AI hype
In plain wordsExaggerated claims about what AI can do or will soon do, made to attract money, attention or customers, which can mislead the public and policymakers.
Exaggerated, unfounded or misleading claims about the capabilities, novelty, imminence or effects of AI, made by companies, investors, researchers, journalists or critics. Common forms include presenting narrow benchmark results as general competence, demos as finished products, anthropomorphic language about understanding or intention, dated promises of artificial general intelligenceartificial general intelligence, n.: AI that could learn and do almost any mental task a capable adult can, rather than excelling at one narrow job. Experts disagree on the exact bar and on when, or whether, it will arrive., and inflated figures for adoption or returns. Hype can raise capital and attention but distorts investment and policy, sets up disappointment and diverts scrutiny from present harms; 'criti-hype' is critique that accepts and amplifies inflated premises about capability. What counts as hype is itself contested: some claims dismissed as hype have come true quickly, and the charge is also used rhetorically to wave away legitimate concerns. Cycles of hype and disappointment preceded the AI wintersAI winter, n.: A slump in which disappointment over AI's unmet promises leads governments and companies to pull back money and attention. Historians count two big ones: one in the 1970s and one starting in the late 1980s..
Strip out the AI hype and the product is a decent classifier with a chat window.
Origin
Hype, from early-20th-century American slang for deception or excessive promotion, has accompanied AI since the 1950s, and overpromising was blamed for the AI winters. 'AI hype' became a common phrase in the late 2010s; historian Lee Vinsel coined 'criti-hype' in 2021, and Arvind Narayanan and Sayash Kapoor's AI Snake Oil (2024) and Emily M. Bender and Alex Hanna's The AI Con (2025) systematized the critique.
Usage note
AI washingAI washing, n.: Falsely claiming that a product or company uses AI, or exaggerating how much it does, to look more advanced and attract customers or investors. is a specific form of hype in which products are falsely or loosely labeled as using AI.
Related
- hype cyclehype cycle, n.: A pattern in which excitement about a new technology rises to a peak, crashes into disappointment, then slowly recovers as the technology finds real uses.
- AI bubbleAI bubble, n.: The worry that investors are pouring far more money into AI companies, chips and data centers than AI will earn back soon, so that prices could crash as in the dot-com bust of 2000.
- AI washingAI washing, n.: Falsely claiming that a product or company uses AI, or exaggerating how much it does, to look more advanced and attract customers or investors.
- AI winterAI winter, n.: A slump in which disappointment over AI's unmet promises leads governments and companies to pull back money and attention. Historians count two big ones: one in the 1970s and one starting in the late 1980s.
- AI as normal technologyAI as normal technology, phr.: The view that AI, however important, will change society gradually, like electricity or the internet, through slow adoption by people and institutions, rather than as a sudden, uncontrollable superintelligence.
- stochastic parrotstochastic parrot, n.: A critical label for AI language systems, likening them to parrots that echo patterns in the text they were trained on, fluently but with no grasp of what the words mean.
- feel the AGIfeel the AGI, phr.: An AI-industry catchphrase meaning to grasp, emotionally as well as intellectually, that human-level AI may be arriving soon; it began as a chant inside OpenAI.
Sources
- Narayanan, A. & Kapoor, S. (2024). AI Snake Oil: What Artificial Intelligence Can Do, What It Can't, and How to Tell the Difference. Princeton University Press.
- Bender, E. M. & Hanna, A. (2025). The AI Con: How to Fight Big Tech's Hype and Create the Future We Want. Harper.
Mentioned in
AI bubbleAI bubble, n.: The worry that investors are pouring far more money into AI companies, chips and data centers than AI will earn back soon, so that prices could crash as in the dot-com bust of 2000., AI winterAI winter, n.: A slump in which disappointment over AI's unmet promises leads governments and companies to pull back money and attention. Historians count two big ones: one in the 1970s and one starting in the late 1980s., AI as normal technologyAI as normal technology, phr.: The view that AI, however important, will change society gradually, like electricity or the internet, through slow adoption by people and institutions, rather than as a sudden, uncontrollable superintelligence., AI washingAI washing, n.: Falsely claiming that a product or company uses AI, or exaggerating how much it does, to look more advanced and attract customers or investors., AI-poweredAI-powered, adj.: Described as using artificial intelligence to work. The label appears on everything from search engines to toothbrushes and often says little about how much AI is really involved., Emily M. BenderEmily M. Bender, prop. n.: An American linguist who argues that language models only mimic the form of language without understanding it, and who co-coined the phrase 'stochastic parrots'., Executive Order 14434Executive Order 14434, prop. n.: A September 2026 order by US President Donald Trump telling federal agencies to say 'super intelligence' (SI) instead of 'artificial intelligence' (AI) in official documents and communications., Gary MarcusGary Marcus, prop. n.: An American cognitive scientist and leading critic of deep learning, who argues that today's AI cannot reason reliably and needs to be combined with symbolic methods., hype cyclehype cycle, n.: A pattern in which excitement about a new technology rises to a peak, crashes into disappointment, then slowly recovers as the technology finds real uses., IBM WatsonIBM Watson, prop. n.: An IBM question-answering computer that beat two champions of the US quiz show Jeopardy! in 2011. IBM then sold business AI products under the Watson name, many of which disappointed., LudditeLuddite, n.: Originally one of the English textile workers who smashed machines in protest in the 1810s; now anyone who opposes new technology, often used as an insult against AI skeptics., productivity paradoxproductivity paradox, n.: The puzzle that a powerful new technology, like computers in the 1980s or AI today, can be everywhere without showing up as faster growth in how much workers produce., techno-solutionismtechno-solutionism, n.: The belief that technology, such as a clever app or algorithm, can fix complicated social problems on its own, overlooking their political, economic and human causes., workslopworkslop, n.: AI-generated work that looks polished but does not actually get the job done, quietly passing the real effort to whoever receives it, who must then puzzle it out or start over., Arvind NarayananArvind Narayanan, prop. n.: An Indian-American computer scientist at Princeton who studies AI's real-world effects and argues that AI is a 'normal technology' whose impact will unfold gradually., circular dealcircular deal, n.: A business arrangement in which an AI chip or cloud supplier invests in, or lends to, a customer that then spends the money buying the supplier's own products., cognitive computingcognitive computing, n.: A label, promoted heavily by IBM in the 2010s, for computer systems that learn, reason and interact with people in humanlike ways. It was largely a marketing term and has given way to 'AI'., Georgetown–IBM experimentGeorgetown–IBM experiment, prop. n.: A 1954 public demonstration in New York in which an IBM computer translated about sixty Russian sentences into English. It raised hopes for automatic translation that took decades to fulfill., Lighthill reportLighthill report, prop. n.: A 1973 review written for a British government research council that judged AI research to have fallen far short of its promises. It led to deep cuts in AI funding in the United Kingdom., safetywashingsafetywashing, n.: Making AI work sound as if it improves safety when it mostly just makes AI more capable — like 'greenwashing' for environmental claims., tokenmaxxingtokenmaxxing, n.: Using as much AI as possible — huge prompts, long sessions, many agents at once — mainly to rank high on workplace measures of how much AI people use.