Blog · Review Essay · Last reviewed June 15, 2026

What Computers Still Can't Do and the Background of Intelligence

Hubert Dreyfus's What Computers Still Can't Do: A Critique of Artificial Reason is not useful because it settled the AI question. It did not. It is useful because it keeps returning attention to the parts of intelligence that become invisible when cognition is imagined as symbol manipulation, prediction, prompt response, or workflow automation: body, skill, situation, care, background, and the practical world that makes some possibilities matter more than others.

The Book

What Computers Still Can't Do was published by MIT Press in 1992 as a revised version of Dreyfus's earlier What Computers Can't Do. MIT Press lists the paperback as 408 pages, published October 30, 1992, with ISBN 9780262540674. Internet Archive's catalog record identifies the book as a 1992 MIT Press title on artificial intelligence, revised from the 1979 edition, with bibliographical references and an index.

The book sits inside a longer argument. RAND's record for Dreyfus's 1965 paper Alchemy and Artificial Intelligence describes it as an examination of the difficulty of simulating cognitive processes on computers, focused on forms of human information processing that resist translation into digital computer language. Berkeley's obituary for Dreyfus describes him as a scholar of Heidegger and European philosophy who taught at UC Berkeley for nearly fifty years and challenged expectations for artificial intelligence as early as the 1960s.

That background matters because Dreyfus was not merely making a technical forecast. He was attacking a picture of mind. Early AI often treated intelligence as the manipulation of explicit symbols according to formal rules. Dreyfus argued that human intelligence depends on background know-how that is not first represented as detached facts. We cope with situations before we describe them. We notice relevance before we search all possibilities. We learn practices through bodies, tools, environments, correction, and social life.

Artificial Reason

The title can mislead if it is read as a scoreboard. Modern systems can now do many things that would have looked startling to earlier AI critics: translate, summarize, draft code, recognize images, play strategic games, operate in multimodal interfaces, and produce fluent language at scale. The question is not whether computers can perform impressive cognitive tasks. They plainly can.

Dreyfus's stronger question is what kind of intelligence is being modeled when a system succeeds. Symbolic AI treated the world as if it could be represented in well-formed facts and rules, then searched or reasoned over those representations. The problem was not only scale. It was relevance. In ordinary life, people do not examine every possible fact and rule. They inhabit a situation in which some things show up as important, urgent, boring, dangerous, polite, plausible, or absurd.

That is why the book belongs beside Computer Power and Human Reason, Understanding Computers and Cognition, The AI Mirror, and The Experience Machine. It is a book about the gap between operating on representations and being answerable to a world.

The Background Problem

Dreyfus's most useful concept is the background. A person does not enter a task as a blank problem solver facing a database of possible facts. A person enters with bodily habits, learned equipment, social expectations, purposes, emotions, memories, institutional roles, and a sense of what normally matters. Much of that cannot be converted into a short rule without being changed.

This is not mystical. It is practical. A nurse, driver, teacher, carpenter, moderator, lawyer, parent, organizer, editor, or programmer often knows that something is off before they can state the rule that was violated. Expertise is not just more facts. It is a better grip on the situation. It includes timing, salience, tact, caution, and the ability to revise the frame when the obvious interpretation fails.

AI systems force a governance question around that background. If a model drafts the medical note, where did the clinician's situated judgment enter? If an answer engine compresses a dispute into a confident paragraph, where did uncertainty remain visible? If an agent chooses the next step in a workflow, who defined what counts as relevant? If a risk score reorganizes public service delivery, whose background knowledge was stripped away as noise?

The danger is not that machines lack souls. The danger is that institutions may treat a machine's output as if it had already done the background work. A generated answer can look complete while depending on missing context. A dashboard can look objective while excluding local knowledge. A workflow can look efficient because it has moved ambiguity onto the person with the least power to object.

After Symbolic AI

The book's 1992 edition already responded to a changing AI field, including connectionism and neural networks. A 2007 Dreyfus article in Artificial Intelligence revisited "Heideggerian AI" after robotics, embodied approaches, and other attempts to move beyond old symbolic representation. A 2024 AAAI Spring Symposium paper revisits Dreyfus again in light of deep learning, large language models, hybrid AI, hallucination, common sense, and the continuing question of whether systems can move from performance to human-like understanding.

That history prevents two lazy readings. One says Dreyfus was simply right because AI still hallucinates, lacks common sense, and depends on human training data. The other says he was simply wrong because neural networks and LLMs blew past old capability limits. Both flatten the problem.

Modern AI weakens some of Dreyfus's old claims and strengthens others. It weakens the idea that explicit rules are the only route to broad machine performance. Pattern-learning systems can produce astonishing results without hand-coded common-sense rules. But those systems also make his background problem more urgent. They can simulate situated speech without sharing the situation. They can generate plausible reasons without being practically committed to the consequences. They can appear context-aware while depending on an interface, data pipeline, retrieval layer, prompt, evaluator, or user to supply the missing world.

That is why LLMs feel so uncanny. They often do not fail like old brittle symbolic systems. They fail by making the wrong kind of sense. They give an answer that fits the conversational surface while missing the practical, legal, emotional, institutional, or bodily situation in which the answer will be used.

The Institutional Reading

Read in 2026, the book is less a metaphysical verdict on machine minds than an audit tool for delegated judgment. The practical question is not "Can this system think?" It is "What background has been removed from the task, and who is expected to repair the loss?"

In a private notebook, a model's missing background may be a manageable inconvenience. In a school, hospital, court, welfare office, newsroom, workplace, police department, or military system, missing background becomes institutional risk. A model summary can become the record. A recommendation can become the default. A confidence score can become a supervisor's reason. A generated explanation can become the public account of a decision that no person fully understands.

This is where Dreyfus connects to Tools for Thought. A good cognitive tool expands the user's contact with the world: more sources, more alternatives, more inspectable uncertainty, more situated correction, more ability to revise. A bad cognitive tool narrows the world while making the narrowed version feel fluent. It replaces friction with closure.

The same test applies to agents. An AI agent does not only answer. It routes attention, selects tools, writes to systems, triggers workflows, remembers context, and produces an action trail. That makes the background problem operational. If the agent does not know what matters, someone else must encode, constrain, monitor, or correct it. If no one can do that, the interface is not augmenting judgment. It is laundering underspecified judgment through automation.

Where the Book Needs Friction

What Computers Still Can't Do is brilliant and exasperating. Its polemical force helped make it famous, but the force can overrun the distinctions a reader needs now. Dreyfus was strongest when criticizing simplified pictures of mind and weakest when the word "can't" made a moving technical frontier sound fixed.

The book also risks romanticizing human cognition. Humans have background understanding, but we also rationalize, stereotype, forget, conform, overfit, hallucinate socially, and turn institutional habit into false common sense. A human decision is not automatically richer because it is embodied. A machine decision is not automatically empty because it is computational. The question is the arrangement: what evidence, feedback, responsibility, appeal, skill, and care surround the decision?

Dreyfus does not give an AI governance program. He gives a philosophical warning. Readers still need other books for procurement, algorithmic accountability, race and gender bias, data labor, surveillance, model evaluation, safety cases, law, and platform power. The value of the warning is that it keeps those policy questions anchored in a deeper one: what kind of world must be assumed before the system can act?

What This Changes

The book changes the evaluation question. Do not ask only whether the system can produce the right answer in a benchmark setting. Ask what the system needs the world to become so that its answer counts as right.

For AI products, the test is concrete. Does the interface preserve uncertainty? Does it expose sources and alternatives? Does it let users bring local knowledge back into the loop? Does it support correction after action, not just before deployment? Can a user refuse the generated frame? Does the workflow teach skill, or does it make the user dependent on fluent prompts and hidden defaults?

For institutions, the test is sharper. Never let a model's fluency stand in for situated responsibility. If a decision affects rights, care, work, money, reputation, safety, or public memory, the institution must know where background judgment lives. It must be inspectable, contestable, and connected to people with authority to change the process.

Dreyfus's old critique survives because it is not finally about what computers can output. It is about the world behind the output. Intelligence is not only a result on a screen. It is a relation among bodies, tools, purposes, histories, institutions, and consequences. Any AI system that enters that relation should be judged by how honestly it preserves the parts it cannot contain.

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