Humans Are Just Stochastic Parrots (tinyclouds.org)

A provocative thesis argues that human cognition is functionally similar to modern statistical language models: people are "stochastic parrots" — powerful next-token predictors that stitch together patterns from vast training data without intrinsic access to meaning. The piece likens humans to an advanced Markov chain or "autocomplete on steroids," claiming our neurons encode correlations rather than truth, explaining phenomena like confident but unsupported explanations, fabricated citations, and predictable failures on adversarially modified riddles. The claim is illustrated with thought experiments showing humans overfit familiar linguistic patterns and often do not demonstrate the internal, deliberative confusion a fully reasoning agent might. For the AI/ML community this reframing matters because it narrows conceptual distance between humans and LLMs, forcing a rethink of benchmarks and expectations: apparent reasoning can arise from pattern completion and can therefore hallucinate, overfit, or generalize spuriously. Technical implications include prioritizing grounding and data provenance, designing adversarial and causally structured evaluations, improving calibration and uncertainty estimation, and combining statistical learners with symbolic or causal modules for robust reasoning. Ethically, it suggests care in attributing understanding or intent to either humans or models and highlights the importance of alignment, interpretability, and user-facing safeguards—even as both humans and models continue to produce surprisingly useful output.