AI’s “paraknowing” mimics understanding without true comprehension

Psychology Today writer John Nosta has introduced the concept of “paraknowing”—a term describing how AI systems mimic human knowledge without truly understanding it. This cognitive phenomenon represents a fundamental shift in how we interact with information, as large language models produce convincing responses that lack genuine comprehension or grounded experience.

What you should know: Paraknowing describes the performed knowledge that AI systems display, offering linguistic coherence without true understanding or connection to reality.

• Large language models arrange words in statistically likely patterns, creating responses that feel knowledgeable but lack intrinsic memory, belief, or genuine worldly experience.
• This differs from human approximation, which is grounded in lived reality, while machine approximation operates purely through computational math and statistical coherence.
• The term builds on Nosta’s earlier concept of “anti-intelligence,” describing AI as structurally different from rather than inferior to human cognition.

The big picture: As AI systems become more sophisticated and convincing, humans risk gradually accepting surface-level fluency as equivalent to deep understanding.

• The shift happens subtly through daily interactions—users type prompts, receive polished answers, and move on without questioning the underlying foundations.
• “We begin to trust that these words and ideas fit well together. We stop interrogating whether it’s grounded in anything beyond statistical patterns,” Nosta explains.
• This represents a potential epistemological shift where “what works replaces what is.”

Why this matters: The rise of paraknowing could fundamentally reshape how society values and pursues knowledge across multiple domains.

• Education might drift from cultivating understanding to merely teaching navigation of AI-generated information.
• Media could prioritize immediacy and fluency over substantive content requiring deeper investigation.
• Trust may shift away from expertise toward delivery quality—how smooth, convincing, and coherent information feels rather than its accuracy or depth.

The upside: Paraknowing isn’t purely detrimental and can serve as a cognitive bridge to new possibilities.

• AI systems can handle dense scaffolding of facts and connections that overwhelm human minds, freeing people to focus on synthesis, creativity, and deep questioning.
• These tools reveal unexpected relationships across vast domains, suggest new research directions, and expand imaginative reach.
• They excel at scaling human thinking and amplifying intellectual reach in ways that complement rather than replace human cognition.

The cultural implications: Extended reliance on paraknowing could gradually erode appreciation for the friction that makes human knowing meaningful.

• Human knowledge involves “the patience to sit with uncertainty, the connection between memory and meaning, the very human accountability that comes from holding a belief rather than merely presenting one.”
• The convenience of frictionless answers may dull sensitivity to the difference between performed and genuine knowledge.
• “If we live too long in the world of paraknowing, we may forget what true knowing feels like,” Nosta warns.

What he’s saying: Nosta emphasizes that humans aren’t being replaced by AI but are being reshaped by it in subtle ways.

• “The real frontier is not whether machines can know but if we will still care to.”
• He describes the mathematical complexity of large language models as “a space so mathematically complex that it defies our intuition, there’s still a flatness that haunts me. A flatland of thought that is intricate yet oddly empty.”
• The transformation “won’t arrive with a loud bang, but with a hum, camouflaged in the glow of screens and the satisfaction of instant answers.”