Google DeepMind has quietly signaled a shift in priorities by recruiting a chief economist focused not on today’s AI markets, but on a world after artificial general intelligence. This is not a support role for policy or public relations. It is a strategic position embedded near the core leadership, suggesting internal confidence that AGI is no longer a distant abstraction but a scenario worth modeling seriously.
The job’s framing is striking. Rather than asking how AI will boost productivity, it asks how economies function when intelligence itself becomes abundant. Labor, capital, pricing, and growth assumptions all break under that premise. By formalizing this question inside the company, DeepMind is implicitly admitting that current economic models may fail once AGI crosses certain thresholds.
This move also hints at unease. If AGI arrives abruptly, the shock will not be technical but systemic. Entire categories of work could compress or vanish faster than institutions can react. An in-house economist is not there to predict GDP gains, but to explore instability, concentration of power, and the second-order effects that rarely appear in optimistic AI roadmaps.
What makes this development notable is its timing. While public messaging remains cautious, internal hiring tells a more candid story. DeepMind appears to be preparing for consequences, not just capabilities. When a leading AI lab starts planning for the economics of “after,” it suggests that “before” may be shorter than most policymakers, and the public, are ready to admit.
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