Global Population and Carrying Capacity in the Anthropocene: the Relative Growth Rate Insight

TL;DR

This paper analyzes global population dynamics during the Anthropocene using the Relative Growth Rate concept, revealing non-monotonic patterns, critical points, and implications for human and AI growth models, suggesting catastrophe can be avoided.

Contribution

It introduces a novel application of the Relative Growth Rate to global population data, highlighting non-monotonic growth patterns and critical points in the Anthropocene era.

Findings

Doomsday singularity at 2026 predicted for population growth

Reversal to a 'reversed criticality' pattern over the last six decades

Human and AI growth dynamics may follow similar models

Abstract

This report provides insights into global population dynamics since the beginning of the Anthropocene, focusing on empirical data and minimizing a priori the impact of model assumptions. It explores the Relative Growth Rate concept, introduced recently to global population studies by Lehman et al. [PNAS 118, e2024150118 (2021)] and subsequently extended to its analytical counterpart [PLoS ONE 20, eo323165 (2025)]. The analysis reveals a general non-monotonic growth pattern in the Anthropocene, emphasizing the uniqueness of the Industrial Revolution era. For the first 290 years, the Doomsday critical scaling provides a superior description for population changes, with a singularity at 2026. This is followed by the crossover to an exceptional 'reversed criticality' patterm, which has held over the last six decades, to the present day. The analysis suggests that the evolution of the human Real Intelligence (RI) population during the Innovations-driven Industrial Revolution times - a period of rapidly increasing connectivity and complexity - can serve as a model counterpart for the puzzling dynamics of Artificial Intelligence (AI) growth. The final conclusion is positive: the catastropic Doomsday singularity can be avoided due to the generic system constraints , both for RI and AI.