The Only AI Explainer You'll Ever Need (kemendo.com)

Andrew Kemendo’s “The Only AI Explainer You’ll Ever Need” is a concise genealogy and definitional primer that traces AI back to the 1955 Dartmouth proposal, which framed AI as the conjecture that “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.” Kemendo argues that much confusion comes from newcomers assuming AI began with the latest hype; in reality AI is a research direction whose solved parts get rebranded as separate disciplines (e.g., perception → computer vision, search → gradient descent, language → NLP, neural nets → CNNs/DNNs/LLMs), leaving “AI” as whatever remains unsolved — the so‑called “AI Effect.” The piece also situates modern debates—AGI vs. narrow AI, ethics, and existential risk—within a long lineage of thinkers (Norbert Wiener, Weizenbaum, Bostrom) and maps the provenance of “AGI” through Ben Goertzel’s work. Key technical implications: AI isn’t a single technology but a spectrum of techniques (Bayesian methods, MCTS, MDPs, LSTMs, transformers, etc.) whose boundaries shift as capabilities mature; the “core AGI hypothesis” distinguishes broadly general, human‑level intelligence from narrow, task-specific systems. Understanding this history and taxonomy reframes policy, research priorities, and ethics discussions, cutting through hype and anchoring arguments in established ideas.