--- title: "AI Premium · AI Fault Line" url: https://aifaultline.com/research/p/title-90a68c62c2cace4a/ description: "AI Fault Line is a neutral, evidence-cited field guide to why experts looking at the same developments disagree about AI's effect on productivity, jobs, inequality, regulation and the pace of change." --- [Research](https://aifaultline.com/research/) # AI Premium Nicola Borri, Yukun Liu, Aleh Tsyvinski · arXiv · 29 Jun 2026 [Read original source](https://arxiv.org/abs/2606.30583v2) Open access Where this sits in the debate - [Will AI actually boost productivity and growth?](https://aifaultline.com/debate/productivity/) · its finding: it depends ([why studies differ](https://aifaultline.com/debate/productivity/#why)) Direction of the finding as read from the abstract by an AI model ([AI-written](https://aifaultline.com/about/)). ## Key takeaway [AI-written](https://aifaultline.com/about/) Using 380 trillion tokens of AI consumption across more than 400 language models from OpenRouter data, the authors build a high-frequency AI factor and estimate firms' stock-return exposure to it. High-exposure firms earn a sizable return premium (64.1 basis points per week long-short), concentrated in frontier, paid and closed-model usage, and occupations heavy in interaction and communication show more positive AI exposure. ## Abstract Using 380 trillion tokens of realized AI consumption across more than four hundred large language models from the licensed proprietary OpenRouter dataset covering approximately 2 percent of current global monthly AI token consumption, we analyze how AI affects firms, markets, and workers. Leveraging the unprecedented size, scope and granularity data, we construct the AI Factor from growth in tokens, dollars, and users, estimate firm-level AI Betas from stock return comovement, and characterize the \emph{AI Premium}. First, we build a high-frequency AI factor and decompose it into salient components. Second, we show that firms whose returns covary more positively with the AI factor -- high AI beta firms -- earn higher subsequent returns, and the AI premium is large and heterogeneous. A value-weighted long-short strategy earns 64.1 basis points per week, and the premium is large for loadings on the intensive, frontier-oriented margin of AI consumption -- closed-source models, paying and seasoned users, and long prompts -- but not on casual or open-weight use. Third, the premium reaches beyond technology firms into consumer-facing and capital-heavy parts of the economy, but is absent in emerging markets, including China. Fourth, the AI exposure is more positive in nonroutine interactive work and more negative in analytical, scientific, and operations-control skills -- an occupation one standard deviation higher in interaction-and-communication content has 0.36-standard-deviation higher market-implied AI exposure. Additionally, we provide early evidence of the rise of the agentic economy. ## About this publication **Preprint.** A preprint — posted publicly before or without journal peer review; findings may still be revised. Published 2026 (4 months ago) Citations Not recorded for this source Citation counts come from OpenAlex; none are recorded for this source — absence of a count is not absence of citations. [Chart: published 2026] ## More on this topic Related by theme, not necessarily by method or conclusion. - [The Effects of Generative AI on High Skilled Work: Evidence from Three Field Experiments with Software Developers](https://aifaultline.com/research/p/doi-10-2139-ssrn-4945566/) SSRN · 2024 - [GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models](https://aifaultline.com/research/p/doi-10-48550-arxiv-2303-10130/) arXiv · 2023 - [Experimental evidence on the productivity effects of generative artificial intelligence](https://aifaultline.com/research/p/doi-10-1126-science-adh2586/) Science · 2023 - [The simple macroeconomics of AI](https://aifaultline.com/research/p/doi-10-1093-epolic-eiae042/) Economic Policy · 2024 Overview assembled automatically from collected metadata. [How this site is built](https://aifaultline.com/about/).