A Universal Law of Energy Survival Governing Living Performance Across Biological and Engineered Systems

Living systems—spanning humans, animals, plants, fish, birds, insects, and microorganisms—exhibit large and persistent variability in growth, reproduction, productivity, resilience, and survival, even when exposed to broadly comparable environmental resources such as food, light, water, oxygen, and habitat. Decades of empirical research across physiology, ecology, agriculture, and medicine demonstrate that classical biological descriptors—metabolic rate, nutrient uptake, enzyme kinetics, hormonal regulation, and genetic potential—while individually well validated, remain fragmented and insufficient to quantitatively predict realized biological performance across species, environments, and time scales.

Concurrently, efficiency-based energy metrics routinely overestimate usable output in both biological and engineered systems. Empirical observations reveal that ecosystem-scale photosynthesis converts only ~1–3% of incident solar energy into net biomass, utility-scale photovoltaic systems deliver ~15–25% of incident energy as grid-exportable electricity, electric drivetrains provide ~60–85% of input energy as mechanical work, and large-scale computing infrastructures convert <1–2% of electrical input into effective information processing. These discrepancies persist despite high component-level efficiencies and decades of optimization, indicating a fundamental limitation not captured by classical efficiency formulations.

Here we introduce a unified, physically grounded life-performance framework that integrates systems biology with thermodynamic energy survival principles. The Life Competency–Ability–Efficiency–Skill–Expertness (Life-CAES) framework formalizes biological performance as a rate-limited, mass-dependent, and competency-modulated process, governed by explicitly measurable variables including organismal mass, uptake velocity, absorptive capacity, biochemical reaction competence (enzymes, cofactors, hormones, pigments, and tissue integrity), and characteristic time scales. This formulation treats living organisms as open, non-equilibrium thermodynamic systems whose functional output depends not only on resource availability, but on the efficiency with which absorbed matter and energy survive internal transport, regulation, and irreversible dissipation.

The biological framework is coupled to a universal Energy Survival Law, expressed through the survival factor

Ψ=AE/TE+ε

where absorbed energy (AE) represents energy successfully retained within the system boundary, TE denotes transport, leakage, and environmental dissipation losses, and ε captures irreducible entropy-generating losses mandated by the second law of thermodynamics. Unlike classical efficiency ratios, Ψ quantifies energy survivability rather than conversion idealization, and is strictly bounded between 0 and 1 for all real systems.

By coupling Ψ with an internal conversion competency term Cint, the unified performance law

Euseful=Ein⋅Ψ⋅Cint

demonstrates that both biological and engineered systems are fundamentally constrained by energy survival and conversion throughput, rather than by theoretical efficiency limits alone. Application of this formulation across representative biological systems (plants, animals, microorganisms) and engineered systems (photovoltaics, electric propulsion, data centers) shows strong quantitative agreement with observed field-scale performance without invoking ad hoc correction factors.

Across living systems, Life-CAES explains why organisms with similar intake of food, nutrients, light, or oxygen can exhibit orders-of-magnitude differences in growth, reproductive output, productivity, and resilience: absorbed resources must both survive thermodynamic degradation (Ψ) and be biochemically converted with sufficient competency (Cint) within finite time. The framework is dimensionally consistent, experimentally accessible, falsifiable, and thermodynamically constrained, providing a universal, cross-kingdom foundation for understanding biological performance and its physical limits.

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