The AI Effect: Why We Always Move the Goalposts on Intelligence

Think about the last time your phone automatically sorted your photos by recognizing faces, or your GPS rerouted you around a sudden traffic jam. Did you marvel at the pinnacle of artificial intelligence at work?

Probably not. You likely just thought, “My phone is working.”

This psychological quirk—where groundbreaking technological achievements are instantly dismissed as “just software” or “ordinary algorithms” the moment they become reliable—is known in computer science as the AI effect. Coined by pioneer John McCarthy, the phenomenon boils down to a sharp observation: “As soon as it works, no one calls it AI anymore.”

The Moving Goalposts of Machine Intelligence

For decades, the definition of artificial intelligence has been plagued by a moving target. Whenever researchers conquer a monumental hurdle, society doesn’t celebrate the birth of artificial intelligence; instead, we redefine what “real” intelligence is to keep machines safely outside the club.

Consider how our perception of capability has shifted over time:

• The 1950s: Playing chess was considered the ultimate test of human reason and strategic brilliance. Surely, a machine that could defeat a grandmaster would possess a mind of its own.
• 1997: IBM’s Deep Blue defeated world chess champion Garry Kasparov.
• The Aftermath: Instead of acknowledging machine sentience, critics shrugged and said, “Well, it’s just brute-force calculation and pattern matching. It doesn’t actually understand chess.”

The goalposts had moved. Intelligence was no longer about calculating moves ahead; it was about “intuition,” “creativity,” or “consciousness.”

A History of Disappearing Magic

The AI effect is a historical pattern repeating itself. Technologies that once sounded straight out of science fiction are now so deeply embedded in our daily lives that we forget they are powered by complex machine learning:

“True AI is whatever hasn’t been done yet.” — Larry Tesler’s theorem variant for AI

1. Optical Character Recognition (OCR): In the mid-20th century, teaching a computer to read handwritten notes or printed text seemed like a monumental cognitive challenge. Today, your smartphone instantly scans text from a photo or a business card without a second thought.
2. Machine Translation: Translating languages used to require elite human polyglots. Now, instantaneous multi-lingual translation happens in our earbuds and web browsers, viewed merely as a convenient utility rather than a stroke of genius.
3. Spam Filters: Early email platforms required complex manual rules. Modern spam filters use adaptive machine learning to protect our inboxes from billions of threats daily. We only notice them when they fail—never when they succeed.

The Modern Paradox: Living in the Era of Generative AI

Today, we find ourselves in the most intense cycle of the AI effect yet. Generative AI tools—capable of writing code, composing symphonies, generating photorealistic imagery, and conversing fluently—are reshaping industries.