AI dependency creates “middle-intelligence trap” for human thinking, says professor

University of Nebraska Omaha economics professor Zhigang Feng has introduced the concept of a “Middle-Intelligence Trap,” warning that society’s increasing reliance on AI tools may lead to intellectual stagnation rather than cognitive enhancement. Drawing parallels to the economic “middle-income trap” where developing nations plateau after initial growth, Feng argues that humans risk becoming too dependent on AI to think independently while failing to achieve the transcendent reasoning that true augmentation promises.

The core problem: Feng identifies a dangerous feedback loop where AI dependency gradually erodes human cognitive abilities through what he calls a “comfortable slide into intellectual mediocrity.”

• Every cognitive task outsourced to AI reduces opportunities for mental training, from basic arithmetic to complex reasoning and creative thinking.
• A recent MIT study found that people using large language models for writing showed weaker neural connectivity, producing more homogeneous essays with fewer original viewpoints.
• The risk isn’t dramatic AI rebellion but a “comfortable, almost imperceptible decline” in human intellectual capacity.

How the trap works: The process follows a predictable pattern of technological adoption and cognitive surrender spanning decades.

• Pocket calculators in the 1970s began eroding daily mental arithmetic skills.
• Word processors freed us from physical writing effort but may have weakened deep conceptual connections formed through deliberate pen-to-paper thinking.
• Search engines fundamentally restructured memory, making us “experts at remembering where information lives rather than what it contains.”

The feedback loop danger: AI systems create self-reinforcing intellectual bubbles that threaten human progress.

• AI learns from human queries and tailors responses to user preferences, while its output shapes beliefs that further refine training data.
• This creates “self-reinforcing bubbles where our biases are reflected with the sheen of machine authority.”
• Since AI trains on existing human knowledge, declining human creativity means “the well from which AI draws its intelligence will eventually run dry.”

Feng’s three-step escape strategy: The professor outlines economic-inspired solutions to avoid the Middle-Intelligence Trap.

• Build Cognitive Reserves: Deliberately protect mental skills from automation by doing math mentally, writing first drafts without assistance, and summarizing books from memory.
• Demand Strategic Friction: Create systems that slow down processes to force human engagement, recognizing that “the friction of thinking isn’t a bug to be fixed, it’s a process to be preserved.”
• Redefine Success: Ask whether tools make us smarter, not just faster, ensuring we can explain AI recommendations and generate genuinely novel breakthroughs.

What he’s saying: Feng emphasizes that avoiding intellectual decline requires conscious effort and strategic thinking about AI integration.

• “The struggle to articulate an argument or find the right word isn’t a bug in human nature, but the feature that makes our minds grow stronger.”
• “We risk becoming cognitive rentiers, living off our machines’ intellectual capital while our own powers wither.”
• “The choice, for now, is still ours.”

Why this matters: The Middle-Intelligence Trap concept addresses a critical gap in AI discourse between utopian and dystopian extremes, focusing on the more subtle but potentially more dangerous risk of gradual intellectual atrophy that could undermine both human and artificial intelligence development over time.