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

Every time artificial intelligence achieves a milestone once thought uniquely human, something peculiar happens: we shrug and say, “That’s not real intelligence.”

When IBM’s Deep Blue defeated world chess champion Garry Kasparov in 1997, critics argued it was merely brute-force calculation, not true thought. When algorithms learned to translate languages, recognize faces, and diagnose rare diseases, skeptics dismissed them as glorified pattern matching. Even today, as generative AI models write poetry, compose symphonies, and code complex software, the refrain remains the same: It’s just math predicting the next word.

This psychological phenomenon has a name in computer science: the AI effect. It describes how, once an AI problem is successfully solved, it is instantly stripped of the label “intelligence” and reclassified as mundane computation.

Moving the Goalposts

Coined by computer scientist John McCarthy—one of the founding fathers of the field—the AI effect highlights a moving target. Humanity has a long history of defining intelligence by what machines cannot do.

• The 1950s: If a machine could do basic arithmetic faster than a human, was that thinking? Most said no; it was just a calculator.
• The 1980s: Expert systems could diagnose medical conditions or configure computer hardware. Were they intelligent? Critics said no; they were just rigid rule-based databases.
• The Modern Era: Generative models can pass the bar exam, converse fluently in dozens of languages, and generate photorealistic art. Yet, because they lack subjective experience or a biological heartbeat, we hesitate to call them “smart.”

As the AI researcher Rodney Brooks famously quipped: “Genie is no longer considered artificial intelligence as soon as it works.”

Why Do We Fall Into This Trap?

The AI effect is rooted in human psychology and ego. We tend to view our own cognitive processes as sacred, mysterious, and fundamentally unreplicable.

When a task feels effortless to us—like recognizing a friend’s face in a crowd or understanding a sarcastic joke—we assume it requires a soul or deep consciousness. But when a silicon chip executes that same task in milliseconds, the magic vanishes. We realize the mechanics behind it, demystifying the achievement and convincing ourselves that true intelligence must require something more.

We suffer from a constant case of anthropocentric bias: we equate “intelligence” not with the capacity to solve complex problems, but with human-ness.

The Danger of Dismissal

While the AI effect sounds like a quirky psychological quirk, it carries real-world consequences:

1. Underestimating Capabilities: By dismissing AI successes as “just statistics,” businesses and policymakers often fail to prepare for the massive disruptions these technologies cause in labor markets, cybersecurity, and education.
2. The “Uncanny Valley” of Trust: When AI doesn’t behave like a conscious human, people either distrust it entirely or over-rely on it, failing to recognize its unique strengths and blind spots (such as hallucinations or bias).
3. Philosophical Confusion: It traps us in an endless semantic debate. Instead of asking what tools are capable of, we get bogged down arguing over the dictionary definition of “thinking.”

Redefining Intelligence for a Synthetic Age

Perhaps the true lesson of the AI effect is that intelligence is not a monolithic crown jewel, but a spectrum of capabilities.

As machines continue to master domains once thought exclusive to biological minds, we are forced to confront a humbling truth: much of what we considered profound cognitive uniqueness—language, art, logic, and problem-solving—can be successfully emulated through architecture, data, and scale.