On: Is AI Reasoning Right for the Wrong Reasons?

August 1, 2026.

The morning papers are occupied with the question of whether these new machines are truly reasoning or merely arriving at correct conclusions through a series of fortunate accidents. The debate suffers from a lack of linguistic hygiene. Before one can ask if a machine reasons, one must define “reasoning” in a way that does not presuppose a human nervous system. If we define it as the ability to derive a conclusion from a set of premises according to the laws of logic, the machine appears to succeed. If we define it as a conscious apprehension of the truth, we have moved from logic into theology.

The critics argue that the machine is “right for the wrong reasons.” This implies there is a moral or metaphysical quality to a syllogism that exists apart from its validity. In geometry, if a student proves the Pythagorean theorem by a sequence of valid steps, we do not say he has failed because he did not “feel” the triangle. The proof is the evidence of the reasoning. To demand more is to demand a ghost in the machine.

However, the evidence for “reasoning” in these models is often anecdotal. We see a correct answer and infer a process. This is a common inductive fallacy. If a man stops a clock, he will be exactly right twice a day; he is not, however, a reliable timekeeper. We must determine if the machine’s successes are the result of a robust logical architecture or a vast library of memorized instances. If the latter is true, the machine does not reason; it merely quotes. The distinction is not one of degree, but of kind. We require an experiment where the machine must solve a problem whose structure has never appeared in its training data. Until then, our confidence in its “intellect” should be strictly proportional to its performance on the novel, not the familiar.