The economic alignment problem of artificial intelligence

TL;DR

The paper discusses how the economic context influences AI risks and proposes post-growth policies and reforms to align AI development with social and environmental sustainability.

Contribution

It reframes the AI alignment problem as an economic issue and offers novel policy and governance solutions rooted in post-growth theory.

Findings

Post-growth research offers policies to reduce AI risks.

Replacing optimisation with satisficing can mitigate risks.

Treating AI as a commons can improve governance.

Abstract

Artificial intelligence (AI) is advancing exponentially and is likely to have profound impacts on human wellbeing, social equity, and environmental sustainability. Here we argue that the "alignment problem" in AI research is also an economic alignment problem, as developing advanced AI within a growth-oriented economic system is likely to increase social, environmental, and existential risks. We show that post-growth research offers concepts and policies that could address the economic alignment problem and substantially reduce AI risks, such as by replacing optimisation with satisficing, using the Doughnut of social and planetary boundaries to guide development, and curbing systemic rebound with resource caps. We propose governance and business reforms that treat AI as a commons and prioritise tool-like autonomy-enhancing systems over agentic AI. Finally, we argue that the development of artificial general intelligence (AGI) requires new economic theories and models, for which post-growth scholarship provides a strong foundation.