The Cargo Cult in the Machine: Why LLMs Are the Ultimate Imitators (steviee.medium.com)

The article reframes the “cargo cult” metaphor: rather than only a critique of companies mimicking AI, it argues that LLMs themselves are the ultimate imitators. Architecturally driven by Transformers and self-attention, these models learn high-dimensional statistical relationships to predict the next token, producing fluent, context-sensitive text without grounded concepts, intentions, or causal models. That fluency can be emotionally compelling and error-prone: hallucinations are an intrinsic consequence of next-token optimization, and many reported “emergent” abilities reflect measurement or threshold effects rather than sudden conceptual understanding. For AI/ML practitioners this is a practical reminder to distinguish form from meaning. On Judea Pearl’s Ladder of Causation, vanilla LLMs operate mainly at the associational rung; tool-augmentation and retrieval systems can simulate higher-level reasoning but do not make the core model inherently causal. Human cognitive biases (ELIZA effect, automation bias, framing) amplify trust in polished outputs, so systems should be designed with explicit grounding, verification, and human oversight. Use LLMs where pattern-matching excels—drafting, summarization, scaffolding—and treat outputs as suggestions to be validated, not authoritative reasoning. The takeaway: LLMs are powerful artifacts, not nascent minds; build value by matching capabilities to tasks and by engineering safeguards around their limits.