Blind Men and AI: Why Engineers, Researchers and Users See a Different Elephant

The ancient parable of the blind men feeling an elephant turns out to be a surprisingly accurate model of how different specialists look at AI: each studies one part and is sure they have understood the whole.

A parable worth recalling

Seven blind men meet an elephant. One grabs a leg and says it is a pillar. Another touches the tail: no, a rope. A third touches the side: a wall. A fourth holds the trunk: a snake. Nobody is lying. Each describes a real part of the object. But each is deeply wrong about the whole.

The author applies this scheme to the AI industry. The ML engineer studies architecture: transformers, tokens, weights, gradient descent. The data scientist sees datasets and loss functions. The product manager looks at retention and conversion. The safety specialist looks at hallucinations and vulnerabilities. The regulator looks at risks. And the user simply pulls the elephant by the trunk and is surprised when it behaves unpredictably.

The question that remains unanswered

The article's sharpest point is not about technology but about a fundamental contradiction in expectations. If a language model is trained on texts written by people, with all their passions, irony, prejudices and inner contradictions, why do we expect it to behave like a cold, neutral mechanism?

"We teach the model to be human, and then we are surprised that it

does not behave like a machine."

Behind this lies a real design problem. The industry wants AI to show empathy in the interface and complete indifference to content. It wants the model to grasp subtle nuances and yet have no "points of view." This contradiction is built into the training method itself, and RLHF or system prompts cannot resolve it.

Why benchmarks don't provide the answer

While most public debate revolves around MMLU, HumanEval and Arena Score, the article poses a fundamentally different question: what exactly are we measuring with them?

• Does the model "think," or does it reproduce patterns of human thinking?
• Is there a practical difference between "understanding" and "predicting the next token"?
• If there is no difference, does that change how we should work with it?
• How do we agree on criteria for AI "behavior" if every profession studies a different elephant?
• Who is responsible for the "whole elephant" at all?

This is not academic philosophy. The answers determine product design, regulation, risk assessment, and how we will build our relationship with the technology over the long term.

What this means

The parable is useful not as a metaphor for incompetence: the sages are not stupid. It shows a structural problem: different professions literally study different aspects of one phenomenon and speak different languages. Until the industry develops a shared language for discussing AI as a whole phenomenon, not just its architecture or individual metrics, every participant in the debate will keep holding on to their own piece of the elephant.