United StatesArtificial intelligence (AI) is the most capital-intensive technology shift of the past two decades. It is also highly resource-intensive. Its implications are far-reaching and still evolving, creating both opportunities and risks for capital allocation.
This paper aims to help asset allocators looking for a disciplined way to translate booming AI capital expenditure into portfolio decisions consistent with environmental markets (investing in products and services that deliver environmental solutions or improve resource efficiency).1
In Part I of this paper, we outline how rising compute demand is reshaping pressure on energy and water resources, and how those pressures bring the AI build-out into the environmental markets frame.
In Part II, we detail how we classify efficiency enhancing solutions across five clusters of the AI value chain – from design and manufacturing equipment, through silicon and memory, to interconnect, thermal management, data centres and cloud services.
Efficient AI earns its place in our Environmental Markets taxonomy not because of sector allocation, but because specific products and services enable measurable efficiency gains. The alignment of environmental and investment cases does not extend across the whole AI supply chain, however; only a portion stands up to scrutiny.
1 Environmental markets are the parts of the global economy where companies generate revenue from products and services that address environmental challenges – ranging from clean energy and water systems to resource efficiency and sustainable food
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