AI Outputs 'Good Enough' But Lack Human Effort

• EEG Brain Activity Patterns: Unassisted writers show stronger and more widely distributed electroencephalograph patterns compared to those using search engines or LLMs, indicating higher cognitive engagement during composition[1] • LLM Feature Recognition: Large language models use richer, more clinically aligned feature sets than humans for text analysis, with stronger inter-annotator agreement (κ = 0.465) due to shared training data and deterministic structure[4] • Probabilistic vs. Deterministic Processing: AI operates through probabilistic word-sequence calculation (most likely next token), fundamentally different from human writing as a mode of thinking that involves genuine memory and self-reflection[3] • Offloading Cascade Effect: When individuals delegate computational thinking to LLMs, computational thinking becomes the first cognitive capability lost, creating a feedback loop of increasing dependence[1]

The research suggests a bifurcation in creative industries: AI will likely dominate formula-driven genres by 2030, but authentic, introspective, and innovative creative work will increasingly command premium value as human-created content. This creates pressure for AI disclosure legislation (currently under U.S. Congressional consideration as of 2026), which paradoxically may harm AI-assisted creators through reader bias while protecting consumers from manipulation. The broader societal risk involves cognitive atrophy across knowledge work—users may experience productivity gains in the short term while suffering long-term erosion of critical thinking, creativity, and taste discrimination. Educational institutions face pressure to redesign curricula around the cognitive struggle itself rather than output quality, as AI commoditizes the final product.