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Alan Turing's biggest AI assumption may have been wrong

A leading computer scientist argues that AI is chasing an impossible dream, warning that machines may become dangerously intelligent without ever truly understanding the human world.

Date:<br>July 13, 2026<br>Source:<br>Taylor & Francis Group<br>Summary:<br>A new book claims AI has been built on a flawed assumption dating back to Alan Turing's famous 1950 paper. Peter J. Denning argues that the most important parts of human intelligence, including common sense, intuition, culture, and practical know-how, cannot be encoded into computers. He believes this makes true human-level AI impossible, regardless of how large language models become.<br>Share:

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Peter J. Denning warns that increasingly autonomous AI systems may evolve in ways that are difficult for humans to predict or manage. Credit: AI/ScienceDaily.com

Alan Turing's famous ideas about artificial intelligence may have sent AI research down the wrong path for the past 75 years, according to prominent computer scientist Peter J. Denning.

In his new book, Turing's Mistake: Escaping the Yoke of Unintelligent Machines, Denning argues that two foundational assumptions made by Turing in 1950 continue to shape AI research today. The first is that intelligence can exist independently of a physical body and therefore be recreated in computer software. The second is that a machine can demonstrate intelligence by successfully imitating a human in conversation, an idea that later became known as the Turing test.

"These two claims have shaped much of AI research and development," Denning writes. "My premise is that our acquiescence to these claims has led to the AI mess in which we find ourselves today."

Denning argues that pursuing artificial general intelligence (AGI), or machines with human level intelligence, is unlikely to succeed. Instead, he warns, the technologies society is building could introduce significant new risks.

The Tacit Knowledge Problem

At the heart of Denning's argument is the idea of tacit knowledge, the vast amount of human understanding that cannot easily be put into words or represented in a form that computers can process.

He says machine learning cannot capture five major categories of tacit knowledge: common sense, everyday interactions with people and the environment, emotions and perception, practical performance skills, and the social and historical knowledge embedded in culture.

Researchers have long attempted to organize common sense into databases. One of the best known efforts was Douglas Lenat's Cyc project, which began in the 1980s with the goal of creating an extensive collection of common sense facts. After four decades of work, the project contained roughly 25 million entries.

"Yet even this treasury could not add up to a background of common sense sufficient to make expert systems smart enough to be experts," Denning notes. "Cyc validated that much of the knowledge that makes people experts cannot be articulated as propositions."

Denning believes practical skills present an even greater challenge.

"Our performance skills in thousands of domains cannot be communicated to machines," he explains. "Whereas descriptions of skillful outcomes ('know what') can often be represented as bits and stored in a machine, we do not know how to encode the embodied knowledge for skillful performance ('know how')."

He points to accomplished musicians as an example.

"A virtuoso violinist can play beautiful music yet cannot describe to an acolyte how to produce it.

"Even if a robot could observe and imitate skilled humans, having no biological body, a robot cannot grasp how the musician feels when playing beautiful music or how an audience feels when hearing it."

Denning also includes intuition, gut feelings, imagination, and spontaneous creativity among the forms of tacit knowledge that remain beyond the reach of machines.

Why Human Knowledge Resists Encoding

Denning argues that all of these limitations stem from what he calls the "representation problem."

Computers can only perform calculations using data and instructions that have been encoded into physical forms they can recognize and process. Tacit knowledge, however, does not naturally fit into that framework.

"Behind every word is a deep well of tacit knowledge that gives it meaning," Denning says. "Words are but symbolic representations of meanings, not the meanings themselves. Commonly used Large Language Models, such as ChatGPT, Claude and Gemini only manipulate words, they cannot know or understand the meaning of what they are saying."

According to Denning, this creates a fundamental divide. Because scientists still cannot fully explain how tacit knowledge works in humans, they also cannot translate it into a form machines can use.

"How we host tacit...