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

We’ve all been there: You type a question into an advanced language model, and seconds later, it synthesizes a college-level essay, translates a complex legal document, or writes working code. A decade ago, this would have felt like absolute magic. Today? You might look at the screen, notice a slightly awkward phrasing, and think, “Well, it’s not really thinking; it’s just predicting the next word.”

Welcome to The AI Effect.

Coined by computer scientist John McCarthy, the AI effect describes a peculiar psychological and cultural phenomenon: As soon as an AI system succeeds in solving a previously “impossible” problem, society stops considering it artificial intelligence at all.

Instead, we rebrand it as mere “automation,” “complex math,” or simply “software.” We move the goalposts, demanding ever-higher peaks of consciousness, intuition, and general reasoning before we are willing to grant a piece of code the title of true “AI.”

The Moving Goalposts of Intelligence

To understand how the AI effect works, you only need to look at the history of computer science. Tasks that once baffled researchers and were considered the absolute pinnacle of human cognition have quietly transitioned into mundane background utilities:

• Chess and Strategy: In 1997, the world watched in awe as IBM’s Deep Blue defeated world chess champion Garry Kasparov. It was hailed as a monumental triumph for artificial intelligence. Yet, shortly after, as chess engines became standard tools on every smartphone, public perception shifted: “Well, it’s just brute-force calculation; it doesn’t actually understand the game.”
• Optical Character Recognition (OCR) & Translation: Recognizing handwritten text or translating languages in real-time used to require advanced human intelligence. Today, your phone scans receipts and street signs instantly. We don’t call this AI anymore; we call it a “feature.”
• Recommendation Algorithms: The algorithms that curate your social media feeds or predict what movie you want to watch next are powered by sophisticated machine learning models. But because they sometimes recommend a bad movie or a redundant product, we dismiss them as annoying code rather than intelligent agents.

In essence, AI is defined not by what it can do, but by what it hasn’t mastered yet. The moment a machine conquers a domain, that domain is instantly evicted from the realm of “real intelligence” and categorized as ordinary computation.

Why Do We Fall Into the Trap?

The AI effect is driven by a mix of psychological defense mechanisms and linguistic quirks.

1. The Demystification of Mechanism

Once we understand how something works, it loses its aura of magic. When we learn that modern generative AI operates on probabilities, attention mechanisms, and vast matrices of weights rather than conscious thought and emotion, we feel let down. We confuse mechanism with absence of intelligence. (Ironically, human brains are also biological prediction and pattern-matching machines, yet we rarely dismiss our own cognition as “just chemistry.”)

2. The Anthropomorphic Trap

Humans have a deep-seated bias toward defining intelligence in our own image. We associate intelligence with consciousness, subjective experience, pain, joy, and organic life. Because silicon chips don’t feel heartbreak or enjoy a cup of coffee, our intuition rebels against calling their outputs “intelligent.”

3. The “Uncanny Valley” of Capability

As AI systems improve, they often land in a frustrating middle ground. They can write a brilliant poem, but fail at basic common-sense physics. These glaring imperfections make it very easy for skeptics to focus on what the AI cannot do, completely discounting the staggering complexity of what it can do.

The Danger of Moving the Goalposts

While the AI effect might seem like a harmless quirk of human semantics, it carries real-world consequences:

• Complacency: By constantly dismissing current breakthroughs as “not real AI,” we risk missing the profound economic, social, and structural disruptions these technologies cause right now.
• Misaligned Regulators and Ethics: Policymakers caught up in the myth that “true AI doesn’t exist yet” may fail to regulate existing algorithms that heavily influence credit scores, hiring processes, criminal sentencing, and public discourse.
• The Seductive Illusion of Superiority: Believing that machines are “just glorified calculators” can lead us to overestimate human uniqueness in domains where algorithms are already outperforming us, from diagnostic medicine to protein folding.

Redefining Intelligence for the Silicon Age

Perhaps the ultimate lesson of the AI effect is that our definition of “intelligence” has always been self-centered. We built mirrors of our own reasoning, and when the reflection looked back at us, we blinked, looked away, and declared it wasn’t human enough to count.

As we march further into an era defined by autonomous systems and artificial general intelligence (AGI), the challenge isn’t forcing machines to feel or think like biological humans. The challenge is recognizing intelligence wherever it appears—whether it is born of carbon or forged in silicon.