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

Anna Krylov’s contributions to theoretical and computational quantum chemistry are transformative. Her pioneering research focusses on the development of innovative electronic structure methodologies tailored for open-shell and electronically excited species, including metastable resonant states. These quantum chemical methods provide a profound understanding of electronically metastable phenomena, a field that extends directly from Gamow’s foundational work on metastability in nuclear physics. Krylov’s approach leverages advanced quantum mechanical frameworks to probe light–matter interactions with profound implications across spectroscopy, combustion chemistry, solar energy conversion, bioimaging technologies, and quantum information science. Her leadership in computational chemistry is further exemplified by her role as President of Q-Chem, Inc., a premier quantum chemistry software firm that supports the global scientific community.

In capturing the scientific essence of Gamow’s visionary legacy, Krylov highlights the intrinsic curiosity, conceptual clarity, and enthusiasm for idea dissemination exemplified by Gamow. Her research trajectory not only continues to elevate theoretical modeling of resonant electronic states but also enriches public engagement in science. Moreover, Krylov’s advocacy for academic freedom and scientific integrity underscores her commitment to maintaining an open intellectual environment, crucial in today’s politicized scientific landscape.

Mikhail Yampolsky’s achievements in the humanities and social sciences present an equally formidable intellectual engagement. As a cultural theorist and historian with extensive contributions in philosophy, literature, film criticism, and intellectual history, Yampolsky’s scholarship offers a unique interdisciplinary nexus. His work bridges disparate fields and reshapes the understanding of Russian culture and intellectual heritage through a critical and expansive analytical lens. Yampolsky’s scholarship spans over three decades, during which he has authored nearly five hundred articles and more than twenty monographs, enriching global discourses on culture and history. His retirement in 2024 marks the culmination of a distinguished career at New York University, a period marked by profound scholarly impact.

Yampolsky underscores the significance of the Gamow Award, not merely as an honor from within the humanities but as a symbol of recognition from the “hard sciences.” He articulates a vision that transcends disciplinary boundaries, advocating for a unity of scholarship that resists narrow specialization. This interdisciplinary ethos characterizes the broader aim of the Russian scientific diaspora, which seeks to integrate diverse scholarly traditions while confronting challenges posed by political and cultural constraints.

The George Gamow Award is more than a recognition of individual achievement; it symbolizes a commitment to scientific rigor, intellectual freedom, and diasporic solidarity. Both Krylov and Yampolsky recognize the dual mandate facing the Russian-speaking scientific diaspora: to preserve and promote valued scientific and cultural traditions, and to warn against the perils of politicized science, particularly given the current geopolitical tensions affecting the scientific community in Russia and its neighboring regions. These themes resonate deeply in their work and advocacy.

Anna Krylov’s academic background reflects a cosmopolitan and resilient trajectory. Born in Donetsk, Ukraine and educated in Moscow and Jerusalem, her scientific journey exemplifies the transnational nature of modern scientific inquiry. Her research into electronic metastability, using sophisticated quantum chemical models, pushes forward the fundamental understanding of transient species critical to numerous chemical and physical processes. Krylov’s engagement with spectroscopic techniques and quantum information science situates her research at the forefront of both theoretical innovation and practical application.

Meanwhile, Mikhail Yampolsky’s career traces a path through the pinnacles of Russian intellectual institutions and the broader global academic landscape. His expertise in art history, philosophy, and cultural criticism reflects a deeply contextualized understanding of Russian intellectual traditions. Yampolsky’s role in bridging Russian and Western scholarly worlds exemplifies the integrative function of diaspora intellectuals amid geopolitical fractures. His reflective insights on Russian cultural legacies and the importance of maintaining academic freedom resonate as a call to both vigilance and collaboration.

The 2024 award ceremony will be conducted during the 16th annual conference of RASA, scheduled for November 15–16, 2025, at the Jane Voorhees Zimmerli Art Museum, Rutgers University. The event will mark the 175th anniversary of Sofya Kovalevskaya, a groundbreaking mathematician who was the first woman to earn a doctorate in mathematics and hold a European professorship. This historical context aligns with the award’s emphasis on scientific excellence and overcoming societal barriers.

The George Gamow Award was inaugurated in 2015 to honor Gamow’s contributions as a physicist and a beloved scientific communicator. Gamow’s work provided the first quantitative theory of nuclear decay via quantum tunneling, and his introduction of the modern conceptual framework of metastable states continues to influence a wide array of scientific disciplines. His legacy embodies the spirit of curiosity, rigorous inquiry, and enthusiastic dissemination of scientific knowledge that the award seeks to perpetuate among Russian-speaking scientists worldwide.

In addition to the celebration of scientific and scholarly excellence, the award recognizes the critical role of the scientific diaspora in supporting colleagues displaced by political turmoil. Both Krylov and Yampolsky emphasize that organizations like RASA are vital for providing networks of support, fostering career continuity, and enhancing collaborative opportunities for scientists and scholars affected by displacement, particularly from Ukraine, Russia, and surrounding regions.

As a scientific and cultural landmark, the George Gamow Award highlights the importance of interdisciplinary collaboration and open intellectual exchange across geographic and disciplinary boundaries. The 2024 laureates exemplify these ideals, demonstrating how deep disciplinary expertise combined with a commitment to societal engagement can drive progress and protect the integrity of science and scholarship. This spirit of integration and resilience amidst adversity defines their recognition and sets a compelling precedent for future generations.

The award coincides with ongoing global challenges that stress scientific autonomy and academic freedom. Krylov’s vocal stance against the politicization of science aligns closely with the award’s ethos. Her involvement as a founding member of the Academic Freedom Alliance and her advocacy through widely read essays underscore the critical nature of safeguarding scientific inquiry from ideological interference. This dimension of her work echoes the deeper historical struggles faced by scientists like Gamow, who navigated political upheaval while advancing fundamental knowledge.

In contrasting yet complementary fashion, Yampolsky’s wide-ranging scholarship embodies the potential for reflection and critique inherent in the humanities. His efforts to reinterpret Russian intellectual history in international contexts reveal how cultural theories can inform and enrich scientific discourse. Together, the awardees embody a holistic vision of scientific and cultural excellence that transcends traditional disciplinary silos and national boundaries.

Subject of Research: Theoretical and Computational Chemistry; Cultural Theory and Russian Intellectual History
Article Title: Leading Minds in Science and Culture: The 2024 George Gamow Award Laureates – Anna Krylov and Mikhail Yampolsky
News Publication Date: Not specified; award ceremony scheduled for November 15–16, 2025
Web References: https://mediasvc.eurekalert.org/Api/v1/Multimedia/81fde728-793d-46c8-87dd-da72f71dc650/Rendition/low-res/Content/Public
Image Credits: Anna Krylov, Mikhail Yampolsky

Keywords

Scientific workforce, theoretical chemistry, computational chemistry, quantum metastability, interdisciplinary scholarship, cultural theory, Russian scientific diaspora, academic freedom, scientific advocacy, quantum information science, intellectual history, spectroscopy