The Indeed Gap: Why AI Is Both Narrowing and Widening Human Differences

The discourse around artificial intelligence often presents two seemingly contradictory conclusions about its impact on human cognitive disparities. This article explores both perspectives through quantitative analysis, revealing that they address different temporal horizons—and indeed, both contain kernels of truth about how the gap between humans is being transformed by technological advancement.

Mapping Cognitive Ability Across Generations

To ground this discussion, let’s establish a hypothetical framework for measuring cognitive capability. An elementary school student might represent 10 units of cognitive ability, a PhD candidate 60 units, a university professor 75 units, and Einstein 100 units. The gap separating an elementary student from Einstein is 90 units, representing a 10x difference—a disparity often compared to the gap between humans and animals.

Current AI systems demonstrate considerable capability. Conservative estimates place contemporary AI at 40 points of cognitive value; however, when accounting for AI’s broad knowledge base compared to the specialized expertise of academics, a more realistic valuation reaches approximately 80 points.

When we integrate AI into this framework, the picture transforms:

• Elementary student + AI = 90 points
• PhD + AI = 140 points
• Professor + AI = 155 points
• Einstein + AI = 180 points

Here emerges a striking insight: while the absolute gap remains substantial at 90 points, the relative proportional difference compresses from 10x to merely 2x. This compression forms the basis for the argument that artificial intelligence narrows the fundamental gap between human cognitive capacities.

The Proficiency Paradox: When Tools Amplify Individual Disparities

Yet this narrative encounters a formidable objection grounded in observable reality. The cognitive value of any tool depends entirely upon the user’s mastery of it. Consider the analogy from animation: even identical superpower capabilities yield vastly different outcomes depending on the wielder’s expertise. A novice might access only 20% of an advanced AI’s potential, while a skilled practitioner—through techniques like sophisticated prompt engineering or intensive collaborative coding—might unlock 100% or even exceed it through optimization strategies.

Recalculating with this proficiency variable:

• Elementary student + novice AI user = 30 points
• Einstein + expert AI user = 200 points

Now the gap expands to 170 points. The introduction of powerful AI tools, in this scenario, actually widens the gap between individuals. This perspective carries weight; the difference is indeed profound, and this amplification effect represents a genuine, observable concern as AI literacy becomes an increasingly critical skill.

From Expanding Gaps to Converging Abilities: The Two-Factor Evolution of AI

Both perspectives contain validity, but they examine different developmental stages. The critical insight lies in recognizing that artificial intelligence continues to evolve along two distinct but complementary dimensions.

First, AI systems become increasingly intelligent. The cognitive capacity of AI is not static. As these systems advance, their effective capability scores rise substantially. Imagine a future state where AI reaches 240 points—this would establish new capability baselines:

• Elementary student utilizing developed AI = 210 points
• Einstein utilizing advanced AI = 280 points

The gap increases to 70 points in absolute terms, yet the relative gap actually decreases. The ratio shifts from 2x to 1.8x—a compression rather than an expansion.

Second, and equally crucial, AI becomes progressively easier to use. The skill threshold for effective AI deployment diminishes continuously. What currently requires advanced technical knowledge—sophisticated prompt formulation, system design thinking—inevitably becomes simplified. The trajectory mirrors historical precedent: programming itself evolved from machine code to high-level languages precisely because tools became more intuitive and accessible.

This democratization fundamentally alters the proficiency variable. Rather than remaining at the novice 20% utilization versus expert 100% dichotomy, future norms will establish humans consistently operating at 80-120% of AI’s potential across broader populations. The outliers will increasingly narrow.

The Ultimate Scenario: When Artificial Intelligence Becomes Universally Accessible

Project this evolution forward a decade or two. Suppose AI advances to approximately 1000 points of cognitive capability, while simultaneously becoming trivially simple to apply. At this juncture:

• Elementary student = 1010 points
• Einstein = 1100 points

The absolute gap widens to 90 points, yet the relative gap collapses to 1.1x. Even genius becomes indistinguishable from ordinariness when both possess access to transformative capability that overwhelms individual variation.

Consider a contemporary analogy: two martial artists with radically different skill levels discover they may now deploy shoulder-mounted rocket launchers. The difference between a master with ten years of training and another with fifteen suddenly becomes meaningless. The tool’s power eclipses the operator’s refinement.

Resolving the Paradox: Why Long-Term Trends Trump Short-Term Disparities

The apparent contradiction between these perspectives vanishes when temporal scope expands. The widening of gaps due to proficiency variance represents a transitional phenomenon, not a permanent condition.

Skeptics sometimes express concern: “Won’t those skilled at leveraging AI remain perpetually superior?” This reasoning overlooks a historical inevitability. If artificial intelligence can replace writers, illustrators, choreographers, and visual artists, why assume it cannot eventually replace the trainers who teach others to optimize AI utilization? This commodification of expertise is precisely what AI does—it automates away specialized knowledge.

The indeed-transformative force operates not through maintaining disparities but through eliminating the conditions that create them. As AI intelligence accelerates and interface accessibility deepens, the marginal advantage of being an early, skilled adopter erodes. Proficiency differences compress because the system increasingly adapts to users rather than demanding users adapt to the system.

The cognitive gap between humans, while potentially magnified temporarily by unequal access and expertise, faces inevitable compression as these systems mature. The gap between humans and transformative technology narrows most dramatically—not in absolute terms necessarily, but in the relative significance of individual human variation. We approach a future where artificial intelligence becomes the great equalizer, making individual cognitive differences, indeed, increasingly negligible.