The AI Effect: Moving Goalposts

Labels can change as technologies become familiar. Stanford’s 2016 AI100 report describes the AI effect as a recurring pattern in which capabilities enter ordinary technology and are no longer commonly regarded as AI. The observation concerns attribution and expectations; it does not mean the capability vanished or ceased to use the same algorithms. Imagine a hypothetical text-recognition system. A difficult prototype demonstration is praised as artificial intelligence. Years later, a user calls comparable recognition a normal scanner feature. The terminology changed, but its performance, limits and engineering history still matter. Calling something ordinary software is not an argument that no technical progress occurred. Conversely, putting an AI label on a product is not evidence that it gained a new capability. Avoid using the concept to dismiss legitimate evaluation. Someone may reasonably ask whether a system works outside a benchmark, handles failures, meets privacy requirements or is affordable. Those are additional questions, not automatically bad faith. The important distinction is whether a person denies a demonstrated result by silently changing the original criterion, or explicitly proposes a new criterion for a different claim. Make comparisons concrete. State the task, data, success measure and test conditions before evaluating progress. Report what improved and what did not. If the intended use changes, explain the new requirements and test again. A system can achieve a real milestone without establishing general intelligence or suitability for every deployment. The AI effect is a useful reminder that public vocabulary and technical performance move on different tracks; neither a familiar name nor an impressive label replaces evidence.