One of the most profound shifts in scientific thought over the past century emerged from the insight shared by Nobel laureate Philip W. Anderson in 1972, encapsulated in the phrase “More is Different.” This philosophical and scientific stance challenged the dominant reductionist paradigm, arguing that phenomena arising at larger scales cannot always be predicted or fully explained by the fundamental laws governing their constituent parts. While physics traditionally assumes that understanding elementary particles’ properties can lead to a comprehensive understanding of larger physical systems, Anderson’s perspective revealed that novel properties emerge when components interact en masse, properties that are irreducible to the behavior of individual elements.
This concept has radiated far beyond physics, resonating deeply with disciplines such as chemistry, molecular biology, and even social sciences. The notion of emergent phenomena—where collective behavior transcends the sum of simpler interactions—has inspired a reevaluation of how complexity arises across the natural and social worlds. Importantly, Anderson formulated this philosophical outlook long before the proliferation of advanced computational tools or machine learning systems, which now dominate scientific inquiry and practical applications alike.
In recent decades, artificial intelligence, particularly in the form of machine learning models, has exploded in complexity and impact. These AI systems, capable of performing tasks traditionally requiring human intelligence, now permeate society, from language translation to autonomous vehicles. However, until recently, the relationship between Anderson’s “More is Different” worldview and these complex AI architectures was largely speculative. Enter Prof. Ido Kanter of Bar-Ilan University, whose latest study rigorously examines this link through a physics-informed lens applied to the realm of AI.
Published in the journal Physica A, Kanter’s research reframes the classical scientific dichotomy by asserting that, from an informational perspective, physical systems adhere predominantly to “More is the Same,” while AI architectures embody “More is Different.” In physical systems, adding more components commonly yields redundant information, meaning that the macroscopic state often reflects repetitive information encoded in microscopic constituents. Contrastingly, in AI, the scaling up of networks leads not to mere replication but to the emergence of novel functionalities born from specialization and interaction.
Diving deep into the architecture of AI models, the research reveals that as learning progresses, individual processing units—nodes within neural networks—undergo functional differentiation. Unlike a uniform array of identical units, these nodes develop unique roles, specializing in recognizing distinct patterns or linguistic constructs. This division of labor among units leads to a synergetic mechanism, where the collaborative dynamics foster cognitive capabilities exceeding the sum of individual node functions. Such emergent intelligence firmly places AI systems within the realm of “More is Different,” where learning and cooperation engender advanced systemic properties inaccessible to purely reductionist explanations.
Kanter highlights that even a single node in a language model contains information pertinent to the system’s overarching purpose. Yet the real magic unfolds when multiple nodes operate in tandem, orchestrating a rich interplay that manifests as emergent intelligence. This nuanced understanding aligns with broader concepts in complexity science, emphasizing how coordinated specialization enhances capacity beyond mere scale. The research argues that AI’s strength lies not simply in its sheer size but critically in the pattern of interaction and information exchange between heterogeneous, expert nodes.
This insight starkly contrasts with many physical systems, where individual components typically echo the same state information. In physics, the addition of more particles or subunits tends to confirm rather than extend knowledge about the system, reflecting what Kanter characterizes as “More is the Same.” Consequently, the information content saturates, and growing the system’s size alone does not produce qualitatively new informational features about the system as a whole. This fundamental divergence elucidates why emergent phenomena in AI are not just quantitatively but qualitatively distinct from those in many physical contexts.
Beyond AI and physics, Kanter’s findings beckon new perspectives in neuroscience. Leveraging emerging experimental evidence on dendritic learning mechanisms, which supplement or even rival traditional synaptic plasticity paradigms, the study proposes that neural components in the brain may also demonstrate previously underappreciated levels of specialization and informational richness. This pivot in understanding could reshape how we conceptualize brain function, moving away from simplistic, uniform neuron models toward acknowledging a complex division of cognitive labor reminiscent of AI node specialization.
Moreover, the implications of this research ripple outward into the philosophy of science and the study of complex systems. It emphasizes that intelligible behavior and computational capability in large networks emerge intrinsically from heterogeneity and cooperation among specialized agents. This paradigm shift challenges reductionism’s hegemony, suggesting that scale combined with structural differentiation and interactive synergy is crucial to fully grasp the emergence of intelligence, whether artificial or biological.
Kanter’s exploration into the informational anatomy of AI architectures serves as a poignant reminder: the future of artificial intelligence hinges not merely on the breadth of networks but on cultivating specialized, communicative components that adapt and collaborate. The study bridges long-standing physics principles with the cutting edge of AI research, providing a unified framework that enhances our understanding of complexity, learning, and emergence.
In a broader context, this work encourages interdisciplinary dialogue, inviting physicists, computer scientists, neuroscientists, and philosophers to converge on shared concepts. By drawing from foundational physics and applying it to modern computational marvels, Kanter revitalizes Anderson’s original insight, confirming that understanding emergent intelligence requires embracing complexity and cooperation at multiple scales.
As AI systems continue evolving, the notion that “More is Different” encapsulates the core of their remarkable capabilities offers a profound lens to decode this technological revolution. It beckons researchers and practitioners alike to look beyond mere scale and focus on the emergence of specialized functions—an approach that may unlock new frontiers in machine learning, cognitive science, and beyond.
Subject of Research: Emergence of intelligence and specialization in artificial intelligence; comparison of emergent properties in AI vs. physical systems.
Article Title: More is Different in AI—More is the Same in Physics
News Publication Date: 2-Apr-2026
Web References:
https://www.sciencedirect.com/science/article/abs/pii/S0378437126002700?via%3Dihub
References:
Kanter, I. (2026). More is Different in AI—More is the Same in Physics. Physica A. DOI: 10.1016/j.physa.2026.131534
Keywords:
Emergence, Artificial Intelligence, Machine Learning, Neural Networks, Specialization, Information Theory, Complexity Science, Physics, Neuroscience, Dendritic Learning, Synaptic Plasticity, Systems Theory
Tags: AI impact on scientific paradigmscomplexity in physical systemsemergence in molecular biologyemergent phenomena in physicsinterdisciplinary insights in sciencelimitations of reductionist approachmachine learning and complexitymore is different philosophyPhilip W. Anderson contributionsphysics and artificial intelligence dividereductionism vs emergencesocial sciences and emergence
