Moving the Goalposts of Mind: Understanding the “AI Effect”

If you ask someone in 1950 what artificial intelligence would look like, they might have imagined a mechanical brain capable of arithmetic, playing a respectable game of chess, or translating sentences between English and French. Back then, those feats were viewed as the exclusive stronghold of human thought—monumental peaks of cognition.

Today, a pocket calculator does arithmetic in microseconds, a smartphone casually beats chess grandmasters, and translation software operates seamlessly in real time. Yet, rather than marveling at these milestones as crowning achievements of thinking machines, we treat them as mundane background utilities. When a machine finally masters a cognitive hurdle, we do not celebrate it as a triumph of artificial intelligence; we redefine it as “just math,” “brute-force computation,” or “glorified autocomplete.”

This curious shifting of the baseline is known in computer science and philosophy as the AI effect: AI is whatever hasn’t been done yet.

The Anatomy of a Moving Target

Coined informally in the decades following the dawn of computer science, the AI effect captures a psychological and cultural paradox. As soon as a machine successfully solves a problem once thought to require human intellect, society stops viewing that problem as a benchmark of intelligence.

Consider the trajectory of optical character recognition (OCR)—the technology that allows computers to “read” handwritten text or scanned documents. In the 1960s and 70s, teaching a machine to recognize letters of various fonts was considered a profound artificial intelligence puzzle requiring sophisticated perceptual reasoning. Today, OCR is baked invisibly into PDF readers and mobile cameras. Nobody looks at their phone scanning a receipt and whispers in awe, “Behold, artificial intelligence is unfolding before our eyes.” It has been demoted to standard software engineering.

The same pattern applies recursively:

• Chess and Strategy: When IBM’s Deep Blue defeated world champion Garry Kasparov in 1997, critics and observers quickly dismissed it, arguing it was merely a brute-force calculation engine lacking true understanding. Decades later, when reinforcement-learning algorithms like AlphaGo mastered Go—a game with more possible board configurations than atoms in the observable universe—the goalposts shifted again to focus on emotional intelligence, creativity, or general reasoning.
• Language and Generation: Natural language processing underwent the exact same cycle. For decades, generating coherent paragraphs or holding a fluid conversation was deemed the ultimate fortress of human uniqueness. Now that generative models draft emails, write code, and converse fluently, skeptics frequently wave them away as stochastic parrots—predicting the next likely word without a soul behind the screen.

Why Do We Move the Goalposts?

The AI effect is more than just human stubbornness; it is rooted in how we define cognition and cope with technological change.

1. The Demystification of Mechanics: Once an algorithm’s internal mechanism is exposed, mapped, and commercialized, it loses its aura of magic. Human intelligence has long been romanticized as an ineffable spark. When a silicon chip replicates an output using linear algebra and probability matrices, we feel disillusioned. We conclude that because it can be reduced to math, it was never “real” intelligence to begin with.
2. Functional Assimilation: Computer scientist Pamela McCorduck famously noted an odd paradox: successful AI systems are rapidly assimilated into mainstream computing, leaving researchers to push forward into new, unsolved territory. Once a technology works reliably, it stops being “AI” and simply becomes “technology”.
3. The Egocentric Definition of Cognition: Humans tend to define intelligence by our current frontiers—whatever we can do that machines currently cannot. Because we view our own consciousness as the gold standard, any capability a machine masters is retroactively disqualified from the definition of intelligence. In essence, intelligence is treated as a moving horizon: no matter how fast you run toward it, the horizon retreats at the exact same pace.

The Double-Edged Sword of the Effect

While the AI effect often stems from a cynical or dismissive view of technological progress, it plays a practical role in keeping expectations grounded. It acts as a defense mechanism against anthropomorphism, reminding us that pattern-matching algorithms, no matter how sophisticated, do not possess biological sentience, lived experience, or moral agency.

However, the effect can also blind us to the profound societal transformation happening right beneath our feet. By constantly minimizing machine achievements as “mere calculation,” we risk undervaluing the economic, behavioral, and cultural disruptions these systems cause. When automation is written off as routine software, we may fail to properly regulate labor shifts, address cognitive offloading, or grapple with the ethical dimensions of systems that behave intelligently, regardless of whether they “feel” it.

The Ultimate Horizon

As artificial intelligence continues to bleed into every corner of modern life, the AI effect reveals a deeper truth about human nature. We are endlessly searching for a boundary line that separates us from our creations. Yet every time technology crosses that line, we redraw it further back, determined to preserve a sanctuary for human uniqueness.

Perhaps the ultimate takeaway of the AI effect is that intelligence is not a static summit. It is an evolving frontier—and as machines conquer what was once miraculous, they force us to ask a harder question: If everything we thought required a soul can be computed, what does it truly mean to be human?