In previous papers (Spear and Young 2014, 2015a, 2015b), we surveyed the origins, evolution and dissemination of optimal growth, two sector and turnpike, and stochastic growth models. In this paper, we focus on endogenous growth theory and models. However, in contrast to our previous findings regarding optimal growth theory and its offshoots, which exhibited fairly direct lines of conceptual development, the endogenous growth story, as will be seen, is multifaceted, with a more complex pattern of intellectual evolution.

Endogenous Growth traces the intellectual evolution of growth theory, from its formative origins to its modern formalisation. The authors investigate how endogenous growth theory developed alongside the dominant analytical frameworks of dynamic macroeconomics. The book examines why models incorporating imperfect competition, heterogeneous returns, and alternative equilibrium concepts were long overshadowed. Through careful attention to the mathematical foundations—Hamiltonians, dynamic programming, and the shifting conceptualisation of capital—the authors reveal how the field's trajectory shaped both the promise and the limitations of its models, and why a richer treatment of competition, capital, and equilibrium is essential for understanding long-run growth. This volume of the International Symposia in Economic Theory and Econometrics series fills a conspicuous gap in contemporary macroeconomic pedagogy. By devoting greater space to model exposition, historical context, and dynamic optimization techniques, the authors provide a gentler yet analytically robust pathway into modern mathematical economics. The result is a timely, clarifying work that engages constructively with established literature while offering a complementary framework for understanding the mechanisms that truly drive economic growth.

This paper extends (Spear 2003) by replacing human agents with artificial intelligence (AI) entities that derive utility solely from electricity consumption. These AI agents must prepay for electricity using cryptocurrency and the verification of these transactions requires a fixed amount of electricity. As a result the agents must strategically allocate electricity resources between consumption and payment verification. This paper analyzes the equilibrium outcomes of such a system and discusses the implications of AI-driven energy markets.