The Moving Goalpost: Unpacking the “AI Effect”

If you ask someone in 1960 what artificial intelligence would look like, they might imagine a mechanical human walking among us, conversing with razor-sharp wit and solving the universe’s deepest mysteries. If you ask someone today, they might talk about neural networks, generative models, or autonomous vehicles. But look closer at how we treat the technologies already living in our pockets, and a strange pattern emerges: as soon as a breakthrough technology becomes reliable, useful, and ordinary, we stop calling it artificial intelligence.

This peculiar psychological phenomenon is known in computer science as the AI effect. Coined in various forms by pioneers of the field—most famously summarized by computer scientist John McCarthy with the observation, “As soon as it works, no one calls it AI anymore”—it describes how our collective definition of machine intelligence shifts the moment a machine actually achieves it.

A History of Diminished Magic

To understand the AI effect, you only need to look at the graveyard of yesterday’s cutting-edge innovations. Technologies that once captured the imagination of science fiction writers are now treated as mundane background infrastructure.

• Optical Character Recognition (OCR): Decades ago, teaching a computer to read handwritten postal codes or printed text was considered a pinnacle of machine perception and advanced pattern matching. Today, your phone scans a receipt or extracts text from a photo in milliseconds, and you view it as a basic utility, not “intelligence.”
• Chess-Playing Algorithms: When IBM’s Deep Blue defeated world chess champion Garry Kasparov in 1997, it was front-page news worldwide, heralded as a watershed moment for machine cognition. Today, chess engines run effortlessly on smartphones, and playing against a grandmaster-level bot on your lunch break feels as ordinary as playing solitaire.
• Route Optimization and Recommendation Systems: Finding the fastest route through a congested city or predicting which movie you want to watch on a Friday night requires massive computational foresight and complex machine learning. Yet, because GPS apps and streaming platforms work reliably every day, we treat them as simple software features rather than feats of artificial intelligence.

In each case, success breeds familiarity, and familiarity strips away the aura of artificial intelligence.

Why Do We Keep Moving the Goalposts?

The AI effect is driven by a mix of human psychology, marketing, and semantics.

1. The Demystification of Mechanics: When a technology is mysterious and elusive, we project consciousness, intent, and high-level intelligence onto it. Once engineers crack the problem, break down the code, and optimize the algorithms, the mystery evaporates. We realize it is running on math and data, leading us to dismissively conclude, “Oh, that’s just an algorithm.”
2. The Definition Trap: “Intelligence” is a moving target even when applied to humans. Because we lack a unified, universally accepted definition for natural intelligence, our definition for artificial intelligence naturally defaults to whatever machines cannot yet do.
3. Commercialization and Mundanity: When AI transitions from an academic experiment into a polished consumer product—complete with a clean user interface and reliable customer support—it crosses the threshold from “frontier science” to “ordinary tool.”

The Modern Iteration: Generative AI and Beyond

We are living through the AI effect in real time. Features that felt mind-blowing just a few years ago—such as automated language translation, real-time speech transcription, and generative image tools—are rapidly being absorbed into standard office suites, operating systems, and creative software.

As these tools integrate seamlessly into daily workflows, the initial sense of wonder gives way to expectations of perfection. When a generative model makes a minor error or a transcription tool mishears a word, public reaction swings quickly from awe to critique. The technology is no longer judged against the miraculous standard of “a computer that can think,” but against the unforgiving standard of a broken household appliance.

Embracing the Normalization of Innovation

The AI effect can occasionally feel frustrating to researchers and developers who spend decades solving impossible problems, only to see their crowning achievements dismissed as “just software” the moment they hit the market.

However, the AI effect is also the ultimate measure of success. It proves that the field’s highest ambition is not to maintain an eternal aura of sci-fi mystery, but to solve problems so thoroughly that they become ordinary parts of human life. The next time you take a predictive text suggestion, a rapid translation, or an automated route adjustment for granted, remember: you are witnessing the AI effect in action.