Moravec's paradox
In plain wordsThe observation that what is hard for people, like chess or algebra, is often easy for computers, while what toddlers do effortlessly, such as seeing, walking and picking things up, is very hard for machines.
The observation that abilities humans find intellectually demanding — logic, algebra, board games, written tests — have proved relatively easy to mechanize, while abilities humans exercise without thought — perceptionperception, n.: How an AI system or robot turns raw sensor signals, such as camera pixels, microphone sound or laser scans, into an understanding of what is around it: objects, people, words and distances., walking, grasping, handling unfamiliar objects — have proved far harder to engineer and need much more computation. Hans Moravec's explanation was evolutionary: the machinery for perception and movement has been tuned over roughly a billion years and works below awareness, so its difficulty is invisible to us, whereas abstract reasoning is a young ability — by his estimate under 100,000 years old — that evolution has had little time to refine. The paradox is now routinely invoked to explain why language modelslarge language model, n.: A computer program trained on enormous amounts of text to predict the words that come next. At vast scale, that simple skill lets it chat, answer questions, translate, summarize and write code. pass professional examinations while general-purpose household robotsrobot, n.: A machine that senses its surroundings, decides what to do and physically acts, moving itself or handling objects, with some independence from a human operator. Factory arms, robot vacuums and self-driving cars are all robots. remain elusive, and why robot learningrobot learning, n.: Teaching robots skills from data and experience, such as watching demonstrations, practicing by trial and error or training in simulation, instead of programming every movement by hand. and physical AIphysical AI, n.: AI built to work in the physical world rather than on a screen: the intelligence inside robots, self-driving cars, drones and other machines that must sense real surroundings and move through them. lag behind text-based AI.
Our agent can file the tax return but not fold the laundry: Moravec's paradox in one sentence.
Origin
Articulated by Hans Moravec in Mind Children (1988), who wrote that computers could be made to play checkers or take intelligence tests at adult level far more easily than given a one-year-old's perception and mobility; Rodney Brooks, Marvin Minsky and others made similar points in the 1980s. Steven Pinker's The Language Instinct (1994) put it as 'the hard problems are easy and the easy problems are hard'.
Usage note
Critics such as Arvind Narayanan, in the essay 'Fact checking Moravec's paradox' (January 2026), argue that it was never tested empirically, mostly reflects which problems AI researchers chose to work on, and has little power to predict which tasks AI will find easy or hard. Often cited alongside, but distinct from, Polanyi's paradoxPolanyi's paradox, n.: The observation that we know how to do many things — recognize a face, ride a bike — that we cannot explain in words. That made such skills hard to program until machines learned them from examples., which concerns tacit knowledge.
Related
- roboticsrobotics, n.: The science and engineering of building robots and making them work: designing their bodies, sensors and motors, and writing the software that lets them see, plan and move safely.
- embodied AIembodied AI, n.: AI that has a body, usually a robot and sometimes a simulated character, and learns or acts by sensing and moving in a world rather than only processing text or images.
- physical AIphysical AI, n.: AI built to work in the physical world rather than on a screen: the intelligence inside robots, self-driving cars, drones and other machines that must sense real surroundings and move through them.
- Polanyi's paradoxPolanyi's paradox, n.: The observation that we know how to do many things — recognize a face, ride a bike — that we cannot explain in words. That made such skills hard to program until machines learned them from examples.
- jagged frontierjagged frontier, n.: The uneven edge of what AI can do: it may excel at some hard tasks yet fail at seemingly easier ones, so how hard a task looks is a poor guide to whether AI can handle it.
- manipulationmanipulation, n.: A robot's use of arms, hands or grippers to change the world around it: picking things up, moving, pushing, opening, assembling or using tools.
Sources
- Moravec, H. (1988). Mind Children: The Future of Robot and Human Intelligence. Harvard University Press.
- Pinker, S. (1994). The Language Instinct. William Morrow.
- Narayanan, A. (2026). Fact checking Moravec's paradox. AI as Normal Technology (newsletter), 29 January 2026. normaltech.ai/p/fact-checking-moravecs-paradox
Mentioned in
physical AIphysical AI, n.: AI built to work in the physical world rather than on a screen: the intelligence inside robots, self-driving cars, drones and other machines that must sense real surroundings and move through them., AI effectAI effect, n.: The habit of deciding that once a computer can do something — play chess, recognize speech, translate — it no longer counts as real intelligence, so the label keeps moving on to tasks machines cannot manage yet., commonsense reasoningcommonsense reasoning, n.: The everyday background knowledge and reasoning people take for granted — dropped glasses break, hungry people eat, nobody is in two places at once — which machines long struggled to match., embodied AIembodied AI, n.: AI that has a body, usually a robot and sometimes a simulated character, and learns or acts by sensing and moving in a world rather than only processing text or images., embodied cognitionembodied cognition, n.: The idea that thinking is not something the brain does on its own: the body, its senses and movements, and its surroundings play a real part in how we think and understand., jagged frontierjagged frontier, n.: The uneven edge of what AI can do: it may excel at some hard tasks yet fail at seemingly easier ones, so how hard a task looks is a poor guide to whether AI can handle it., jagged intelligencejagged intelligence, n.: The uneven pattern of AI abilities: today's systems can solve very hard problems yet fail at some a child could manage, so their skills form a spiky profile rather than a smooth one., Polanyi's paradoxPolanyi's paradox, n.: The observation that we know how to do many things — recognize a face, ride a bike — that we cannot explain in words. That made such skills hard to program until machines learned them from examples., Rodney BrooksRodney Brooks, prop. n.: An Australian-American roboticist who argued that intelligence grows out of acting in the world rather than abstract reasoning, and who co-founded the company behind the Roomba vacuum., Dreyfus's critique of AIDreyfus's critique of AI, n.: The philosopher Hubert Dreyfus's argument, from the 1960s on, that rule-based AI could never match human intelligence, because much human skill rests on bodily know-how and intuition rather than explicit rules., Hans MoravecHans Moravec, prop. n.: An Austrian-born roboticist who observed that skills humans find effortless, like seeing and moving, are the hardest for machines, and who forecast when computers would match the brain., Seymour PapertSeymour Papert, prop. n.: A South African-born mathematician who co-wrote 'Perceptrons', a critique of early neural networks, with Marvin Minsky, and invented the children's programming language Logo., Shakey the robotShakey the robot, prop. n.: A wheeled robot built at a California research institute between 1966 and 1972 that could look around, make a plan and carry it out: the first mobile robot to work out its own steps toward a goal.