Blind Sages and AI: Why Engineers, Scientists, and Users See Different Elephants

Blind Sages and AI: Why Engineers, Scientists, and Users See a Different Elephant

The ancient parable of blind sages touching an elephant turns out to be an unexpectedly accurate model of how different specialists view AI: each studies their own part and is convinced they understand the whole.

The Parable Worth Remembering

Seven blind sages encounter an elephant. One grasps its leg — says it's a column. Another touches the tail — no, it's a rope. A third touches the side — a wall. A fourth holds the trunk — a snake. No one is lying. Each describes a real part of the object. But each is deeply mistaken about the whole.

The article's author applies this scheme to the AI industry. An ML engineer studies architecture — transformers, tokens, weights, gradient descent. A data scientist sees datasets and loss functions. A product manager looks at retention and conversion. A security specialist — at hallucinations and vulnerabilities. A regulator — at risks. And a user simply pulls the elephant by its trunk and wonders why it behaves unpredictably.

The Question That Remains Unanswered

The sharpest thesis of the article is not about technology, but about a fundamental contradiction in expectations. If a language model is trained on texts written by humans — with all their passions, irony, prejudices, and internal contradictions — why is it expected to behave like a cold neutral mechanism?

"We teach the model to be human, and then we're surprised it doesn't

act like a machine."

Behind this lies a real design problem. The industry simultaneously wants AI to show empathy in its interface — and complete indifference to content. It wants the model to understand subtle nuances — and at the same time have no "points of view." This contradiction is built into the very way training works, and it can't be solved through RLHF or system prompts.

Why Benchmarks Don't Provide an Answer

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

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

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

What This Means

The parable is useful not as a metaphor for incompetence — the sages are not foolish. It shows a structural problem: different professions literally study different aspects of the same phenomenon and speak in different languages. Until the industry develops a common language for talking about AI as a whole phenomenon — not just in terms of architecture or individual metrics — each participant in the discussion will remain holding their own piece of elephant.