Исследования

Saving for the Machines -- by Jesús Fernández-Villaverde

When machines can perform a growing share of tasks, growth depends on two margins: how the task structure affects the returns to accumulating machines, and how strongly capital accumulation responds to those returns. I argue that, in a standard task model, capital supply governs the size of the growth boom associated with automation, while the task structure sets the duration of this growth boom and the evolution of wages. I document how the same technology path that generates 11.7% annual growt

7 November 2026 · Source: NBER new working papers

When machines can perform a growing share of tasks, growth depends on two margins: how the task structure affects the returns to accumulating machines, and how strongly capital accumulation responds to those returns. I argue that, in a standard task model, capital supply governs the size of the growth boom associated with automation, while the task structure sets the duration of this growth boom and the evolution of wages. I document how the same technology path that generates 11.7% annual growth when households choose their saving rate optimally delivers only 2.2% when the saving rate is fixed at the calibrated U.S. 2025 level of 23%. Therefore, slow growth might reflect too little capital accumulation instead of a technological bottleneck. Also, the saving rate rises mainly because automation raises the capital share, not because households become thriftier. Long yields price the growth boom: the ten-year real yield jumps from 5.1% to 13.6% on impact. In my benchmark calibration, hand-to-mouth households lose the equivalent of two-fifths to two-thirds of their consumption, while keeping them on trend temporarily costs at most a fifth of GDP. Acknowledgements and Disclosures The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research. Citation and Citation Data Copy Citation Jesús Fernández-Villaverde, "Saving for the Machines," NBER Working Paper 35964 (2026), https://doi.org/10.3386/w35964. Copy to Clipboard Download Citation MARC RIS BibTeΧ Download Citation Data In addition to working papers, the NBER disseminates affiliates’ latest findings through a range of free periodicals—the NBER Reporter, the NBER Digest, the Bulletin on Health, the Bulletin on the Economics of Alzheimer's, and the Bulletin on Entrepreneurship—as well as research spotlights, lectures, and panel videos. Presenters: Melissa Dell & Ashesh Rambachan Presenter: Paul Goldsmith-Pinkham

Read at the source →

More in this topic

ИсследованияSame Facts, Different Remedy: Linguistic Profiling and Arbitrariness in LLM and System One DecisionsarXiv:2611.08995v1 Announce Type: new Abstract: Large language models are increasingly used to resolve consumer complaints, and the way complainants write varies with age, education and socioeconomic status (SES). We audit three remedy-decision systems using sociolinguistic rewrites of US consumer financial complaints that are instructed to preserve every fact: two generative LLMs that return a single label (GPT-4o-mini and GPT-5-mini) and a probabilistic "System One" model that returns a distriarXiv cs.CY (Computers and Society)
ИсследованияAI Exposure and the Changing Responsibilities in Job PostingsarXiv:2611.09870v1 Announce Type: new Abstract: The growing adoption of artificial intelligence (AI) in the workplace has raised questions about how employer demand for different work activities is changing. We examine how the prevalence and content of work activities in online job postings change in association with activity-level measures of AI exposure, derived from Copilot and Claude usage. To enable systematic comparisons, we develop a pipeline that extracts the responsibilities from job adarXiv cs.CY (Computers and Society)
ИсследованияSeeing Is Not Knowing: The Epistemic Limits of LLM-Based Mental Health Inference from Youth Social Media ConversationsarXiv:2611.08565v1 Announce Type: cross Abstract: Adolescents increasingly use social media for private conversations, sharing experiences and seeking support. These interactions generate digital traces, prompting efforts to infer mental health and psychosocial states. As large language models become more capable of reasoning about conversations and interpersonal dynamics, a fundamental question remains nonetheless: what can models actually "know" about an individual's psychological experience farXiv cs.CY (Computers and Society)
ИсследованияEntering a New Domain in the Era of Generative AI: An Autoethnography of Solo Game Development as a Bioinformatics EducatorarXiv:2611.09623v1 Announce Type: cross Abstract: The rapid improvement of generative AI (GenAI) not only increased the efficiency of practice but also empowered individuals with capabilities to explore new domains. However, identity formation in the wake of such empowerment remains understudied. We use an autoethnography to understand identity formation in the context of solo game development as a bioinformatics educator. With the goal of developing a create-your-own-adventure (CYOA) game, we carXiv cs.CY (Computers and Society)