A new critical analysis has cast doubt on the originality and utility of "assembly theory," a recent proposal that seeks to quantify an object's complexity based on the number of steps required to build it. Researchers from the University of Cambridge have demonstrated that the fundamental principles of this theory are mathematically equivalent to existing statistical data compression algorithms, such as those used in dictionary compression (e.g., Lempel-Ziv).

Assembly theory, which has gained some traction in fields like the origin of life and astrobiology, posits that objects with a higher "assembly number" are intrinsically more complex and, therefore, less likely to form randomly. However, the current study argues that this metric introduces no novel concepts nor provides a deeper understanding of complexity than what standard statistical tools already offer. The mathematical equivalence suggests that assembly theory might not be a fundamental theory of complexity, but rather a reformulation of known principles in information theory.

The authors of the analysis emphasize that while assembly theory may be useful as a heuristic or an intuitive way of thinking about complexity, it lacks the theoretical novelty attributed to it. The work suggests that researchers seeking to quantify the complexity of natural or artificial systems could obtain similar and more robust results using well-established data compression algorithms, which already possess a solid mathematical foundation and widespread application across various disciplines.