The name was coined by John Haugeland, with affectionate irony: Good Old Fashioned Artificial Intelligence (Haugeland, 1985, p. 112). For him, GOFAI is not just any intelligent artifact but an empirical thesis, heir to Hobbes's idea that reasoning is computation, with two commitments: (1) our ability to deal with things intelligently is due to our capacity to think about them reasonably, and (2) thinking reasonably amounts to a faculty for internal "automatic" symbol manipulation (1985, pp. 112–113). Newell and Simon stated it as the physical symbol system hypothesis: such a system has "the necessary and sufficient means for general intelligent action" (Newell & Simon, 1976, p. 116).
The bet rests on what Haugeland called the formalists' motto: "If you take care of the syntax, the semantics will take care of itself" (1985, p. 106). An interpreted formal system whose rules preserve truth, such as a logical calculus, yields true conclusions without understanding anything. From this came the Logic Theorist (Newell & Simon, 1956); expert systems such as MYCIN, with about six hundred rules distilled from interviews with physicians (M. Mitchell, 2019, ch. 2); and Deep Blue, which combined massive search with hand-coded heuristics (Buckner, 2024, p. 26).
Its virtues are transparency and explainability: every step can be read off. Its Achilles' heel was brittleness: the common sense that experts use without noticing did not fit into rules, and the systems broke down when faced with the unforeseen (M. Mitchell, 2019, ch. 2). It was machine-learning researchers who adopted the label, somewhat disparagingly (2019, ch. 2). Buckner (2024) describes the history of AI as a pendulum swinging between a great deal of hand-programmed structure (GOFAI) and very little (deep learning) (2024, p. 26). Santelli (2026) puts it with the metaphor of the architect and the gardener: GOFAI wanted to build intelligence; today's AI systems are grown.
Yet its questions are still alive. The debate over systematicity and compositionality is, at bottom, the question of whether a neural network can do what GOFAI took for granted; and the grounding problem was first raised against it. Is GOFAI a refuted theory, or a level of description that networks might be implementing without anyone having written it in?