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Joules-to-GDP: How Much Economic Value Do AI Systems Produce per Unit of Energy?
cs.LGecon.GNq-fin.EC Amirreza Zeinali, Jon Saad-Falcon, Avanika Narayan et al. · Oct 22, 2026

Existing AI evaluations treat economic value and energy consumption separately, leaving a critical gap in understanding whether more capable systems actually deliver better returns per watt. This paper formalizes Net Value per Joule ($NVJ$) and Time Saving per Joule ($TSJ$), pairing judged professional output quality with end-to-end energy measurement across 220 real-world tasks. The work matters because it shows rankings of occupations and models shift dramatically once energy is accounted for, challenging the assumption that raw capability or dollar cost alone guide efficient deployment.

An AI system can produce more economically valuable work while delivering less value for the energy it consumes. We therefore ask: How much economic value does an AI system create per unit of energy consumed? We formalize economic value in two ways: (1) net dollar value, the output's dollar value minus AI-system execution cost, and (2) time saving, the expert's completion time minus the AI system's completion time. Dividing each by measured end-to-end energy yields Net Value per Joule (NVJ) and Time Saving per Joule (TSJ), reported in USD/MJ and hours/MJ. We study 220 public GDPval tasks across 44 occupations, serving five open-weight models locally and evaluating four accelerator types. First, the occupation groups where AI systems succeed most often, deliver the greatest dollar value, or save the most time do not necessarily provide the greatest economic return per joule. For example, among the tested tasks, engineering work delivers about 10% more net dollar value per task than software development. Yet software development delivers about 27% more net value per joule. Second, GLM 5.2 delivers the highest average net dollar value, at $72.27 per task using 0.430 MJ, equivalent in energy to about four hours of active laptop use. By contrast, Gemma 4 31B delivers 56% of that net dollar value using just 23% of the energy, achieving approximately 2.5 times the NVJ and 2.8 times the TSJ. Third, across four single-accelerator deployments, Qwen3.6-27B's NVJ varies by a factor of 4.32 and TSJ by 9.27, with B200 leading both. Additionally, one and eight H100s deliver similar net dollar value, but eight reduce completion time by 22% at 3.24 times the energy. Our framework makes output value and expert time saving under an energy budget explicit objectives for improving models, accelerators, and software, while offering a new basis for assessing AI's potential economic effect on occupational work.
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The Delegation Divide: An Open Index of AI-Agent Delegability across Occupations and Countries
econ.GNcs.AIcs.CY Cristian Espinal Maya (Universidad Nacional de Colombia), Santiago Jim\'enez Londo\~no (Universidad EAFIT) · Oct 22, 2026

Most studies measure how much AI can speed up a task—exposure—rather than whether a worker can actually hand it off. This paper introduces the Agent Delegability Index (ADI), which rates all 18,796 O*NET tasks on a frozen, preregistered four-level delegation ladder, then maps them to ISCO-08 occupations and weights them by employment across 146 countries. By distinguishing partial from full delegation and tagging physical, accountability, trust, and access barriers, the authors reveal a steep income gradient in who can delegate: high-income countries have roughly twice the delegable employment share of low-income ones, driven mainly by the composition of work.

35.2% of task time in world employment is at least partly delegable to AI agents in 2026, and only 2.5% can be handed over in full. Existing measures score exposure to generative AI, mostly for the United States. We build the Agent Delegability Index (ADI): two language models rate all 18,796 O*NET 30.2 task statements on a frozen, preregistered rubric of delegation levels and barriers; tasks are mapped to 433 ISCO-08 unit groups and weighted by ILOSTAT employment in 146 countries. Four findings. First, delegable employment rises with income, from 22.5% in low-income to 48.7% in high-income countries, a slope of 7.23 percentage points per log point of GDP per capita. This delegation divide is compositional: the physical barrier dominates the jobs poorer countries employ most. Second, at the occupation level ADI tracks exposure closely (r = 0.93 with Eloundou et al. and 0.89 with Felten et al.), above the preregistered bound of 0.80; ADI adds delegation levels and barriers on top of exposure, for the world, not a new ranking of occupations. Third, full delegation is rare and peaks for tasks that take a human about twelve minutes (11.8 minutes). Fourth, the preregistered adoption gap is not supported: across 101 countries, delegability and observed AI usage correlate at r = 0.86, and the gap between them is not wider in middle-income countries (p = 0.27). Model agreement is kappa = 0.77; a blind human rating of 300 tasks agrees with the model consensus at kappa = 0.85, and an agentic panel with web search at kappa = 0.87. We release the index, the atlas, the prompts and an MCP server at https://github.com/Cespial/delegation-divide.
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AI Token Futures Market: Commoditization of Compute and Derivatives Contract Design
cs.AIecon.GNq-fin.EC Yicai Xing · Mar 23, 2026

This paper proposes that AI inference tokens are evolving into a standardized commodity like electricity, and designs a complete futures market framework including the "Standard Inference Token" (SIT) contract, settlement mechanisms, and margin systems. The core motivation is hedging compute cost risk for application-layer enterprises as inference displaces training as the dominant AI cost.

As large language models (LLMs) and vision-language-action models (VLAs) become widely deployed, the tokens consumed by AI inference are evolving into a new type of commodity. This paper systematically analyzes the commodity attributes of tokens, arguing for their transition from intelligent service outputs to compute infrastructure raw materials, and draws comparisons with established commodities such as electricity, carbon emission allowances, and bandwidth. Building on the historical experience of electricity futures markets and the theory of commodity financialization, we propose a complete design for standardized token futures contracts, including the definition of a Standard Inference Token (SIT), contract specifications, settlement mechanisms, margin systems, and market-maker regimes. By constructing a mean-reverting jump-diffusion stochastic process model and conducting Monte Carlo simulations, we evaluate the hedging efficiency of the proposed futures contracts for application-layer enterprises. Simulation results show that, under an application-layer demand explosion scenario, token futures can reduce enterprise compute cost volatility by 62%-78%. We also explore the feasibility of GPU compute futures and discuss the regulatory framework for token futures markets, providing a theoretical foundation and practical roadmap for the financialization of compute resources.
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Behavioural feasible set: Value alignment constraints on AI decision support
cs.AIecon.GNq-fin.EC Taejin Park · Mar 22, 2026

Organizations deploying commercial AI systems inherit vendor-imposed value constraints that limit which recommendations the system can produce. This paper formalizes these boundaries as a "behavioural feasible set" and demonstrates through controlled experiments that alignment training compresses this set, making AI systems structurally unable to endorse certain legitimate organizational actions even under strong contextual pressure. The work reframes AI governance from a capability question to a constraint diagnosis problem, showing that vendor selection partially determines which trade-offs remain negotiable for adopting firms.

When organisations adopt commercial AI systems for decision support, they inherit value judgements embedded by vendors that are neither transparent nor renegotiable. The governance puzzle is not whether AI can support decisions but which recommendations the system can actually produce given how its vendor has configured it. I formalise this as a behavioural feasible set, the range of recommendations reachable under vendor-imposed alignment constraints, and characterise diagnostic thresholds for when organisational requirements exceed the system's flexibility. In scenario-based experiments using binary decision scenarios and multi-stakeholder ranking tasks, I show that alignment materially compresses this set. Comparing pre- and post-alignment variants of an open-weight model isolates the mechanism: alignment makes the system substantially less able to shift its recommendation even under legitimate contextual pressure. Leading commercial models exhibit comparable or greater rigidity. In multi-stakeholder tasks, alignment shifts implied stakeholder priorities rather than neutralising them, meaning organisations adopt embedded value orientations set upstream by the vendor. Organisations thus face a governance problem that better prompting cannot resolve: selecting a vendor partially determines which trade-offs remain negotiable and which stakeholder priorities are structurally embedded.
AI reviewed
arXivPDF