A Sociophysical Field Theory Analysis of Jensen Huang’s COMPUTEX 2025 Keynote

Abstract

This study adopts the methodologies of sociophysics and quantum field theory (QFT) to analyze the structural impact of artificial intelligence (AI) on social systems, as highlighted by NVIDIA CEO Jensen Huang in his COMPUTEX 2025 keynote. By constructing a Lagrangian model of the social field, we conceptualize AI as a “technological field” coupled with individual and organizational behaviors, examining how it drives changes in social order, spontaneous symmetry breaking, and institutional phase transitions. Our findings indicate that AI, beyond being a digital infrastructure, has emerged as a field source that governs the energy distribution of social interactions. This effect can be systematically described and simulated through field-theoretical frameworks. The research offers an interdisciplinary analytical lens for digital-era social theory and lays a foundation for empirical studies.

1. Introduction

In recent years, the rapid advancement of artificial intelligence (AI) has significantly reshaped institutional structures, economic systems, and individual behavioral patterns. At COMPUTEX 2025, NVIDIA CEO Jensen Huang emphasized the transformation of AI from a functional tool to a foundational infrastructure of modern civilization—sparking broad discussions in both technological and policy circles. This study, grounded in sociophysics and quantum field theory (QFT), analyzes how the technological transitions outlined in Huang’s keynote act as a field source that dynamically alters the evolution of social systems. By modeling AI as a technological field, we aim to uncover its deep influence on social structure, institutional norms, and behavior patterns, thereby offering a novel analytical perspective for the social sciences in the digital age.

2. Theoretical Framework: Mapping QFT onto Social Systems

2.1 Analogy Between Social Fields and Technological Fields

We conceptualize AI as a space-time distributed technological field ϕ(x, t), dynamically coupled with individual and organizational actors represented by the agent field ψ(x, t), analogous to fermion-scalar interactions in QFT. The local gradients ∂ϕ/∂x and ∂ϕ/∂t indicate the spatial diffusion rate and temporal influence intensity of AI technology, respectively. The potential function V(ϕ) reflects the resistance or stabilizing forces that AI introduces to existing social institutions and economic patterns. This field-theoretic perspective allows us to quantify AI’s societal impact as a dynamic field evolution, surpassing traditional technology adoption models.

2.2 Lagrangian Model of the Social Field

To describe the interaction between AI and the social system, we propose a simplified Lagrangian of the social field:

𝓛ₛₒ𝚌ᵢₐₗ = ψ̄ (i γ^μ ∂_μ − m) ψ + ½ (∂^μ ϕ)(∂_μ ϕ) − V(ϕ) + g ψ̄ ψ ϕ

Where:

• ψ: Represents individuals or organizations (e.g., persons, firms, governments) whose behaviors are influenced by the AI field.

• ϕ: The AI technological field, reflecting AI’s penetration and influence.

• g: Coupling constant indicating the receptiveness and willingness of actors to adopt AI, modulated by accessibility and societal acceptance.

• V(ϕ): Potential function describing AI’s structural impact on institutions and behavior, possibly incorporating stable states, resistance, or transformation costs.

• ∂^μ ϕ: The space-time gradient of the technological field, indicating the dynamic process of AI diffusion.

This Lagrangian decomposes the social dynamics into agent behavior (ψ), technology diffusion (ϕ), and their interaction (g ψ̄ ψ ϕ), providing a mathematical foundation for subsequent analysis.

3. Field-Theoretic Interpretation of the COMPUTEX 2025 Keynote

3.1 AI as a Generator of Social Order

Huang emphasized that AI has evolved into an infrastructure permeating economics, healthcare, and education. From a field-theoretic view, the AI field ϕ becomes a global source within the social system, no longer confined to isolated applications but reshaping order through multi-level coupling. For instance, NVIDIA’s DGX Spark platform decentralizes AI compute capabilities to edge devices, forming a “decentralized technological field.” This can be seen as ϕ being excited at multiple spatial locations, with strong coupling (high g) triggering local restructurings in social systems, such as enhanced AI adoption by SMEs or transformed labor patterns.

3.2 Heterogeneous Integration as a Resonance Mechanism

Huang introduced NVLink Fusion and the Grace Blackwell platform as examples of heterogeneous integration, where multiple computing architectures (GPU, CPU, DPU) collaborate. This can be modeled as several sub-fields (ϕ₁, ϕ₂, …) aligning in phase at boundary conditions, producing resonance effects that increase overall field energy density (∝ (∂^μ ϕ)(∂_μ ϕ)). This resonance not only stabilizes the technological field but also accelerates AI’s penetration into society, akin to cooperative effects induced by multi-mode coupling in physical systems.

3.3 Integration of Robotic and Language Fields

Huang’s mention of the Groot and Newton platforms—which integrate language models (LLMs) with robotic control—signals an expansion of the AI field ϕ from semantic space (information processing) to action space (physical operations). This cross-domain coupling can be modeled as interaction between the language field ϕ_lang and the action field ϕ_act via the term g′ ϕ_lang ϕ_act. This not only expands the dimensionality of the AI field but also reshapes social field interactions—e.g., through robotic diagnostics in healthcare or smart manufacturing—thereby transforming labor structures and value distribution.

4. Phase Transitions and Steady-State Shifts in Social Fields

4.1 Spontaneous Symmetry Breaking and Institutional Reconfiguration

When the AI field ϕ exceeds a critical intensity, the social system may undergo spontaneous symmetry breaking, where existing norms or institutional structures lose their symmetry, leading to a new order. For example, Huang’s observation of a shift from centralized cloud AI to adaptive edge AI implies a distributional shift in ϕ from high local concentrations to more uniform dispersion. This undermines traditional power structures (e.g., dominance by cloud giants) and induces a new vacuum expectation value ⟨ϕ⟩, such as a decentralized AI ecosystem.

4.2 Redefining the Vacuum Structure

As the AI field continues to act on the system, the social field may settle into a new meta-stable vacuum, wherein AI becomes institutionalized into a new equilibrium. Under such conditions, interaction energy among actors (e.g., economic transactions, information flows) follows new coupling rules. For instance, AI-driven smart contracts may redefine trust, reduce transaction costs, and alter capital movement. This process can be analyzed via renormalization theory, modeling how institutional evolution adapts to long-term AI influence.

5. Conclusion and Outlook

From a field-theoretic perspective, Jensen Huang’s COMPUTEX 2025 keynote reveals AI’s role as a technological field that fundamentally reconstructs society. AI nonlinearly alters social structures, exhibits phase transition characteristics, and propagates its effects through GPU-centered network externalities and training ecosystems. It reshapes behaviors, organizations, and industries across scales, while driving institutional adaptation. The field-theoretic framework not only helps forecast AI development trajectories but also guides proactive social transformation.