Does Computer Science Need Computers?
Author: Ben Brubaker
Publication:Quanta Magazine
Date: August 28, 2026 (Quanta Magazine)
The article examines the provocative claim often associated with Edsger Dijkstra that “computer science is no more about computers than astronomy is about telescopes.” Theoretical computer science certainly supports this view: many of its central questions concern abstract ideas such as algorithms, computability and computational complexity rather than physical machines. Alan Turing’s 1930s work, for example, developed the mathematical concept of the Turing machine before modern general-purpose electronic computers existed. Turing was originally trying to understand the limits of mathematical calculation, not design hardware. This theoretical tradition leads to two fundamental questions: What can be computed? and How can it be computed efficiently?
The second question gave rise to computational complexity theory, which studies why some problems admit fast algorithms while others seem inherently difficult. The crucial distinction is not simply whether a faster computer can solve a problem, but whether the mathematical structure of the problem allows an efficient algorithm at all. This perspective has produced ideas that reach far beyond conventional computing, including interactive proofs and zero-knowledge proofs, where someone can demonstrate that a statement is true without revealing the underlying information. Computational thinking has also become a way of studying physics, biology, evolution and even questions in quantum gravity.
Yet Brubaker ultimately argues that Dijkstra’s slogan is only partly correct. Although theoretical computer science can exist mathematically without physical computers, many of its deepest questions arose precisely because people were building and experimenting with real machines. Complexity theory became compelling when researchers confronted practical computational limitations during the 1960s. The relationship therefore runs both ways: theory creates technology, but technology also reveals questions that pure theorists might never have thought to ask. The better version of the telescope analogy may therefore be that astronomy is not about telescopes, but without telescopes much of astronomy would never have existed. As Ryan Williams summarizes the underlying idea, interesting practical problems often generate profound theoretical questions.
Key takeaways
Central idea: Computer science may not fundamentally be about computers, but without computers, much of modern computer science might never have been discovered.
ARTICLE [PDF] / ARTICLE
Author: Ben Brubaker
Publication:Quanta Magazine
Date: August 28, 2026 (Quanta Magazine)
The article examines the provocative claim often associated with Edsger Dijkstra that “computer science is no more about computers than astronomy is about telescopes.” Theoretical computer science certainly supports this view: many of its central questions concern abstract ideas such as algorithms, computability and computational complexity rather than physical machines. Alan Turing’s 1930s work, for example, developed the mathematical concept of the Turing machine before modern general-purpose electronic computers existed. Turing was originally trying to understand the limits of mathematical calculation, not design hardware. This theoretical tradition leads to two fundamental questions: What can be computed? and How can it be computed efficiently?
The second question gave rise to computational complexity theory, which studies why some problems admit fast algorithms while others seem inherently difficult. The crucial distinction is not simply whether a faster computer can solve a problem, but whether the mathematical structure of the problem allows an efficient algorithm at all. This perspective has produced ideas that reach far beyond conventional computing, including interactive proofs and zero-knowledge proofs, where someone can demonstrate that a statement is true without revealing the underlying information. Computational thinking has also become a way of studying physics, biology, evolution and even questions in quantum gravity.
Yet Brubaker ultimately argues that Dijkstra’s slogan is only partly correct. Although theoretical computer science can exist mathematically without physical computers, many of its deepest questions arose precisely because people were building and experimenting with real machines. Complexity theory became compelling when researchers confronted practical computational limitations during the 1960s. The relationship therefore runs both ways: theory creates technology, but technology also reveals questions that pure theorists might never have thought to ask. The better version of the telescope analogy may therefore be that astronomy is not about telescopes, but without telescopes much of astronomy would never have existed. As Ryan Williams summarizes the underlying idea, interesting practical problems often generate profound theoretical questions.
Key takeaways
- Computer science is broader than programming or computers: at its theoretical core it studies computation, algorithms and the limits of efficient problem solving.
- Turing machines show that computation is fundamentally a mathematical concept, independent of any particular hardware.
- Complexity theory asks why some problems are intrinsically harder than others, not merely whether we have sufficiently powerful computers.
- Physical computers nevertheless matter enormously: experimentation with real machines has repeatedly inspired entirely new theoretical questions.
- The article’s deeper message is that science and technology develop together. Pure theory can generate new technologies, while practical inventions can reveal previously invisible mathematical structures.
Central idea: Computer science may not fundamentally be about computers, but without computers, much of modern computer science might never have been discovered.
ARTICLE [PDF] / ARTICLE
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