In the latest EJIS conversation, European Journal of International Security (EJIS) editor Jason Ralph (University of Leeds) talks to Jeffrey Ding about his article 'Engines of power: Electricity, AI, & general-purpose, military transformations', which was co-authored with Allan Dafoe. The article uses evidence from electricity's impact on military affairs to support propositions about general-purpose military transformation and general-purpose technologies.
AI has been mooted as the new electricity, so Jeffrey discusses what can be learnt from this comparison of AI to electricity, and how AI will affect the future of warfare. He also talks about a new project looking at how AI will shape military accidents in the future.
Want to know more? You can read the full article at: https://doi.org/10.1017/eis.2023.1
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Major theories of military innovation focus on relatively narrow technological developments, such as nuclear weapons or aircraft carriers. Arguably the most profound military implications of technological change, however, come from more fundamental advances arising from ‘general-purpose technologies’ (GPTs), such as the steam engine, electricity, and the computer. Building from scholarship on GPTs and economic growth, we argue that the effects of GPTs on military effectiveness are broad, delayed, and shaped by indirect productivity spillovers. We label this impact pathway a ‘general-purpose military transformation’ (GMT). Contrary to studies that predict GPTs will rapidly diffuse to militaries around the world and narrow gaps in capabilities, we show that GMTs can reinforce existing balances if leading militaries have stronger linkages to a robust industrial base in the GPT than challengers. Evidence from electricity's impact on military affairs, covering the late nineteenth and early twentieth centuries, supports our propositions about GMTs. To probe the explanatory value of our theory and account for alternative interpretations, we compare findings from the electricity case to the military impacts of submarine technology, a non-GPT that emerged in the same period. Finally, we apply our findings to contemporary debates about artificial intelligence, which could plausibly cause a profound GMT.