Short answers, pulled from the story.
The AI effect is the phenomenon in which advances in artificial intelligence lead to a redefinition of intelligence, so that capabilities achieved by AI systems are no longer regarded as examples of real intelligence. Successful AI techniques are reclassified as routine computation or absorbed into other domains. Historian Pamela McCorduck described it as a recurring feature of AI research in her 2004 book Machines Who Think.
Tesler's theorem is commonly expressed as "AI is whatever hasn't been done yet." It captures the AI effect by showing that the word intelligence functions as a placeholder for unsolved problems rather than a stable description of any fixed capability. Once a system solves a problem, that problem is no longer counted as intelligence.
IBM's Deep Blue defeated Garry Kasparov in 1997, but critics argued the system relied on brute-force search methods rather than genuine understanding. Rather than being celebrated as proof of machine intelligence, the victory was reclassified as an example of computation, following the same pattern seen earlier with checkers-playing systems.
Historian Pamela McCorduck described the AI effect as a recurring feature of AI research in her 2004 book Machines Who Think, calling it an "odd paradox" in which successful AI systems are assimilated into other domains. Researcher Rodney Brooks made a similar observation in 2002, noting that understood systems are often dismissed as "just computation".
Nick Bostrom noted in 2006 that widely adopted technologies are often no longer labeled as AI. Marvin Minsky observed that successful AI innovations tend to evolve into separate disciplines. Michael Swaine reported in 2007 that AI advances are frequently presented to the public as developments in entirely different fields.
Researchers and commentators have noted that the capabilities of large language models and other generative AI systems are frequently described as statistical or mechanical once understood, rather than as intelligence. A 2016 survey of artificial intelligence observed that AI systems were increasingly embedded in everyday applications, a pattern consistent with the AI effect observed in earlier decades.