an apple every day” activates the food cluster
“Apple released a new iPhone” activates the tech cluster
Same word, completely different position in the model’s internal space.

Another interesting aspect is that relationships can be manipulated. For example:
“king” – “man” + “woman” = “queen”
This is not true understanding, it’s a mathematical relationship learned from patterns in data.

The AI learns structures, relationships, and patterns in language. It doesn’t just repeat, it generalizes. This allows it to generate coherent, context-aware responses that can often feel insightful. And that’s exactly what creates the illusion of intelligence. Human language carries reasoning, emotion, and logic, and the AI reproduces these patterns. It can sound like it understands, argues, or even empathizes, while operating in a fundamentally different way from humans.

This illusion has real consequences in business. Many companies use AI as if it “knows.” They ask things like: “Create a marketing strategy for my business” or “Write a complete SEO article.” The result is often clean and structured, but generic, interchangeable, and rarely differentiated. In some cases, decisions are even made based on unverified outputs, simply because they sound convincing.

The issue is not that AI makes mistakes. The issue is that it can be wrong while sounding completely right. It doesn’t aim for truth. It aims for plausibility. It generates what is statistically most likely to “sound correct”. This naturally pushes its outputs toward the average, toward consensus. But the majority is not always right—and rarely exceptional. This is why poorly guided AI usage often leads to standardized thinking and content. At scale, the risk is clear: automating mediocrity.

On the other hand, companies that truly benefit from AI use it differently. They don’t treat it as a replacement for thinking, but as an amplifier. They provide direction, context, and hypotheses. Instead of asking for a full strategy, they say: “Here’s our positioning and target, challenge this and suggest improvements.” Instead of delegating everything, they use AI to structure, refine, and enhance their own ideas.

In this context, AI becomes extremely powerful. It accelerates execution, explores variations, tests ideas, and optimizes outputs. But it remains dependent on human intelligence to guide it. Without direction, it reproduces what already exists. With strong intent, it becomes a performance multiplier.

It is therefore critical not to delegate certain functions:

Critical thinking remains essential, as AI can produce plausible but incorrect information.

Human creativity remains central, as it comes from experience, contradictions, and intuition, while AI primarily recombines existing patterns.

Finally, curiosity and the ability to challenge dominant ideas must be preserved, as AI naturally converges toward what is already established.

In conclusion, the limitations of LLM are not bugs, they are direct consequences of how they work. These tools are extraordinarily powerful, but only if we understand what they truly are. The companies that will succeed with AI are not those that use it the most, but those that use it intelligently, as a complement to human intelligence, not a replacement.

LG. NAGA PUTIH KONSULTING

No more hashtags in 2026?

whitedragoon — Wed, 01 Apr 2026 01:00:56 +0000

Instagram hashtags are still useful, but they are no longer the main driver of reach. Based on Instagram’s own guidance, public posts with hashtags can still appear in hashtag search results, while users can also discover content through keywords, places, audio, and accounts. This shows that hashtags still support discoverability, but they now work best as part of a broader content strategy rather than as a standalone growth tactic.

At Naga Putih Konsulting, we see this as a clear shift in how Instagram understands and distributes content. The platform now reads more than just hashtags. It also looks at the context of the post, the words used in the caption, and the way people interact with the content. This means visibility today depends not only on tagging, but also on how clearly and relevantly the content is presented.

That is why Naga Putih Konsulting believes the best hashtag strategy today is a simple and focused one. We recommend using a small number of relevant hashtags that clearly match the content, the intended audience, and the niche. Random, repetitive, or misleading hashtags are no longer effective, and in some cases may even weaken discoverability. Relevance is now far more important than volume.

Another important shift is that performance now relies much more on content quality and audience response. Strong visuals, clear messaging, watch time, shares, saves, and comments send stronger signals to Instagram than hashtags alone. This is especially true for Reels, where retention and engagement often carry more weight than tagging. In practical terms, a strong piece of content can perform well with only a few hashtags, while a weak post will not gain traction simply because it includes many of them.

From our perspective at Naga Putih Konsulting, hashtags are now more useful as a positioning tool than a growth shortcut. They can still help Instagram understand what a post is about and who it may be relevant to, especially when using niche or category-specific hashtags. For brands, this makes hashtags helpful for reinforcing context, whether that context is based on industry, location, audience interest, or content theme.

Industry articles also point in the same direction. They suggest that hashtags are no longer the main growth lever, and that better performance now comes from good content, niche keywords, strong visuals, Reels, and engagement signals such as saves, shares, and watch time. While these articles are not as authoritative as Instagram’s own documentation, they reflect what many marketers and businesses are already seeing in day to day results.

So, Naga Putih Konsulting recommends a more balanced and modern approach. Brands should not rely on hashtags as the centre of their Instagram strategy. Instead, they should build content that is visually strong, easy to understand, and closely aligned with audience interests. Captions should include clear and searchable language, and hashtags should be used only to support that broader discoverability strategy.

In short, hashtags still matter, but only as a supporting tool. Naga Putih Konsulting believes the stronger approach today is to combine a few relevant hashtags with searchable keywords, clear captions, and content that is genuinely engaging for the target audience.

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