# AI effect > Mediated Wiki article. Canonical URL: https://mediated.wiki/source/AI_effect > Markdown URL: https://mediated.wiki/source/AI_effect.md > Source: https://en.wikipedia.org/wiki/AI_effect > Source revision: 1356151178 > License: Creative Commons Attribution-ShareAlike 4.0 International (https://creativecommons.org/licenses/by-sa/4.0/) Phenomenon in which AI achievements are reclassified as non-intelligent Part of a series on Artificial intelligence (AI) Major goals Artificial general intelligence Intelligent agent Recursive self-improvement Planning Computer vision General game playing Knowledge representation Natural language processing Robotics AI safety Approaches Machine learning Symbolic Deep learning Bayesian networks Evolutionary algorithms Neuro-symbolic AI Systems integration Open-source AI data centers Applications Bioinformatics Deepfake Earth sciences Finance Generative AI Art Audio Music Government Healthcare Industry Software development Translation Military Physics Projects Philosophy AI alignment Artificial consciousness The bitter lesson Chinese room Friendly AI Ethics Existential risk Turing test Uncanny valley Human–AI interaction History Timeline Progress AI winter AI boom AI bubble Controversies Deepfake pornography Taylor Swift deepfake pornography controversy Grok sexual deepfake scandal Google Gemini image generation controversy It's the Most Terrible Time of the Year Pause Giant AI Experiments Removal of Sam Altman from OpenAI Statement on AI Risk Tay (chatbot) Théâtre D'opéra Spatial Voiceverse NFT plagiarism scandal Glossary Glossary v t e The **AI effect** is a phenomenon in which advances in [artificial intelligence](/source/Artificial_intelligence) lead to a redefinition of what is considered intelligence, such that capabilities achieved by AI systems are no longer regarded as examples of "real" intelligence.[1] The concept has been used to describe both a cognitive tendency and a sociotechnical pattern, in which successful AI techniques are reclassified as routine computation or absorbed into other domains.[2] Historian [Pamela McCorduck](/source/Pamela_McCorduck) described this as a recurring feature of AI research, noting in her 2004 book *Machines Who Think* that once a problem is solved, it is no longer considered evidence of intelligence.[3] Researcher [Rodney Brooks](/source/Rodney_Brooks) similarly observed in 2002 that once systems are understood, they are often regarded as "just computation".[4] ## Definition The AI effect refers to a shift in how intelligence is defined as machines acquire new capabilities. Tasks such as playing chess, recognizing speech, or interpreting images were historically considered indicators of intelligence, but after successful automation they are often reclassified as routine computation.[1] McCorduck described this as an "odd paradox", in which successful AI systems are assimilated into other domains, leaving AI researchers to focus on unsolved problems.[5] The phenomenon is often interpreted as an instance of [moving the goalposts](/source/Moving_the_goalposts).[6] A commonly cited formulation is Tesler's theorem, often expressed as "AI is whatever hasn't been done yet".[7] When problems are not fully formalised, they may be described using models involving [human computation](/source/Human_computation), such as human-assisted [Turing machines](/source/Turing_machine).[8] ## Historical examples ### Game playing Early AI systems capable of playing games such as checkers and chess were initially regarded as demonstrations of machine intelligence. As these systems improved and became better understood, their achievements were often reinterpreted as examples of computation rather than intelligence.[9] The victory of IBM's [Deep Blue](/source/Deep_Blue_(chess_computer)) over [Garry Kasparov](/source/Garry_Kasparov) in 1997 is a frequently cited example. Critics argued that the system relied on brute-force methods rather than genuine understanding.[9] ### Pattern recognition Technologies such as [optical character recognition](/source/Optical_character_recognition) and speech recognition were once considered core problems in artificial intelligence. As these systems became reliable and widely deployed, they were increasingly treated as standard engineering solutions.[1] ### Integration into applications Many techniques originally developed within AI research have been incorporated into broader technological systems, including marketing, automation, and software applications.[2] [Michael Swaine](/source/Michael_Swaine_(technical_author)) reported in 2007 that AI advances are often presented as developments in other fields.[10] [Marvin Minsky](/source/Marvin_Minsky) observed that successful AI innovations often evolve into separate disciplines.[11] [Nick Bostrom](/source/Nick_Bostrom) noted in 2006 that widely adopted technologies are often no longer labeled as AI.[12] ## Contemporary discussion The AI effect continues to be discussed in the context of recent advances in [machine learning](/source/Machine_learning), particularly [large language models](/source/Large_language_models) and other [generative AI](/source/Generative_AI) systems. As these systems have become more widely used, some researchers and commentators have noted that their capabilities are frequently described as statistical or mechanical once understood, rather than as intelligence.[13] A 2016 survey of artificial intelligence also noted that AI systems are increasingly embedded in everyday applications, reinforcing earlier observations that successful AI technologies tend to become normalized and no longer identified as AI.[14] At the same time, the widespread commercial use of artificial intelligence has led to greater visibility of the field, contrasting with earlier periods in which AI techniques were often present but unacknowledged.[1] ## Interpretations ### Cognitive bias Some authors describe the AI effect as a cognitive bias in which expectations of intelligence shift as machines achieve new capabilities.[2] ### Sociotechnical perspective Another interpretation emphasizes how technologies are reclassified over time as they become widespread and commercially successful.[1] ### Philosophical debate Some philosophers argue that reclassification reflects genuine conceptual distinctions rather than bias.[6] ## Historical context During periods such as the [AI winter](/source/AI_winter), researchers sometimes avoided the term "artificial intelligence" due to negative perceptions.[1] In the 21st century, however, the term "AI" has become widely used in public discourse and marketing.[1] ## Broader implications The AI effect has been linked to broader questions about human uniqueness and the nature of intelligence. [Michael Kearns](/source/Michael_Kearns_(computer_scientist)) suggested that people may seek to preserve a special role for humans.[15] Similar patterns have been observed in studies of [animal cognition](/source/Animal_cognition). [Herbert A. Simon](/source/Herbert_A._Simon) noted that artificial intelligence can provoke strong emotional reactions.[16] ## See also - [ELIZA effect](/source/ELIZA_effect) - [Moving the goalposts](/source/Moving_the_goalposts) - [Moravec's paradox](/source/Moravec's_paradox) - [Chinese room](/source/Chinese_room) - [Functionalism (philosophy of mind)](/source/Functionalism_(philosophy_of_mind)) - [Computational intelligence](/source/Computational_intelligence) - [Artificial intelligence in video games](/source/Artificial_intelligence_in_video_games) - [God of the gaps](/source/God_of_the_gaps) - [Organoid intelligence](/source/Organoid_intelligence) ## References 1. ^ [***a***](#cite_ref-haenlein_1-0) [***b***](#cite_ref-haenlein_1-1) [***c***](#cite_ref-haenlein_1-2) [***d***](#cite_ref-haenlein_1-3) [***e***](#cite_ref-haenlein_1-4) [***f***](#cite_ref-haenlein_1-5) [***g***](#cite_ref-haenlein_1-6) Haenlein, Michael; Kaplan, Andreas (2019). "A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence". *California Management Review*. **61** (4): 5–14. [doi](/source/Doi_(identifier)):[10.1177/0008125619864925](https://doi.org/10.1177%2F0008125619864925). 1. ^ [***a***](#cite_ref-henke_2-0) [***b***](#cite_ref-henke_2-1) [***c***](#cite_ref-henke_2-2) ["AI Glossary"](https://web.archive.org/web/20080509132655/http://www.stottlerhenke.com/ai_general/glossary.htm). Stottler Henke Associates. Archived from [the original](http://www.stottlerhenke.com/ai_general/glossary.htm) on 2008-05-09. Retrieved 20 March 2026. 1. **[^](#cite_ref-mcc1_3-0)** McCorduck, Pamela (2004). *Machines Who Think* (2nd ed.). A. K. Peters. p. 204. 1. **[^](#cite_ref-4)** Kahn, Jennifer (March 2002). ["It's Alive"](https://www.wired.com/2002/03/everywhere/). *Wired*. Retrieved 20 March 2026. 1. **[^](#cite_ref-mcc2_5-0)** McCorduck, Pamela (2004). *Machines Who Think* (2nd ed.). A. K. Peters. p. 423. 1. ^ [***a***](#cite_ref-nadin_6-0) [***b***](#cite_ref-nadin_6-1) Nadin, Mihai (2023). "Intelligence at any price? A criterion for defining AI". *AI & Society*. **38** (5): 1813–1817. [doi](/source/Doi_(identifier)):[10.1007/s00146-023-01695-0](https://doi.org/10.1007%2Fs00146-023-01695-0). 1. **[^](#cite_ref-7)** Hofstadter, Douglas (1980). *Gödel, Escher, Bach: an Eternal Golden Braid*. Basic Books. p. 601. 1. **[^](#cite_ref-8)** Shahaf, Dafna; Amir, Eyal (2007). ["Towards a theory of AI completeness"](https://web.archive.org/web/20070824040343/http://www.cs.uiuc.edu/~eyal/papers/ai-complete-commonsense07.pdf) (PDF). *Proceedings of the 8th International Symposium on Logical Formalizations of Commonsense Reasoning (Commonsense 2007)*. 1. ^ [***a***](#cite_ref-mcc3_9-0) [***b***](#cite_ref-mcc3_9-1) McCorduck, Pamela (2004). *Machines Who Think* (2nd ed.). A. K. Peters. p. 433. 1. **[^](#cite_ref-10)** Swaine, Michael. ["AI – It's OK Again!"](http://philippe.ameline.free.fr/techtreads/070905_AiOkAgain.htm). Retrieved 20 March 2026. 1. **[^](#cite_ref-11)** Minsky, Marvin. ["The Age of Intelligent Machines: Thoughts About Artificial Intelligence"](https://web.archive.org/web/20090628081048/http://www.kurzweilai.net/articles/art0100.html?printable=1). Archived from [the original](http://www.kurzweilai.net/articles/art0100.html?printable=1) on 2009-06-28. Retrieved 20 March 2026. 1. **[^](#cite_ref-12)** ["AI set to exceed human brain power"](https://edition.cnn.com/2006/TECH/science/07/24/ai.bostrom/). *CNN*. 2006-08-09. Retrieved 2026-04-27. 1. **[^](#cite_ref-13)** Bommasani, Rishi (2021). [On the Opportunities and Risks of Foundation Models](https://arxiv.org/abs/2108.07258) (Report). Stanford Center for Research on Foundation Models. 1. **[^](#cite_ref-14)** Stone, Peter (2016). [Artificial Intelligence and Life in 2030](https://ai100.stanford.edu/2016-report) (Report). Stanford University. 1. **[^](#cite_ref-15)** Flam, Faye (January 15, 2004). "A new robot makes a leap in brainpower". *Philadelphia Inquirer*. 1. **[^](#cite_ref-16)** Hann, Reuben L. (1998). "A Conversation with Herbert Simon". *Gateway*. ## Further reading - McCorduck, Pamela (2004). *Machines Who Think*. - Hofstadter, Douglas (1980). *Gödel, Escher, Bach*. - Phillips, Everard M. (1999). [*If It Works, It's Not AI*](https://dspace.mit.edu/bitstream/handle/1721.1/80558/43557450-MIT.pdf?sequence=2) (PDF) (Thesis). MIT. v t e Artificial intelligence (AI) History timeline Glossary Companies Projects List of open-source AI software Concepts Automated reasoning Parameter Hyperparameter Loss functions Regression Bias–variance tradeoff Double descent Overfitting Clustering Gradient descent SGD Quasi-Newton method Conjugate gradient method Backpropagation Attention Convolution Normalization Batchnorm Activation Softmax Sigmoid Rectifier Gating Weight initialization Regularization Datasets Augmentation Prompt engineering Reinforcement learning Q-learning SARSA Imitation Policy gradient Diffusion Latent diffusion model Autoregression Adversary RAG Uncanny valley RLHF Self-supervised learning Reflection Recursive self-improvement Hallucination Word embedding Vibe coding Symbolic AI Neuro-symbolic AI Applications Automated theorem proving Machine learning In-context learning Artificial neural network Deep learning Language model Large NMT Reasoning Model Context Protocol Intelligent agent AI agent Artificial human companion Humanity's Last Exam Lethal autonomous weapons (LAWs) Generative AI Weak AI Hypothetical Artificial general intelligence (AGI) Artificial superintelligence (ASI) Agent2Agent protocol Implementations Audio–visual AlexNet WaveNet Human image synthesis HWR OCR Computer vision Speech synthesis 15.ai ElevenLabs Speech recognition Whisper Facial recognition AlphaFold Text-to-image models Aurora DALL-E Firefly Flux GPT Image Ideogram Imagen Midjourney Recraft Stable Diffusion Text-to-video models Dream Machine Runway Gen Hailuo AI Kling Sora Seedance Veo Music generation Riffusion Suno Udio World models Genie Oasis Text List of large language models Project Debater IBM Watson IBM Watsonx Decisional AlphaGo AlphaZero OpenAI Five Self-driving car MuZero Action selection AutoGPT Robot control Reasoning systems Deductive classifiers Expert systems Inference engines Knowledge-based systems Logic programs Procedural reasoning systems Semantic reasoners Rule-based systems Cognitive architectures ACT-R Soar CLARION LIDA OpenCog Knowledge bases ConceptNet Wikidata DBpedia YAGO People Alan Turing Warren Sturgis McCulloch Walter Pitts John von Neumann Christopher D. Manning Claude Shannon Shun'ichi Amari Kunihiko Fukushima Takeo Kanade Marvin Minsky John McCarthy Nathaniel Rochester Allen Newell Cliff Shaw Herbert A. Simon Oliver Selfridge Frank Rosenblatt Bernard Widrow Joseph Weizenbaum Seymour Papert Seppo Linnainmaa Paul Werbos Geoffrey Hinton John Hopfield Jürgen Schmidhuber Yann LeCun Yoshua Bengio Lotfi A. Zadeh Stephen Grossberg Alex Graves James Goodnight Andrew Ng Fei-Fei Li Alex Krizhevsky Ilya Sutskever Oriol Vinyals Quoc V. Le Ian Goodfellow Demis Hassabis David Silver Andrej Karpathy Ashish Vaswani Noam Shazeer Aidan Gomez John Schulman Mustafa Suleyman Jan Leike Daniel Kokotajlo François Chollet Neural network architectures Neural Turing machine Differentiable neural computer Transformer Vision transformer (ViT) Recurrent neural network (RNN) Long short-term memory (LSTM) Gated recurrent unit (GRU) Echo state network Multilayer perceptron (MLP) Convolutional neural network (CNN) Residual neural network (RNN) Highway network Mamba Autoencoder Variational autoencoder (VAE) Generative adversarial network (GAN) Graph neural network (GNN) Political AI Cold War AI safety (Alignment) AI takeover Elections Ethics of AI EU AI Act Nationalism Precautionary principle Regulation of AI US Virtual politician Propaganda Social and economic AI boom AI bubble AI data center AI effect AI literacy AI slop AI veganism AI winter Anthropomorphism Arms race Competition Environmental impact Explainable AI Generative engine optimization In architecture In education In fiction In healthcare Chatbot psychosis In marketing In video games In visual art Military applications AI warfare Workplace impact Category --- Adapted from the Wikipedia article [AI effect](https://en.wikipedia.org/wiki/AI_effect) by Wikipedia contributors ([contributor history](https://en.wikipedia.org/wiki/AI_effect?action=history)). 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