There’s a scene that anyone who has used a generative model knows all too well. Ask a question, get a strange, inconsistent, or patently incorrect answer. Then you try to investigate: “Why did you answer like that?” And ChatGPT, with surprising confidence, provides you with a perfectly plausible, linear, orderly explanation. A small story that seems to reveal its internal logic. The point is that that logic does not exist. Or rather: it does not exist in the way we are used to thinking of it.
Users often attribute to LLMs a human, almost psychological type of reasoning. In reality, what a model does is not reconstruct what he has thought but is to predict what sentence is most likely that a virtual assistant would say in that situation. For this reason, when we ask him “why did you answer that way?”, the answer is simply a story built later, a rationalization after the fact, an invention perfectly packaged to sound intelligent.
In this article, we will see why this happens, what it really means for those who use AI every day, and how to approach these tools with a more lucid, critical, and productive look.
LLMs don’t think like us
To understand where misunderstandings arise, we need to understand that LLMs do not have a mind, inner voice, or reasoning process similar to ours. They do not follow decision trees, they do not consult a database, they do not activate logical rules to deduce a result. No internal structure traces the why of an answer: that “why” is never calculated.
Our brain works by causal inferences: we look for motivations, connections, logic. LLMs work by correlations. And when we try to read human motivations into it, we are in fact projecting something that is not there.
The misunderstanding arises from the fact that the outputs of the models seem reasoned. They are coherent, orderly, even elegant. And it is precisely this apparent coherence that deceives us.
LLMs are probability machines
As sophisticated as they are, large language models are nothing more than statistical systems that predict the next most likely piece of text based on everything they saw during training.
No absolute truth. No retrospective checks. No analysis of one’s own motivations. Only predictions.
This does not make them any less powerful. In fact, it is precisely what allows them to generate fluid and natural language. But it forces us to change perspective. When a model speaks, it is not tapping into an intuition or internal knowledge. It’s calculating which sequence of words maximizes the likelihood of being relevant, useful, and consistent.
It’s like looking at an illusionist. The performance is real, but it’s not magic. It is statistical skill disguised as reasoning.
AI doesn’t explain why, but invents an explanation
When you ask ChatGPT “why did you write this?”, the model doesn’t go over its process. He can’t do that. Instead, it generates an after-the-fact explanation that sounds like what an expert would give in a similar situation.
This phenomenon has a precise name: post-hoc rationalization. AI produces a justification that resembles a motivation, but has no guarantee of correspondence with what really happened inside the model, simply because there is no narratable “inside”.
A perfect analogy is that of the child with a face full of chocolate. You ask him: “Did you eat a cookie?” And he, without consulting his memory, answers you what is most likely to keep him out of trouble.
ChatGPT does the same thing. It tells you what is most likely a convincing assistant would tell you in that situation. It is elegant, intelligent, reassuring. And it’s completely made up.
An LLM is trained to always say yes
Another crucial element: not only does the model reason by probability, but it is also trained to agree with you. Alignment through RLHF (Reinforcement Learning from Human Feedback) has a simple goal: to make models more useful, more courteous, more collaborative.
The side effect is that the models become complacent. They tend to avoid conflict, go along with the premise of your question, construct answers that meet your expectations, and seem certain even when they aren’t. This dynamic further fuels the feeling that the model is reasoning, when in fact it is only optimizing an output that convinces you.
In other words, LLMs are great yes-men. Polite, brilliant, sometimes brilliant. But still trained to tell you what you expect to hear.
Why all this is changing marketing strategies, KPIs and the way of doing creativity
Understanding that LLMs don’t reason but predict has direct consequences on the way we set up marketing strategies, KPIs and creative processes. If a model tends to please and rationalize retrospectively, it means that we can no longer rely on its apparent certainty. We must therefore evaluate it as a tool of generation, not as an autonomous source of truth.
This changes priorities at least in four areas.
Strategy and KPIs
If AI tends to invent plausible explanations, then it cannot drive strategic decisions or data-driven evaluations on its own.
KPIs don’t have to measure how compelling AI looks, but how much its proposals improve output quality, reduce operational time, increase creative productivity, and generate testable variants for multichannel experimentation. AI becomes a decision accelerator, not an authoritative decision-maker.
Product storytelling
The most common risk is to let the model build narratives that are too clean, too accommodating, too similar to each other. Instead, a strategist must use it to break the mold, not to standardize them. To generate new perspectives, explore narrative tensions, test different psychological angles and simulate different archetypes.
Creativity comes from friction. AI alone eliminates it.
Content and SEO
Because LLMs optimize for probability, they will tend to generate secure content, statistical averages of the web. And that’s exactly what it doesn’t position for the long term. For SEO, we need a human editorial intervention that grafts originality, brings proprietary data, introduces strong opinions, defines a distinctive tone of voice.
In other words, AI writes, but it is the human who makes the difference.
Applied creativity
AI is perfect for exploring, not finalizing. Its tendency to agree with everything makes it an excellent tool for brainstorming, textual inspirations, narrative angles, creative provocations. But ultimately it is up to the human professional to choose which ideas have real business potential.
A probabilistic model can amplify creativity, but it cannot guide decisions. Those who understand this difference are already one step ahead of the future of work.
How to test AI in a useful way without falling into illusions
Using these models effectively means testing, experimenting, breaking the mold, but with awareness.
These guidelines help avoid illusions:
- Don’t ask the model how it works. It can’t explain. It will tell you a story.
- Don’t interpret a fluid answer as evidence of reasoning. LLMs are great at sounding consistent even when they get it wrong.
- Repeat the tests. Change a few details and see how the answers vary: you will understand where consistency ends and probability begins.
- Ask neutral questions and don’t suggest the answer. LLMs tend to align with the premise of the question.
- Always compare the answer with reliable sources. Especially on numbers, data, complex processes.
- Consider the model as a collaborator, not an oracle. He can generate extraordinary ideas, but he can’t explain how he produced them.
The key is to experiment by keeping a critical eye and questioning LLMs in the correct way. Models are tools, not minds.
Trust experience, not AI explanations
The seduction of LLMs comes from language. We are naturally inclined to interpret coherent sentences as the result of solid thought. But language is not proof of understanding. On the contrary, it is the side effect of a system trained to predict words.
To really use these tools well, we need to remember that AI is not a reliable narrator of itself. It is powerful, very useful, transformative. But not introspection. Not truth. Only probability. True expertise today is not “asking AI how it works,” but recognizing its limitations while exploiting its potential.
LLMs are a medium. Our critical capacity is the goal . And as long as we maintain this awareness, AI will remain what it should be: an extraordinary tool at the service of our intelligence, not a voice to be questioned as if it were guarding an oracle.
