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

We have all done it. You type a prompt into an advanced AI model, watch as it instantly synthesizes a complex computer program, drafts a professional legal contract, or paints a Renaissance-style portrait in seconds, and you think: Well, that’s just neat software. It doesn’t actually understand anything.

Moments later, you might find yourself frustrated because a chatbot didn’t pick up on a subtle piece of sarcasm, or because a robotic vacuum cleaner got stuck under the couch. To us, the vacuum’s struggle feels like “true” failure, while the AI’s brilliant creative output is dismissed as mere statistical matching.

Welcome to the AI effect.

First coined by computer science researcher John McCarthy, the term describes a peculiar psychological phenomenon: once an AI system successfully solves a problem, we stop considering it “real” artificial intelligence. Instead, we quietly reclassify it as mundane automation, brute-force computing, or just plain software.

Moving the Goalposts of Intelligence

The history of artificial intelligence is essentially a history of shifting definitions. Every time a milestone is reached, the goalposts move.

• The 1950s—Chess and Computation: When early computer scientists began teaching machines to play chess, critics argued that true intelligence required human-like strategic thought. When IBM’s Deep Blue defeated world chess champion Garry Kasparov in 1997, skeptics shrugged: It’s just raw processing power and brute-force calculation; it doesn’t “know” it’s playing.
• The 2010s—Vision and Translation: For decades, teaching a computer to recognize a cat in a photograph or translate spoken Mandarin into English in real-time seemed like the pinnacle of cognitive science. Today, optical character recognition and instant language translation are baked into our smartphones, treated as utility features rather than technological marvels.
• The Present Day—Reasoning and Creativity: Generative AI models can now write poetry, debug code, and analyze medical scans with startling accuracy. Yet, the moment an LLM makes a factual hallucination or struggles with basic common sense, public sentiment swings instantly back to: See? It’s just a glorified predictive text engine.

We judge artificial intelligence by an impossible standard: human perfection. If a machine makes a mistake that a human wouldn’t, we write off its intelligence entirely, ignoring the countless ways it outperforms us.

Why Do We Fall for the AI Effect?

Our reluctance to grant AI the title of “intelligent” stems from a deep-seated human bias.

1. The Mystery Factor: Human intelligence is wrapped in consciousness, emotion, and subjective experience (qualia). Because we don’t fully understand our own minds, we associate intelligence with the feeling of being alive. When a machine operates through cold mathematics and matrix multiplication, it feels fundamentally alien to us—no matter how eloquent its output.
2. Familiarity Breeds Contempt: As Arthur C. Clarke famously noted, “Any sufficiently advanced technology is indistinguishable from magic.” But once that magic becomes a routine part of our Tuesday morning workflow—like auto-completing an email or navigating via GPS—the magic evaporates, and we take it for granted.
3. The Semantic Trap: The word “intelligence” itself is a moving target. We define it anthropocentrically. If an AI cannot experience grief, fall in love, or smell fresh coffee, we hesitate to call it intelligent, conflating sapience (consciousness) with cognitive capability (problem-solving).

The Danger of Dismissing AI

While cynicism can protect us from tech-industry hype, the AI effect carries real risks.

When we dismiss accomplishments as “just math,” we underestimate the profound societal shifts they bring. We risk overlooking the ethical challenges of deploying systems we mistakenly believe are harmless, or conversely, panicking over sci-fi doomsday scenarios while ignoring the tangible, mundane impacts of automated systems—such as algorithmic bias in hiring, copyright dilemmas, and workforce displacement.

Furthermore, falling victim to the AI effect blinds us to our own cognitive quirks. Humans rely heavily on pattern recognition, heuristics, and internalized memory—mechanisms not entirely dissimilar from the neural networks we so readily dismiss.

Redefining the Future

The AI effect reminds us that intelligence is not a fixed summit, but a horizon that recedes the closer we get to it.

Perhaps the solution is to stop waiting for machines to become human and instead appreciate them for what they truly are: entirely new forms of cognitive mirrors. They reflect our language, our art, our history, and our flaws back at us at hyperspeed.

The next time an AI assistant drafts an email in seconds, solves a complex equation, or generates a breathtaking image, try to resist the urge to say, “It’s just code.” Instead, marvel at the fact that a collection of silicon and electricity can even approximate the wonders of human thought.

What is your take? At what point—if ever—would you look at an artificial intelligence system and genuinely call it “intelligent”?