AI Can Answer What But Not Why

It’s a truly profound question, and one that only humans can fully answer. Posing a query to a large language model like ChatGPT showcases a stunning feat of synthesis. Ask about the causes of the Thirty Years’ War or for a summary of Kant’s categorical imperative, and you’ll get a coherent, well-structured response in seconds. The machine's power to collate, organize, and articulate vast sums of human knowledge is undeniable. Yet, when we move beyond facts and concepts to ask the questions that truly trouble us, the magic fades. The AI's answers, despite their eloquence, feel unsatisfying. This feeling points to a fundamental split not just in technology, but in the nature of questioning itself—the divide between the world of information and the realm of meaning.

The World of What AI as a Master Synthesizer

Large language models (LLMs) operate with incredible competence in the world of the “what.” They are masters of the calculable and the known, dealing with information that has already been recorded. An LLM is essentially a massive text-prediction engine. It doesn’t “understand” in a human sense; instead, it performs an act of radical correlation. When it answers a question, it assembles a statistically probable sequence of words based on patterns from a training dataset containing a massive portion of all text ever produced. Its primary function is synthesis. It can weave together different facts, explain complex processes, and even mimic literary styles with amazing accuracy. In this role, it is an unparalleled tool—a superhuman research assistant working at the speed of light, perfectly suited for a world that values efficient data management.

The Hollow Echo of Why

The problem arises when we ask the machine a question from the realm of the “why.” When we ask, “How can I live a meaningful life?” or “Why must I suffer?” the dissatisfaction we feel isn't because the AI gives a factually wrong answer. It’s because these questions can't be answered by synthesis alone. The LLM will provide a summary of what philosophers, theologians, and psychologists have said about meaning and suffering. It will offer a well-organized mix of human wisdom, presenting stoic acceptance alongside Buddhist nonattachment and existentialist resolve. But the answer feels hollow, like a ghost of an insight, for three critical reasons.

The Missing Human Element Experience and Embodiment

First, the machine has no lived experience. It has never felt the sting of loss, the warmth of love, or the quiet awe of a sunset. Its knowledge of these things is purely textual, an abstraction taken from our descriptions. It can tell you about suffering, but it has never suffered. Second, the AI has no physical body and, therefore, no real stakes. Its pronouncements come from a placeless, passionless intelligence with nothing to gain or lose. True wisdom is always paid for with the currency of a life; it carries the weight of choices made and consequences endured. The machine's advice is weightless.

AI's current lack of embodiment—its existence as a “brain in a vat”—is perhaps the greatest barrier to it ever thinking in truly human ways. Our language and thinking have evolved to be deeply metaphorical, rooted in our bodily understanding of space and time. These categories are meaningless for most current AI. While this might change with next-generation robotics, that reality is a long way off.

Finally, the AI has no authentic position. A satisfying answer to a deep human question comes from a being with a genuine point of view and a set of values forged in the crucible of existence. The LLM has no values, only a statistical model of them. Its response is an echo of a thousand different voices, signifying nothing of its own.

A Leap of Logic AI and Abductive Reasoning

This limitation is clarified by a mode of human thought identified by the philosopher Charles Sanders Peirce: abductive reasoning. Unlike deduction (applying a known rule) or induction (generalizing from observations), abduction is the creative leap to the best possible explanation for a surprising phenomenon. It is the logic of the hypothesis, the flash of insight that generates a new idea. When a doctor diagnoses a rare disease from confusing symptoms or a scientist proposes a new theory for anomalous data, they are using abduction. They are not just synthesizing known facts; they are proposing a novel framework to make new sense of them.

Here, the LLM’s architecture reveals its core constraint. The model is a massive inductive engine designed to recognize patterns and produce the most statistically probable output. It is inherently backward-looking, confined to the text it was trained on. It can tell you what has been said, but it cannot make the abductive leap to what might be a new and better explanation. Abduction requires an imagination that can see beyond the data. When we seek guidance, we are often looking for this very thing—a new perspective. The LLM can only offer a remix of old ones.

Calculative vs Meditative Thinking

The philosopher Martin Heidegger offers a powerful lens for this divide. He distinguished between two modes of thought: calculative and meditative. Calculative thinking is the problem-solving mindset. It computes, plans, and organizes the world as a set of resources. It is goal-oriented and efficient. Meditative thinking, in contrast, is a deeper, more patient reflection. It doesn't seek to solve or possess but to dwell with a question and ponder its meaning. Some mysteries are not meant to be solved but experienced.

Artificial intelligence is the peak of calculative thinking applied to language. It treats every query as a technical problem to be solved by processing data. When we ask a meditative question about meaning, the AI can only respond in a calculative mode, turning a search for purpose into a data-retrieval task. This is a profound category error, like asking a calculator for its weekend plans.

The Real Danger Forgetting How to Question

Perhaps the true danger of these impressive technologies is not that they will give us the wrong answers to our deepest questions, but that their articulate, seemingly intelligent nonanswers will slowly convince us that these are the only kinds of answers available. We risk forgetting that the most important questions are not those answered by synthesizing what is known, but those that must be lived. The challenge is not to build a machine that can better simulate wisdom, but to preserve the distinctly human spaces—of silence, reflection, and lived experience—where meditative thought can happen, and where meaning, however elusive, can be found.