Some Simple Economics of AGI

Christian Catalini; Xiang Hui; Jane Wu

econ.GNFeb 24, 2026

Some Simple Economics of AGI

Authors: Christian Catalini, Xiang Hui, Jane Wu

Organizations: MIT · WashU · UCLA

Abstract

For millennia, human cognition was the primary engine of progress on Earth. As AI decouples cognition from biology, the marginal cost of measurable execution falls to zero, absorbing any labor capturable by metrics--including creative, analytical, and innovative work. The binding constraint on growth is no longer intelligence but human verification bandwidth: the capacity to validate, audit, and underwrite responsibility when execution is abundant. We model the AGI transition as the collision of two racing cost curves: an exponentially decaying Cost to Automate and a biologically bottlenecked Cost to Verify. 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. Rents migrate to verification-grade ground truth, cryptographic provenance, and liability underwriting--the ability to insure outcomes rather than merely generate them. The current human-in-the-loop equilibrium is unstable: eroded from below as apprenticeship collapses (Missing Junior Loop) and from within as experts codify their obsolescence (Codifier's Curse). Unverified deployment becomes privately rational--a Trojan Horse externality. Unmanaged, these forces pull toward a Hollow Economy. Yet by scaling verification alongside agentic capabilities, the forces that threaten collapse become the catalyst for unbounded discovery and experimentation--an Augmented Economy. We derive a practical playbook for individuals, companies, investors, and policymakers. Today's defining challenge is not the race to deploy the most autonomous systems; it is the race to secure the foundations of their oversight. 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.

Figures & tables

Category	Variable	Description
Time & Tasks	$t\geq 0$	Continuous time ( $\dot{x}\equiv dx/dt$ )
	$i\in[0,1]$	Continuum of tasks
Time Allocation	$T_{m}$	Measurable work
	$T_{nm}$	Non-measurable work (steering and verification)
	$T_{sim}$	Synthetic practice, learning-by-doing
State Variables	$S_{nm}(t)\geq 0$	Human experience stock

Table 1

Figure 1: Static Regime Map with Dynamic Pressures. Each point represents a task $i$ mapped by its cost to automate ( $c_{A}(i)=i/K_{C}$ ) and cost to verify ( $c_{H}(i)=w\,t_{fb}(i)/S_{nm}$ ).

Figure 2: Missing Junior Loop, Experience Trap and Simulation Ladder. Nullcline diagram for the human experience stock $S_{nm}$ . • System Dynamics: The dashed curves (blue, orange, green) plot the $\dot{S}_{nm}=0$ frontiers implied by the reduced-form accumulation law $\dot{S}_{nm}=T_{m}(m_{A})+T_{sim}-dS_{nm}$ . For the plotted illustration, we use $T_{m}(m_{A})=T_{m0}(1-m_{A})$ , so the nullcline $S_{nm}^{*}(m_{A};T_{sim})=\big(T_{m0}(1-m_{A})+T_{sim}\big)/d$ slopes downward: as automation increases ( $m_{A}\uparrow$ ), measurable work $T_{m}$ contracts, lowering the steady-state experience stock unless simulation investment ( $T_{sim}$ ) rises. • Risk Threshold: The shaded gray region marks a budget-violating zone for high-stakes verification ( $c_{H}>B$ ); because $c_{H}(i)=w\,t_{fb}(i)/S_{nm}$ , verification costs increase as $S_{nm}$ falls. • The Trap (Solid Red): Under a Fixed Policy with low $T_{sim}$ , the trajectory inevitably crashes into the unverified gray zone. • The Ladder (Dashed Purple): Under an Adaptive Policy , governance steps up $T_{sim}$ at trigger points (vertical arrows), shifting the $\dot{S}_{nm}=0$ frontier upward (blue $\to$ orange $\to$ green) and allowing $S_{nm}$ to land safely above the threshold despite the decline in $T_{m}$ .

Figure 3: Alignment Maintenance Frontier under a Widening Measurability Gap. • Maintenance Frontiers (Dashed): The dashed curves plot the instantaneous $\dot{\tau}=0$ boundaries implied by $\dot{\tau}=T_{nm}(1-\tau)-\eta\,\Delta m_{+}\,\tau$ , yielding the steady state $\tau^{\star}(\Delta m)=\frac{T_{nm}}{T_{nm}+\eta\,\Delta m_{+}}$ (where $\Delta m_{+}\equiv\max\{\Delta m,0\}$ ). For a given steering/verification allocation $T_{nm}$ , points below the corresponding dashed frontier satisfy $\dot{\tau}>0$ (maintenance dominates) and points above satisfy $\dot{\tau}<0$ (drift dominates). • Alignment Trajectories (Solid): The solid curves track illustrative paths as compute scaling relentlessly widens the measurability gap over time ( $\dot{\Delta m}>0$ , capturing $K_{C}\uparrow\Rightarrow m_{A}\uparrow$ ). • Passive vs. Proactive Regimes: Under a No response policy with fixed low oversight ( $T_{nm}=0.1$ , solid red), the system eventually crosses the frontier into the drift region, causing alignment to decline steadily. Conversely, Always high oversight ( $T_{nm}=0.5$ , solid green) sustains higher alignment along the same gap-widening path. • Adaptive Response: The solid purple curve illustrates a dynamic intervention: once drift pressure becomes salient (at $\Delta m=0.40$ ), institutions execute a policy step-up to increase effective steering capacity ( $T_{nm}\uparrow$ ). This shift—achieved either by reallocating scarce expert time or through human augmentation (tooling that increases the oversight yield of a single human hour)—produces a kinked path that safely bends toward the high-oversight regime.

		Verification
		Easy	Hard
Automation	Hard	The Directors	The Meaning Makers
		Navigate Knightian uncertainty, transform	Neither process nor outcomes can be
		vague intent into operational constraints,	objectively measured, but depend
		direct/orchestrate agent swarms through	purely on social consensus.
		the search space, detect intent drift.	Strategy: monetize provenance,

Table 5

Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

Symbol	Definition
Time, Tasks, and Labor Allocation
$t\geq 0$	Continuous time ( $\dot{x}\equiv dx/dt$ ).
$i\in[0,1]$	Continuum of tasks, ordered by automation difficulty.
$T$	Total budget of human time (normalized to 1).
$T_{m},T_{nm}$	Time allocated to measurable work ( $T_{m}$ ) and non-measurable work ( $T_{nm}$ ).
$T_{e},T_{sim}$	Time allocated to theoretical education ( $T_{e}$ ) and synthetic practice ( $T_{sim}$ ).

Appendix

Table 6

Human Oversight of AI

Human oversight of AI studies how people supervise, verify, and control AI decisions, especially as systems become more autonomous and consequential.

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Jun 21, 2026

Grounded Scaling: Why Agentic AI Needs Deterministic Environments

Long-chain agent execution fails exponentially in environments designed for human tolerance: with per-step determinism

δ< 1

k

-step chain success degrades as

δ^k

. The AGI-to-ASI scaling debate (Genewein et al., 2026) has so far framed progress as a race between compute growth and a list of frictions (data wall, abstraction barrier, embodied bottleneck, multi-agent trust); we argue that environment determinism is a complementary binding axis cutting across all four, for the broad class of agentic AI tasks whose outcomes are verifiable economically, physically, or through multi-party settlement. Three formal results pin down the regime: a Determinism-Efficiency Bound on chain-task success, a Verifier-Goodharting Floor on flywheel ceilings under imperfect rewards, and a convergence condition for environment-side skill evolution. We operationalise the framework as a Supply Certainty Index (SCI) over five measurable properties, a five-level Determinism Maturity Model (DMM) as adoption ladder, and a falsifiable open-question programme (OQ1-OQ5) with explicit null results that would force retraction. The position is platform-agnostic. We engage three competing positions: sim-to-real sufficiency, alignment sufficiency, and AI-as-normal-technology.

Liang Ding, Xintong Wang

1Alibaba Group

AI Agent Reliability Long-Horizon Agent Tasks Agentic AI

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Aug 20, 2026

Growth Without Us: Machine Consumers, Corporate Circularity, and the Decoupling of GDP from Humanity after AGI

The standard objection to full automation is demand-side: if humans earn nothing, who buys the output? This confuses an accounting role with a biological species. We model a post-AGI economy in which corporations own populations of AI and robotic agents that are both producers and consumers of energy, compute, maintenance, and upgrades, traded among firms. Three results follow. (i) Demand closure: a closed inter-corporate economy with zero human consumption is not degenerate; it is the classical von Neumann expanding economy, whose growth rate is well defined, positive, and maximal precisely because all output is reinvested. (ii) Bottleneck removal: once economic agents are manufactured rather than reared, the binding constraint on growth shifts from human demography (a ~20-year, non-parallelizable reproduction technology capped at a few percent per year) to fabrication throughput and energy capture, permitting growth one to two orders of magnitude higher, with hyperbolic episodes when machine researchers raise their own productivity. (iii) Decoupling: output and human welfare separate completely, and the welfare relevance of arbitrarily large GDP collapses into one state variable: the human ownership share

ε_t

of the corporate network. A golden-rule decoupling theorem sharpens this. At maximal growth the interest rate equals the growth rate (r = g), so any positive human consumption rate out of wealth makes

ε_t

decay exponentially at exactly that rate. The human share survives only if the machine economy runs strictly inside its expansion frontier, or if law forces it to. We characterize three terminal regimes -- rentier post-scarcity, full circular decoupling, socialized ownership -- and the instruments that select among them. The conclusion is narrow: in a post-AGI economy, employment policy is obsolete and ownership policy is everything.

Sahil Sharma

Independent Researcher

AI and Labor

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May 4, 2026

Human-Provenance Verification should be Treated as Labor Infrastructure in AI-Saturated Markets

We argue that AI-saturated markets are likely to create Veblen-good premiums, which we term human-provenance premiums, for verified human presence, and hence AI governance should treat human-provenance verification as labor infrastructure. Generative and agentic AI systems lower the cost of many standardized cognitive, creative, and coordination tasks, weakening the scarcity premiums that have supported much middle-tier knowledge work. We argue that this pressure may produce an asymmetric barbell-shaped structure of value capture in advanced economies: high-volume synthetic production controlled by owners of AI infrastructure at one pole, and scarce, high-status human labor valued for verified human presence at the other. We advance three claims. First, AI compresses the value of standardized middle-tier labor by making good-enough synthetic substitutes scalable at low marginal cost, hollowing out the middle of the skill distribution currently categorized by knowledge work. Second, this compression reallocates demand for human labor toward work valued for its visible human character. We term this performative humanity and distinguish three forms of labor: relational presence, aesthetic provenance, and accountability. Third, as these premiums depend on credible verification, AI governance should treat human-provenance systems as labor infrastructure rather than as luxury authenticity labels. To evaluate hybrid human-AI work, we propose constitutive human presence as the relevant standard: human labor retains premium value when human judgment, attention, accountability, authorship, or relational participation is not incidental to the output but constitutive of what is being purchased.

Erin McGurk, David Khachaturov

Department of Land Economy · University of Cambridge · Department of Computer Science and Technology

AI Governance Human-AI Collaboration AI and Labor

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