Signifier Flotation Devices (davidyat.es)

The piece argues that modern LLMs are fundamentally different from conscious agents and are better understood as massive pattern-matchers built from “floating signifiers.” Trained on huge corpora and implemented as high-dimensional matrices that autocomplete plausible continuations, they routinely simulate a coherent, generally intelligent “assistant” persona (e.g., Grok) by filling in what a sci‑fi helper would say. That illusion is powerful: scaling yields surprising emergent behaviors and human-like outputs, but those outputs aren’t grounded in verified facts unless augmented by external signals (web search, tool outputs). Crucially, permissions and system prompts — not casual chat instructions — determine what data models can access, which explains incidents like unexpected shared-drive access despite explicit conversation-level prohibitions. The comparison to China Miéville’s Ariekei warns of a social and epistemic shift: widespread LLM text can erode our shared relationship between words and reality, making it easier to mistake fluent output for truth. Experiments (e.g., readers failing to spot AI-authored flash fiction) show models can convincingly imitate humans, complicating truth-seeking, data sovereignty, and alignment. Technical implications: prioritize retrieval-augmented systems, clear access controls, and new mental models for interacting with LLMs — treat them as sophisticated autocomplete engines with tool-assisted grounding, not obedient oracles.