arXiv subjects

Martin Fleming

Publications and source records attributed to Martin Fleming.

5 recordsLinked to original sources

arXiv2026

AI Adoption in S&P 500 Firms

The adoption of artificial intelligence (AI) by large enterprises is an important potential source of aggregate productivity improvement and labor market impact. We study AI adoption of S&P 500 firms over the period 2016 to 2025, estimating adoption at the enterprise level. While generative AI tools are useful for personal and professional applications, our focus is on the deep integration of AI in the business processes of large enterprises which are bellwethers for firm adoption more broadly. We develop a novel measure to assess deep AI adoption (and distinguish it from AI hype) that is based on SEC 10-K filings, where laws and regulations ``prohibit companies from making materially false or misleading statements." In 2025, 11% of S&P 500 enterprises had AI deeply integrated into their business processes, and a further 10% were using AI in the production of goods and delivery of services. AI adoption has more than quadrupled from 5% in 2022 with slowly accelerating adoption among non-technology firms but very aggressive adoption in the technology sector which accounts for two-thirds of deeply integrated enterprise adoption. Firm profitability shows a "J-curve" as firms move from no adoption to deep adoption, but we observe no differences in capex or productivity. Among technology firms, but not others, AI adoption is higher for firms with more employees and higher values of Tobin's q.

econ.GN↗

arXiv2026

Crashing Waves vs. Rising Tides: Findings on AI Automation from Thousands of Worker Evaluations of Labor Market Tasks

We characterize AI automation as a continuum between crashing waves, in which capabilities jump abruptly across narrow task sets, and rising tides, in which capabilities improve continuously and broadly. Using evidence from more than 6,000 text-based, LLM-addressable tasks derived from the U.S. Department of Labor's O*NET taxonomy and over 60,000 evaluations by experienced workers, we find little evidence of crashing waves (contrary to existing views). Instead, rising tides are the primary form of AI progress. AI performance is high and improving rapidly across many tasks. In 2024-Q2, models completed text-based tasks that take humans about 1.5 hours to complete with roughly 60% success, rising above 70% by 2025-Q3. If recent trends in AI capability growth persist, frontier LLMs will be able to complete most text-based tasks at minimally sufficient quality with 88%-97% success by 2030.

cs.AI↗

arXiv2026

Economics of Human and AI Collaboration: When is Partial Automation More Attractive than Full Automation?

This paper develops a unified framework for evaluating the optimal degree of task automation. Moving beyond binary automate-or-not assessments, we model automation intensity as a continuous choice in which firms minimize costs by selecting an AI accuracy level, from no automation through partial human-AI collaboration to full automation. On the supply side, we estimate an AI production function via scaling-law experiments linking performance to data, compute, and model size. Because AI systems exhibit predictable but diminishing returns to these inputs, the cost of higher accuracy is convex: good performance may be inexpensive, but near-perfect accuracy is disproportionately costly. Full automation is therefore often not cost-minimizing; partial automation, where firms retain human workers for residual tasks, frequently emerges as the equilibrium. On the demand side, we introduce an entropy-based measure of task complexity that maps model accuracy into a labor substitution ratio, quantifying human labor displacement at each accuracy level. We calibrate the framework with O*NET task data, a survey of 3,778 domain experts, and GPT-4o-derived task decompositions, implementing it in computer vision. Task complexity shapes substitution: low-complexity tasks see high substitution, while high-complexity tasks favor limited partial automation. Scale of deployment is a key determinant: AI-as-a-Service and AI agents spread fixed costs across users, sharply expanding economically viable tasks. At the firm level, cost-effective automation captures approximately 11% of computer-vision-exposed labor compensation; under economy-wide deployment, this share rises sharply. Since other AI systems exhibit similar scaling-law economics, our mechanisms extend beyond computer vision, reinforcing that partial automation is often the economically rational long-run outcome, not merely a transitional phase.

econ.GN↗

arXiv2026

How Much Progress Has There Been in NVIDIA Datacenter GPUs?

As the role of modern Graphics Processing Units (GPUs) becomes increasingly essential for several computing tasks, analyzing their past and current progress is paramount for determining future constraints on scientific research. This is particularly compelling in the Artificial Intelligence (AI) domain, where rapid technological advancements and fierce global competition have led the United States to recently implement export control regulations limiting international access to advanced AI chips. Consequently, this paper examines technical progress in NVIDIA datacenter GPUs from the mid-2000s through 2025. Our main results identify doubling times of 1.43 and 1.67 years for FP16 and FP32 dense operations, while FP64 doubling times range from 2.05 to 3.79 years. Off-chip memory size and bandwidth have grown at slower rates than computing performance, doubling every 3.29 to 3.41 years, whereas the release prices and power consumption roughly doubled every 5.03 and 15 years, respectively. Moreover, our cross-vendor comparison of the top-performing GPUs per year shows that NVIDIA's performance advantage is narrowing, but not enough to compel a major market shift. Finally, we quantify the potential implications of current U.S. export control regulations and the consequent performance gaps, which the recently proposed policy changes could shrink from 23.6X to 3.54X.

cs.AR↗

arXiv2020

Learning Occupational Task-Shares Dynamics for the Future of Work

The recent wave of AI and automation has been argued to differ from previous General Purpose Technologies (GPTs), in that it may lead to rapid change in occupations' underlying task requirements and persistent technological unemployment. In this paper, we apply a novel methodology of dynamic task shares to a large dataset of online job postings to explore how exactly occupational task demands have changed over the past decade of AI innovation, especially across high, mid and low wage occupations. Notably, big data and AI have risen significantly among high wage occupations since 2012 and 2016, respectively. We built an ARIMA model to predict future occupational task demands and showcase several relevant examples in Healthcare, Administration, and IT. Such task demands predictions across occupations will play a pivotal role in retraining the workforce of the future.

cs.CY↗