Google DeepMind Economists Release The Economics of Intelligence, Arguing Coordination Drives AI's Economic Impact
I joined @GoogleDeepMind to develop a research program on the economics of AGI. Today, together with Andrew Koh (@andrewjkoh) and Shane Legg (@ShaneLegg), we are incredibly excited to release The Economics of Intelligence, the first in a series of projects from our group on how AGI will change the economy. How do capability improvements transform the economy, and when? In this project, we define thresholds of economic capabilities, e.g., minimal AGI, based on how they transform the economy. We then track how the economy–market structure, employment, and wages–changes during the transition across capability regimes. We have seen exponential increases in what AI agents can do but the broader economy seems almost remarkably unchanged. Part of the reason is that the solo AI agent is the wrong economic object; economic transformation is an equilibrium outcome where organizations compete. Looking at how intelligence functions within organizations, transformation will depend on when two key capability thresholds are crossed: competence (gains on individual skills, e.g., coding, math) and coordination (gains on ability of agents to cooperate and work together). We show that what economic transformation looks like depends on whether AI becomes supercompetent, supercooperative, or both—the regime of artificial superintelligence (ASI). Tracking this transition shows why the real visible transformation may come from improvements in coordination—a capability most benchmarks don’t yet track. Past a certain threshold, AI capabilities can break the Coasian ceiling on firm size and lead to an economy that looks drastically different than the one we see today. Two main takeaways: 1. To understand the path of transformation, we should be tracking agentic coordination more closely. 2. The impact of AI will likely show up in wages before market structure or employment. We are really excited about this paper. Excited to hear people’s thoughts. Link:
Context
It offers a framework for why rapid AI progress has barely shown up in the economy, and suggests watching wages and agent coordination for the first real signs of change.
Alex Imas is an economist who recently joined Google DeepMind to lead research on the economics of AGI. Shane Legg is a DeepMind cofounder and its chief AGI scientist. The Coasian ceiling refers to economist Ronald Coase's idea that coordination costs limit how large firms can grow.
Deep dive
Alex Imas said on Thursday that he joined Google DeepMind to build a research program on the economics of AGI, and that with Andrew Koh and Shane Legg he is releasing The Economics of Intelligence, the first in a series of projects on how AGI will change the economy.
According to the post, the paper defines thresholds of economic capability, such as minimal AGI, by how they transform the economy, and tracks how market structure, employment and wages change across those regimes. Imas argues that AI agents have improved exponentially while the wider economy looks almost unchanged, partly because a single AI agent is the wrong unit of analysis. Transformation, he says, depends on two thresholds: competence on individual skills like coding and math, and coordination, meaning agents' ability to work together.
The paper distinguishes AI that is supercompetent, supercooperative or both, which it treats as superintelligence. Imas says the most visible change may come from better coordination, which most benchmarks do not track, and that past a point AI could break the Coasian ceiling on firm size.
His two takeaways are to track agent coordination more closely and to expect AI's impact to show up in wages before market structure or employment.
What to expect next
Watch for reactions from economists and whether benchmark makers start measuring multi agent coordination, as the paper urges. Later papers in the series may offer testable predictions. If wages move first, as the authors expect, labor data in heavily automated fields could become the earliest signal of transformation. Policymakers studying AI and jobs may also draw on its framework when planning for labor market changes.