Some Simple Economics of AGI

Abstract For three hundred thousand years, human cognition was the primary engine of progress on Earth. Today, as AI decouples cognition from biology, its capacity to recombine knowledge—and exhaustively map every combination within the known landscape—is driving the marginal cost of measurable execution toward zero. This is not merely the automation of routine work, but any labor that can be captured by metrics—including large swaths of what was once considered creative, analytical, and innovative. Yet most economic models treat AI as a labor substitute or a complement to exogenous human judgment, assuming machine output translates directly into realized value. We argue this paradigm is dangerously incomplete. When a scarce resource becomes abundant, the constraint does not vanish—it migrates to its nearest complement. In an economy where agents act with broad agency, the binding constraint on growth is no longer intelligence. It is human verification bandwidth: the scarce capacity to validate outcomes, audit behavior, and underwrite meaning and responsibility when execution is abundant. We model the transition toward AGI as the collision of two racing cost curves: an exponentially decaying Cost to Automate, driven by compute and accumulated knowledge, and a biologically bottlenecked Cost to Verify, bounded by human time and embodied experience. This structural asymmetry widens a Measurability Gap between what agents can execute and what humans can afford to verify. It also drives a shift from skill-biased to measurability-biased technical change and a radical bifurcation of economic value. Rents migrate to what remains scarce: verification-grade ground truth, cryptographic provenance, and liability underwriting—the ability to insure outcomes rather than merely generate them. Economic progress has always rested on an implicit compact: that the value claimed was the value produced. The Measurability Gap is the first force in history capable of systematically breaking that compact—not through crisis, but through the ordinary economics of cost minimization. When an AI agent generates output that looks correct, passes every test, yet silently violates unmeasured human intent, the economy accumulates systemic risk. The current "human-in-the-loop" equilibrium is unstable. It is eroded from below as apprenticeship pathways collapse (the Missing Junior Loop), shrinking human expertise precisely when oversight becomes most valuable. It is eroded from within as experts codify their own obsolescence (the Codifier's Curse), converting experience into training data.

As capabilities outpace oversight, deploying unverified systems becomes privately rational—introducing a "Trojan Horse" externality of misaligned output. Using AI to verify AI only manufactures false confidence as correlated blind spots propagate. Left unmanaged, these forces pull toward a Hollow Economy of explosive nominal output but decaying human agency. Yet this outcome is not inevitable. The answer is not a retreat into obsolescence, but a radical elevation of human purpose. By scaling verification infrastructure alongside agentic capabilities, the forces that threaten collapse become the catalyst for unbounded discovery, experimentation, and execution—powering an Augmented Economy. We derive a detailed operational playbook. For individuals: leverage synthetic practice to accelerate aptitude discovery, compress mastery, and execute at startup scale; because intelligence is now a commodity, shift human work toward defining intent, verifying outcomes, underwriting risk, or creating where value is non-measurable. For companies: invest in observability, verification-grade ground truth and network effects, and the scarce talent needed to underwrite risk—and reorganize around an "AI sandwich" topology: human intent, machine execution, human verification. For investors: pivot from funding commoditized execution to capitalizing on what is not yet measurable—deep tech and long-horizon R&D—alongside verification infrastructure and Liability-as-a-Service. For policymakers: price the externality through liability regimes, treat verification as a public good, and ensure safe scaling is not outcompeted by reckless deployment—reaping the broadest expansion of public-good provision in generations. The defining economic challenge of the agentic era is not the race to deploy the most autonomous systems; it is the race to secure the foundations of their oversight. Scale without verification is not a moat. It is an accumulating debt. Only by scaling our bandwidth for verification alongside our capacity for execution can we ensure that the intelligence we have summoned preserves the humanity that initiated it. Keywords: artificial intelligence, artificial general intelligence, AGI, Measurability-Biased Technical Change, Labor Substitution, AI Alignment, verification, cryptographic provenance, AI governance, economics of artificial intelligence