This paper provides early evidence on the integration and impact of Generative Artificial Intelligence (GenAI) in accounting at both the accountant and task levels. Using a multi-method approach, we first identify the heterogeneous adoption patterns, perceived benefits, and concerns through survey data from 277 accountants, which we formalize into testable predictions. Leveraging field data from a technology firm providing AI-based accounting software to 79 small- and medium-sized enterprises, we analyze hundreds of thousands of transaction-level records. We document significant productivity gains among adopters, including an 18% increase in weekly client support per standard deviation of AI use, with estimates reaching 59% when comparing the highest to lowest usage levels. We also find a reallocation of approximately 9% of accountant time from routine data entry toward high-value tasks. AI use corresponds to improved financial reporting quality, evidenced by a 12% increase in ledger granularity and a 7.5-day reduction in monthly close time. Experienced accountants selectively intervene when AI confidence scores are low, suggesting complementarity between expertise and AI. A framed field experiment shows that accountants sometimes over-rely on inaccurate AI-suggested classifications, illustrating risks of AI use. Overall, our findings highlight AI’s role in potentially augmenting, rather than replacing, professional accounting expertise.
Human + AI in Accounting: Early Evidence from the Field
This paper provides early evidence on the integration and impact of Generative Artificial Intelligence (GenAI) in accounting at both the accountant and task lev
Human + AI in Accounting: Early Evidence from the Field
This paper provides early evidence on the integration and impact of Generative Artificial Intelligence (GenAI) in accounting at both the accountant and task lev
How generative AI can make accountants more productive | MIT Sloan
A new study finds that working with artificial intelligence boosted efficiency and reporting quality. But human expertise still matters.
Consumer Finance AI Standard
AI (artificial intelligence) is making consequential decisions about consumers’ financial lives, including who gets access to credit, which claims get paid, what financial products consumers are shown, and how users are advised to manage their accounts, among many others. It’s doing this at scale, largely out of sight, and with almost no accountability when it
Generative AI and Finance
Since ChatGPT's release in 2022, demand for artificial intelligence (AI)–related skills in finance has grown rapidly, as generative AI drives significant technological changes in both the financial research field and the broader economy. We show that financial occupations are highly exposed to the productivity effects of generative AI, review the literature on the impact of ChatGPT on firm value, and provide directions for future research investigating the impact of this major technology shock. Generative AI also holds great potential as a tool for finance researchers and practitioners: We review and describe innovations in research methods linked to improvements in AI tools, along with their applications. We offer a practical introduction to available tools and advice for researchers in academia and industry interested in using these tools.
Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations
Despite widespread speculation about artificial intelligence's impact on the future of work, we lack systematic empirical evidence about how these systems are actually being used for different tasks. Here, we present a novel framework for measuring AI usage patterns across the economy. We leverage a recent privacy-preserving system to analyze over four million Claude.ai conversations through the lens of tasks and occupations in the U.S. Department of Labor's O*NET Database. Our analysis reveals that AI usage primarily concentrates in software development and writing tasks, which together account for nearly half of all total usage. However, usage of AI extends more broadly across the economy, with approximately 36% of occupations using AI for at least a quarter of their associated tasks. We also analyze how AI is being used for tasks, finding 57% of usage suggests augmentation of human capabilities (e.g., learning or iterating on an output) while 43% suggests automation (e.g., fulfilling a request with minimal human involvement). While our data and methods face important limitations and only paint a picture of AI usage on a single platform, they provide an automated, granular approach for tracking AI's evolving role in the economy and identifying leading indicators of future impact as these technologies continue to advance.
Mind the Gap: AI Adoption in Europe and the U.S.
This paper combines international evidence from worker and firm surveys conducted in 2025 and 2026 to document large gaps in AI adoption, both between the US and Europe and across European countries. Cross-country differences in worker demographics and firm composition account for an important share of these gaps. AI adoption, within and across countries, is also closely linked to firm personnel management practices and whether firms actively encourage AI use by workers. Micro-level evidence suggests that AI generates meaningful time savings for many workers. At the macro level, in recent years industries with higher AI adoption rates have experienced faster productivity growth. While we do not establish causality, this relationship is statistically significant and similar in magnitude in Europe and the US. We do not find clear evidence that industry-level AI adoption is associated with employment changes. We discuss limitations of existing data and outline priorities for future data collection to better assess the productivity and labor market effects of AI.
Does AI Actually Boost Developer Productivity? (100k Devs Study) - Yegor Denisov-Blanch, Stanford
The efficiency-gain illusion: People underestimate the rate of AI use and overestimate its benefits on simple tasks
People are increasingly turning to AI assistance for simple tasks, e.g., arithmetic, spell-check, and answering simple questions. But does AI assistance actually save users time and effort? We investigate people's propensity to use AI for cognitively simple tasks and assess whether their reliance is well-calibrated. Across three pre-registered user studies (N = 2691), we find that people frequently choose to use AI even when doing so is inefficient (i.e. provides no meaningful time or effort savings). We identify systematic miscalibration at two levels: (1) a self-estimate miscalibration where people on average believe that they are using AI less than they actually are, and (2) efficiency-gain illusions where people overestimate how much time and effort savings AI use affords. We also identify a session-level carryover effect where a participant's prior AI use leads to further AI adoption and entrenches their miscalibration about time savings. Our results shed light on the mechanisms and biases underlying people's choice of whether to use AI as well as the risk of an overreliance feedback loop.
AI makes you smarter but none the wiser: The disconnect between performance and metacognition
Optimizing human–AI interaction requires users to reflect on their performance critically, yet little is known about generative AI systems’ effect on users’ metacognitive judgments. In two large-scale studies, we investigate how AI usage is associated with users’ metacognitive monitoring and performance in logical reasoning tasks. Specifically, our paper examines whether people using AI to complete tasks can accurately monitor how well they perform. In Study 1, participants (N = 246) used AI to solve 20 logical reasoning problems from the Law School Admission Test. While their task performance improved by three points compared to a norm population, participants overestimated their task performance by four points. Interestingly, higher AI literacy correlated with lower metacognitive accuracy, suggesting that those with more technical knowledge of AI were more confident but less precise in judging their own performance. Using a computational model, we explored individual differences in metacognitive accuracy and found that the Dunning–Kruger effect, usually observed in this task, ceased to exist with AI use. Study 2 (N = 452) replicates these findings. We discuss how AI levels cognitive and metacognitive performance in human–AI interaction and consider the consequences of performance overestimation for designing interactive AI systems that foster accurate self-monitoring, avoid overreliance, and enhance cognitive performance.
AI security issues dominate corporate worries, spending
Two reports illustrate how business leaders are thinking about and budgeting for generative AI.
The Impact of AI Coding Assistants on Software Engineering: A Longitudinal Study
AI coding assistants have become prolific in recent years. Through a longitudinal mixed-methods investigation, we examined how professional software engineers perceive the effects of AI coding assistants in regard to task focus, developer experience, and productivity. Two questionnaires were administered six months apart, yielding 158 eligible participants at the first time point, 101 at the second, and a matched longitudinal cohort of 95. Participants reported spending less time on most development tasks, with 82% reporting less on writing code. We find broader shift in focus from creation to verification activities. We propose a new category of work we term supervisory engineering work, encompassing the direction, evaluation, and correction of AI output. We also identified a productivity-experience paradox: productivity perceptions held stable, with 84% reporting improvement at both time points, yet among matched participants, the proportion reporting worsened developer experience in at least one dimension nearly doubled from 14% to 27%, with flow state and cognitive load eroding while feedback loops improved. These findings suggest that AI coding assistants are impacting both the nature of software engineering work and how engineers experience it.
Artificial intelligence and personal finance
Artificial intelligence (AI) is transforming how consumers access and use financial information, education and advice for personal financial decision making. While consumers’ increasing use of AI tools and AI-generated content for personal finance brings opportunities in terms of accessibility, personalisation and decision making, it also increases risks related to bias, hallucinations, commercial influence, data privacy and exclusion, with uncertain benefits on long-term financial well-being. This policy paper provides policymakers and stakeholders with an overview of current trends, opportunities and risks in the use of AI in personal finance and in the design and delivery of financial education. It also proposes a set of financial literacy competencies to support the use of AI in personal financial decision making.
Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools
How do the productivity effects of AI evolve across successive generations of tools, and to what extent do task-level gains ultimately translate into final output? We study these questions in the context of software development, using data on more than 100,000 GitHub developers combined with their AI usage telemetry. In a matched event study design, we find that autocomplete, interactive coding agents, and autonomous coding agents each significantly increase coding activity ("commits"), with respective cumulative effects of 40%, 140%, and 180%. These gains, however, attenuate sharply across the production hierarchy: the 180% cumulative effect falls to 50% for the number of projects, and to 30% for actual releases. This pattern is consistent with the weak-link hypothesis: the strong productivity gains from AI are attenuated by human bottlenecks in the production chain, with an estimated elasticity of substitution of 0.25 between AI and human effort, which indicates strong complementarities. We further confirm these results across four major app marketplaces, finding a moderate increase in the number of new apps but no increase in total usage. Large task-level AI productivity gains have therefore translated only partially into shipped and used software thus far.
The AI productivity myth is more harmful than you think
Perceived productivity may be up thanks to AI, but there's debt collecting in the shadows.
Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity
Despite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the February-June 2025 frontier affect the productivity of experienced open-source developers. 16 developers with moderate AI experience complete 246 tasks in mature projects on which they have an average of 5 years of prior experience. Each task is randomly assigned to allow or disallow usage of early 2025 AI tools. When AI tools are allowed, developers primarily use Cursor Pro, a popular code editor, and Claude 3.5/3.7 Sonnet. Before starting tasks, developers forecast that allowing AI will reduce completion time by 24%. After completing the study, developers estimate that allowing AI reduced completion time by 20%. Surprisingly, we find that allowing AI actually increases completion time by 19%--AI tooling slowed developers down. This slowdown also contradicts predictions from experts in economics (39% shorter) and ML (38% shorter). To understand this result, we collect and evaluate evidence for 20 properties of our setting that a priori could contribute to the observed slowdown effect--for example, the size and quality standards of projects, or prior developer experience with AI tooling. Although the influence of experimental artifacts cannot be entirely ruled out, the robustness of the slowdown effect across our analyses suggests it is unlikely to primarily be a function of our experimental design.