Generative AI at Work
Open accessAbstract
We study the staggered introduction of a generative AI-based conversational assistant using data from 5,179 customer support agents.Access to the tool increases productivity, as measured by issues resolved per hour, by 14 percent on average, with the greatest impact on novice and lowskilled workers, and minimal impact on experienced and highly skilled workers.We provide suggestive evidence that the AI model disseminates the potentially tacit knowledge of more able workers and helps newer workers move down the experience curve.In addition, we show that AI assistance improves customer sentiment, reduces requests for managerial intervention, and improves employee retention.
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Claims the paper makes
- Finding§ ABSTRACT
Access to the AI tool increases productivity, measured by issues resolved per hour, by 14% on average.
“Access to the tool increases productivity, as measured by issues resolved per hour, by 14% on average”
Quote in the paperNot yet classified - Finding§ ABSTRACT
AI assistance improves customer sentiment, increases employee retention, and may lead to worker learning.
“In addition, we find that AI assistance improves customer sentiment, increases employee retention, and may lead to worker learning.”
Quote in the paperNot yet classified - Finding§ 5 Adherence, Learning, and Conversational Change
Access to AI assistance changes the content of what agents write and makes low-skill workers communicate more like high-skill workers.
“We document within-agent changes in communication following AI deployment, with larger changes for lower-skill workers. Acrossperson, we show that these changes increase the similarity of communication patterns between lowand high-skill agents.”
Quote in the paperNot yet classified - Finding§ 5 Adherence, Learning, and Conversational Change
Exposure to AI leads to durable changes in worker skills, as workers continue to perform better during outages.
“Using information on software outages in which AI assistance is temporarily unavailable, we provide evidence that exposure to AI leads to durable changes in worker skills. We find that workers exposed to AI recommendations continue to perform better during outages”
Quote in the paperNot yet classified - Finding§ 5 Adherence, Learning, and Conversational Change
Workers follow AI recommendations about 35% to 38% of the time on average.
“we show that workers are selective about the recommendations they adopt, following the recommendations 35% on average.”
Quote in the paperNot yet classified
- Finding§ 4.2.1 Pre-treatment Worker Skill
The productivity effects are strongest for workers in the lowest skill quintile, who see a 34% increase in resolutions per hour.
“In Panel A, we show that the productivity impact of AI assistance is most pronounced for workers in the lowest skill quintile (leftmost side), who see a .29 log point or 34% increase in resolutions per hour.”
Quote in the paperNot yet classified - Finding§ ABSTRACT
Access to AI assistance reduces worker attrition, especially among newer agents.
“These changes come alongside a substantial decrease in worker attrition, which is driven by the retention of newer workers.”
Quote in the paperNot yet classified - Finding§ 4.2.2 Pre-treatment Worker Experience
Agents with less than one month of tenure improve their resolutions per hour by 46%, while agents with more than a year of tenure see no effect.
“Agents with less than 1 month of tenure improve their resolutions per hour by .38 log points or 46% improvement (relative to agents of the same tenure who do not have access to AI assistance). In contrast, we see no effect for agents with more than a year of tenure.”
Quote in the paperNot yet classified - Finding§ ABSTRACT
The productivity gain is 34% for novice and low-skilled workers but minimal for experienced and highly skilled workers.
“including a 34% improvement for novice and low-skilled workers but with minimal impact on experienced and highly skilled workers.”
Quote in the paperNot yet classified - Finding§ 4.1 Productivity Metrics
With agent and tenure fixed effects, the deployment of AI increases resolutions per hour by 0.30 chats, or 13.8%.
“with agent and tenure fixed effects, we find that the deployment of AI increases RPH by 0.30 chats or 13.8%.”
Quote in the paperNot yet classified - Finding§ 4.2.3 Moving Down the Experience Curve
Workers with always-on AI access increase productivity faster than workers without AI access, suggesting the tool helps them move down the experience curve more quickly.
“Comparing just these two groups suggests that access to AI recommendations helps workers move more quickly down the experience curve.”
Quote in the paperNot yet classified - Finding§ ABSTRACT
Access to AI assistance improves customer sentiment, with the largest effects for lower-skill and less-experienced agents.
“We show that access to AI assistance markedly improves how customers treat agents, as measured by the sentiment of their chat messages.”
Quote in the paperNot yet classified - Finding§ 5.1 Adherence to AI recommendations
Higher adherence is associated with larger gains in productivity.
“We find a steady and monotonic increase in returns by agent adherence: among agents in the lowest quintile, we still see a 10% gain in productivity, but for agents in the highest quintile, the estimated impact is over twice as high, close to 25%.”
Quote in the paperNot yet classified - Finding§ 4.1 Productivity Metrics
Access to AI recommendations increases resolutions per hour by 0.47 chats, or 22.2%, in the simplest specification.
“access to AI recommendations increases resolutions per hour by 0.47 chats, up 22.2% from an average of 2.12.”
Quote in the paperNot yet classified - Finding§ ABSTRACT
The authors provide suggestive evidence that the AI model disseminates the best practices of more able workers and helps newer workers move down the experience curve.
“We provide suggestive evidence that the AI model disseminates the best practices of more able workers and helps newer workers move down the experience curve.”
Quote in the paperNot yet classified - Finding§ ABSTRACT
The paper concludes that access to generative AI can increase productivity, with large heterogeneity in effects across workers.
“Our results suggest that access to generative AI can increase productivity, with large heterogeneity in effects across workers.”
Quote in the paperNot yet classified - Finding§ 4.2.1 Pre-treatment Worker Skill
The most skilled workers do not see a productivity increase from AI assistance.
“In contrast, AI assistance does not lead to any productivity increase for the most skilled workers (rightmost side).”
Quote in the paperNot yet classified - Method§ ABSTRACT
The paper studies the staggered introduction of a generative AI conversational assistant for customer support agents using data from 5,179 agents.
“In this paper, we study the staggered introduction of a generative AI-based conversational assistant using data from 5,179 customer support agents.”
Quote in the paperNot yet classified - Method§ 3.1 AI Model Deployment
The AI assistant was gradually rolled out at the agent level after an initial seven-week randomized pilot featuring 50 agents.
“The AI assistant we study was gradually rolled out at the agent level after an initial seven-week randomized pilot featuring 50 agents.”
Quote in the paperNot yet classified - Method§ 3.2 Summary Statistics
The study observes 3 million chats by 5,179 agents, including 1.2 million chats by 1,636 agents in the post-AI period.
“In total, we observe the conversation text and outcomes associated with 3 million chats by 5,179 agents. Within this, we observe 1.2 million chats by 1,636 agents in the post-AI period.”
Quote in the paperNot yet classified - Background§ 1.1 Technical Primer
LLMs are trained to predict the next word in a sequence using a large corpus of text.
“An LLM is trained by learning to predict the next word in a sequence, given what has come before, using a large corpus of text (such as Wikipedia, digitized books, or portions of the Internet).”
Quote in the paperNot yet classified - Method§ 1.1 Technical Primer
The paper focuses on large language models as an important class of generative AI.
“This paper focuses on an important class of generative AI, large language models (LLMs).”
Quote in the paperNot yet classified - Method§ 2.3 AI System Design
The AI system combines a recent version of GPT with additional ML algorithms fine-tuned for customer service interactions.
“The AI system we study combines a recent version of GPT with additional ML algorithms specifically fine-tuned to focus on customer service interactions.”
Quote in the paperNot yet classified - Method§ 2.3 AI System Design
The deployed system generates real-time response suggestions and links to internal documentation based on the conversation history.
“Once deployed, the AI system generates two main types of output: 1) real-time suggestions for how agents should respond to customers and 2) links to the data firm's internal documentation for relevant technical issues.”
Quote in the paperNot yet classified
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Generative AI models often require significant computational power and resources for training and inference such as GPUs or cloud computing services.
“Generative AI models often require significant computational power and resources for training and inference such as GPUs or cloud computing services [8, 15].”
From Generative AI in healthcare: an implementation science informed translational path on application, integration and governance (2024) · cites this paper as [8]
The Technical Primer (section 1.1; page not provided) says that pre-training an LLM requires thousands of GPUs and weeks to months of dedicated training time, supporting the claim about substantial training resources. The passages do not establish comparable resource requirements for inference or mention cloud computing services. (LLM, conf=0.88)
Experimental — accuracy not yet measuredGenerative AI technologies like GANs and LLMs are complex to understand and implement.
“However, generative AI technologies like GANs and LLMs are complex to understand and implement [8].”
From Generative AI in healthcare: an implementation science informed translational path on application, integration and governance (2024) · cites this paper as [8]
The Technical Primer (§1.1; page unavailable) describes substantial resources needed to pre-train an LLM, supporting a narrower claim that building one is demanding. It does not say that LLMs are difficult to understand or discuss GANs, so it does not support the full claim. (LLM, conf=0.80)
Experimental — accuracy not yet measuredGenerative AI assistance reduces the 'skill gap' by providing the largest benefits for the weakest students.
“matching prior findings that generative AI assistance reduces the “skill gap” by providing the largest benefits for the weakest students (7–9).”
From Generative AI without guardrails can harm learning: Evidence from high school mathematics (2025) · cites this paper as [8]
Section 4.2 reports the largest productivity gain for customer-support workers in the lowest skill quintile and no gain for the most skilled workers. This supports a narrowing of the skill gap among workers, but the paper does not study students, so it does not support that population-specific part of the claim. (LLM, conf=0.90)
Experimental — accuracy not yet measuredLLM can be used to summarize and synthesize vast amounts of information, manage customer inquiries, support organizational decision-making and generate technical and creative content.
“LLM can be used to summarize and synthesize vast amounts of information (eg, legislative analysis tools), manage customer inquiries (eg, chatbot customer service applications), support organizational decision-making (eg, resume screening and job matching platforms) and generate technical and creative content (eg, content ideation tools) (17–21).”
From Large language model exposure and precarious occupations: Unpacking relationships in the Canadian labor force (2026) · cites this paper as [17]
Section 1.2 describes LLM tools that summarize legal documents and research and perform coding, writing, and graphic-design tasks. Section 2.1 describes their use in customer support. The passages do not support the organizational decision-making example of resume screening and job matching. (LLM, conf=0.86)
Experimental — accuracy not yet measuredIn the three studies of writing, customer service and software coding, AI tools supplemented expertise rather than displaced experts.
“In all three instances, AI tools supplemented expertise rather than displaced experts.”
From Applying AI to Rebuild Middle Class Jobs (2024) · cites this paper as Brynjolfsson et al., 2023
The paper’s Abstract says the customer-service tool was designed to augment agents, who remained responsible for conversations and could ignore its suggestions. It does not establish the same conclusion for the writing and coding studies. It also says the study was not designed to assess aggregate employment effects, so it cannot establish that experts were not displaced. (LLM, conf=0.88)
Experimental — accuracy not yet measured
AI tools helped novice customer service workers attain the capabilities of experienced agents in three months rather than 10.
“AI tools helped novice workers attain the capabilities of experienced agents in three months rather than 10.”
From Applying AI to Rebuild Middle Class Jobs (2024) · cites this paper as Brynjolfsson et al., 2023
Section 4.3, “Moving Down the Experience Curve” (page unavailable), supports the central point that AI helped new agents reach a comparable productivity level faster. It reports about 2 months with AI versus 8–10 months without it—not 3 versus 10—and measures productivity rather than agents’ capabilities generally. (LLM, conf=0.88)
Experimental — accuracy not yet measuredSupport workers buffered by the AI tool experienced significantly less hostility both from their clients and toward their clients.
“Buffered by the AI tool, support workers experienced significantly less hostility both from their clients and also toward their clients.”
From Applying AI to Rebuild Middle Class Jobs (2024) · cites this paper as Brynjolfsson et al., 2023
Section 4.6.1 (Sentiment; page unavailable) reports improved customer sentiment after AI deployment, consistent with less hostility from clients. But it reports no detectable effect on agent sentiment, so the claim that workers were significantly less hostile toward clients is not supported. The study measures sentiment rather than hostility directly. (LLM, conf=0.91)
Experimental — accuracy not yet measuredThe fall in quit rates among new customer service agents was likely due to less customer rage directed at them in chat windows.
“As an unexpected side-effect, quit rates among new agents also fell substantially, likely due to less customer rage directed at them in chat windows.”
From Applying AI to Rebuild Middle Class Jobs (2024) · cites this paper as Brynjolfsson et al., 2023
The attrition analysis (Additional information; page unavailable) reports a substantial reduction in departures among agents with less than six months’ experience. Section 4.6.1 reports improved customer sentiment after AI deployment and describes customer abuse as a source of stress linked to attrition. The paper does not establish that reduced customer hostility caused the fall in quit rates. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe field of AI is evolving rapidly, particularly in areas such as Generative AI and Large Language Models.
“However, the field of AI is evolving rapidly, particularly in areas such as Generative AI (Brynjolfsson et al. 2023) and Large Language Models (Min et al. 2023).”
From Artificial intelligence for literature reviews: opportunities and challenges (2024) · cites this paper as Brynjolfsson et al. 2023
That generative AI particularly benefited less able writers is paralleled in recent studies focusing on other domains in which generative AI has been shown to help less productive workers.
“That generative AI particularly benefited less able writers is paralleled in recent studies focusing on other domains in which generative AI has been shown to help less productive workers (4, 5).”
From Generative artificial intelligence enhances creativity but reduces the diversity of novel content (2023) · cites this paper as [5]
Previous studies have found that LLMs increase productivity the most for low-performing workers.
“Previous studies have found that LLMs increase productivity the most for low-performing workers (Brynjolfsson et al. 2023, Noy and Zhang 2023, Dell'Acqua et al. 2023).”
From AI Meets the Classroom: When Do Large Language Models Harm Learning? (2024) · cites this paper as Brynjolfsson et al. 2023
Generative AI requires large amounts of data to train models effectively.
“Generative AI requires large amounts of data to train models effectively [8].”
From Generative AI in healthcare: an implementation science informed translational path on application, integration and governance (2024) · cites this paper as [8]
Generative AI models learn from data.
“Generative AI models learn from data [8], so the healthcare organisation needs to collect and prepare relevant data for training the models.”
From Generative AI in healthcare: an implementation science informed translational path on application, integration and governance (2024) · cites this paper as [8]
Workers can perform knowledge-intensive tasks significantly more efficiently when given access to generative AI.
“Broadly speaking, these studies have focused on productivity, finding that workers can perform knowledge-intensive tasks significantly more efficiently when given access to generative AI.”
From Generative AI without guardrails can harm learning: Evidence from high school mathematics (2025) · cites this paper as [8]
The study's results are consistent with the existing literature on the productivity effects of generative AI.
“These results are consistent with the existing literature on the productivity effects of generative AI (7, 8).”
From Generative AI without guardrails can harm learning: Evidence from high school mathematics (2025) · cites this paper as [8]
LLMs appear to increase inequality in education.
“Unlike in workplaces (Brynjolfsson et al. 2023, Dell'Acqua et al. 2023), LLMs appear to increase inequality in education.”
From AI Meets the Classroom: When Do Large Language Models Harm Learning? (2024) · cites this paper as Brynjolfsson et al. 2023
Many of the reported productivity gains of AI have occurred in cases in which the technology complemented human work.
“Many of the reported productivity gains of AI have occurred in cases in which the technology complemented human work (Fügener et al. 2022, Brynjolfsson et al. 2023, Dell'Acqua et al. 2023, Keppler et al. 2024).”
From AI Meets the Classroom: When Do Large Language Models Harm Learning? (2024) · cites this paper as Brynjolfsson et al. 2023
Brynjolfsson et al. estimated a significant, roughly 14% improvement in productivity for customer service agents using generative AI tools that provide suggested responses.
“They also estimated a significant, roughly 14% improvement in productivity, and akin to Noy and Zhang's study, these gains were most pronounced among novice workers.”
From Applying AI to Rebuild Middle Class Jobs (2024) · cites this paper as Brynjolfsson et al., 2023
Quit rates among new customer service agents fell substantially.
“As an unexpected side-effect, quit rates among new agents also fell substantially, likely due to less customer rage directed at them in chat windows.”
From Applying AI to Rebuild Middle Class Jobs (2024) · cites this paper as Brynjolfsson et al., 2023
In the Brynjolfsson et al. study, productivity gains from generative AI were most pronounced among novice workers.
“They also estimated a significant, roughly 14% improvement in productivity, and akin to Noy and Zhang's study, these gains were most pronounced among novice workers.”
From Applying AI to Rebuild Middle Class Jobs (2024) · cites this paper as Brynjolfsson et al., 2023
Brynjolfsson et al. (2023) report a 14% productivity increase using Generative AI-based conversational assistants for customer service representatives.
“Brynjolfsson et al. (2023) report a 14% productivity increase using Generative AI-based conversational assistants for customer service representatives [9].”
From Sentiment Analysis in the Age of Generative AI (2024) · cites this paper as [9]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
US agent share conflicts with the stated share abroad
MinorThe text says 83 percent of agents are outside the United States, implying 17 percent are in the United States. Table 1 instead reports a US share of 11 percent for all agents.
“Most agents in our sample, 83 percent, are located outside the United States, primarily in the Philippines.”
“| Share US Agents | .11 | .15 | .081 | .072 |”
Manager-request result is assigned to the wrong table column
MinorThe text attributes the decline in manager requests to Column 1 of Table 5, but Column 1 reports attrition. Manager requests are in Column 2; the subsequent discussion likewise assigns transfers to Column 2 although they are in Column 3.
“In Column 1 of Table 5, we find that AI assistance generates an almost 25 percent decline in customer requests to speak to a manager.”
“| | (1) | (2) | (3) | | --- | --- | --- | --- | | VARIABLES | Leaves this Month Request Manager Transfer | | | | Post AI X Ever Treated | -0.0868** | -0.00875*** | 0.00792 |”
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More than a Feeling: Accuracy and Application of Sentiment Analysis
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Two-way Fixed Effects Estimators with Heterogeneous Treatment Effects
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Polanyi's Paradox and the Shape of Employment Growth
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InfluentialMake Versus Buy in Trucking:Asset Ownership, Job Design, and Information
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An empirical evaluation of GitHub copilot's code suggestions
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Hierarchies and the Organization of Knowledge in ProductionPreprint
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The Impact of AI on Developer Productivity: Evidence from GitHub Copilot
Peng, S., Kalliamvakou, E., Cihon, P., & Demirer, M. (2023) · arXiv · doi:10.48550/arxiv.2302.06590 · Cited by 287 · see citing works
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Self-citationThe Distinct Effects of Information Technology and Communication Technology on Firm Organization
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BERT: A Review of Applications in Natural Language Processing and UnderstandingPreprint
Koroteev, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.11943 · Cited by 193 · cited 2× · see citing works
“…for each conversation, we can compare the similarity of conversations by looking at the cosine similarities of their associated vectors; this common approach yields a score of 0 if two pieces of text are semantically orthogonal and a score of 1 if they have the same meaning (Koroteev, 2021).”
Peng, B., Galley, M., He, P., Cheng, H., Xie, Y., Hu, Y., Huang, Q., Liden, L., Yu, Z., Chen, W., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.12813 · Cited by 156 · see citing works
Where to Hide a Stolen Elephant: Leaps in Creative Writing with Multimodal Machine Intelligence
Singh, N., Bernal, G., Savchenko, D., & Glassman, E. L. (2023) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3511599 · Cited by 119 · cited 1× · see citing works
“writing suggestion tool, Singh et al. (2022) finds that four of the 23 study participants refused to engage with AI suggestions.”
Knowledge-Based Hierarchies: Using Organizations to Understand the Economy
Garicano, L., & Rossi‐Hansberg, E. (2015) · Annual Review of Economics · doi:10.1146/annurev-economics-080614-115748 · Cited by 100 · cited 1× · see citing works
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Make Versus Buy in Trucking: Asset Ownership, Job Design and Information
Baker, G. P., & Hubbard, T. N. (2002) · National Bureau of Economic Research · doi:10.3386/w8727 · Cited by 95 · see citing works
Sentiment Analysis: An Overview
Kadam, S. A., & Joglekar, S. T. (2013) · Cited by 82 · see citing works
The Skill Complementarity of Broadband Internet
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ICT capital–skill complementarity and wage inequality: Evidence from OECD countries
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Context-Sensitive Visualization of Deep Learning Natural Language Processing Models
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Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models
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“Finally, general-purpose LLMs can be further “fine-tuned” to generate output that matches the priorities of any specific setting (Ouyang et al., 2022; Liu et al., 2023).”
InfluentialDo Users Write More Insecure Code with AI Assistants?
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Sorted by citations
Experimental evidence on the productivity effects of generative artificial intelligence
Noy, S., & Zhang, W. (2023) · Science · doi:10.1126/science.adh2586 · Cited by 2,150 · see citing works
“These results are consistent with other studies showing productivity-enhancing and equalizing effects of recent AI technologies (8, 15, 16, 18).”
ResultBrynjolfsson, E., Li, D., & Raymond, L. (2025) · The Quarterly Journal of Economics · doi:10.1093/qje/qjae044 · Cited by 1,083 · see citing works
Self-citationGenerative artificial intelligence enhances creativity but reduces the diversity of novel content
Doshi, A., & Hauser, O. (2023) · doi:10.2139/ssrn.4535536 · Cited by 887 · see citing works
“…( 3 ), increase quality and efficiency of production of typical white-collar work ( 4 ), promote productivity in customer support relations ( 5 , 6 ), speed up programming tasks ( 7 ), and enhance persuasion messaging ( 8 ), little is known about generative AI’s potential impact on a…”
Bankins, S., Ocampo, A. C., Marrone, M., Restubog, S. L. D., & Woo, S. E. (2024) · Journal of Organizational Behavior · doi:10.1002/job.2735 · Cited by 799 · see citing works
Generative artificial intelligence
Banh, L., & Strobel, G. (2023) · Electronic Markets · doi:10.1007/s12525-023-00680-1 · Cited by 722 · see citing works
“…GET3D (Gao et al., 2022), and Point-E (Nichol et al., 2022), which generate realistic and complex 3D models that facilitate a range of applications from product design and architecture to virtual reality and game development with their customers (Brynjolfsson et al., 2023; Mondal et al., 2023).”
BackgroundReddy, S. (2024) · Implementation Science · doi:10.1186/s13012-024-01357-9 · Cited by 554 · see citing works
“However, generative AI technologies like GANs and LLMs are complex to understand and implement [8].”
BackgroundMethodInfluentialJackson, I., Ivanov, D. A., Dolgui, A., & Namdar, J. (2024) · International Journal of Production Research · doi:10.1080/00207543.2024.2309309 · Cited by 402 · see citing works
“These technologies belong to the realm of Generative AI (GAI), a branch of Machine Learning (ML) that can create new content, including text, images, music, or video, by learning patterns from existing data (Brynjolfsson, Li, and Raymond 2023).”
InfluentialBengesi, S., El-Sayed, H., Sarker, M. K., Houkpati, Y., Irungu, J., & Oladunni, T. (2023) · IEEE Access · doi:10.1109/access.2024.3397775 · Cited by 286 · see citing works
“Despite numerous recent studies to address the surge of GAI [31], [31], [34], [34], [35], [35], [36], [36], [37], [38], [39], [40], [41], [44], [45], [46], discussing challenges, tasks, and models, there still needs to be more thorough exploration into the theoretical and mathematical foundations…”
BackgroundThe Crowdless Future? Generative AI and Creative Problem-Solving
Boussioux, L., Lane, J. N., Zhang, M., Jaćimović, V., & Lakhani, K. R. (2024) · Organization Science · doi:10.1287/orsc.2023.18430 · Cited by 369 · see citing works
“Most generative AI studies using LLMs in organizational settings have focused on investigating the productivity effects of these technologies in the workplace (Brynjolfsson et al. 2023, Dell’Acqua et al. 2023, Noy and Zhang 2023, Otis et al. 2024).”
BackgroundWamba, S. F., Guthrie, C. H., de Queiroz, M. M., & Minner, S. (2023) · International Journal of Production Research · doi:10.1080/00207543.2023.2294116 · Cited by 322 · see citing works
Artificial intelligence for literature reviews: opportunities and challenges
Bolaños, F., Salatino, A., Osborne, F., & Motta, E. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10902-3 · Cited by 246 · see citing works
Generative AI without guardrails can harm learning: Evidence from high school mathematics
Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2422633122 · Cited by 309 · see citing works
Hui, X., Reshef, O., & Zhou, L. (2024) · Organization Science · doi:10.1287/orsc.2023.18441 · Cited by 200 · see citing works
Artificial Intelligence and the Transformation of Higher Education Institutions: A Systems Approach
Katsamakas, E., Pavlov, O. V., & Saklad, R. (2024) · Sustainability · doi:10.3390/su16146118 · Cited by 195 · see citing works
Will Generative Artificial Intelligence Deliver on Its Promise in Health Care?
Wachter, R. M., & Brynjolfsson, E. (2023) · JAMA · doi:10.1001/jama.2023.25054 · Cited by 190 · see citing works
Self-citationSentiment Analysis in the Age of Generative AI
Krugmann, J. O., & Hartmann, J. (2024) · Customer Needs and Solutions · doi:10.1007/s40547-024-00143-4 · Cited by 189 · see citing works
“Brynjolfsson et al. (2023) report a 14% productivity increase using Generative AI-based conversational assistants for customer service representatives [9].”
The power of generative marketing: Can generative AI create superhuman visual marketing content?
Hartmann, J., Exner, Y., & Domdey, S. (2025) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2024.09.002 · Cited by 229 · see citing works
From Fiction to Fact: The Growing Role of Generative AI in Business and Finance
Chen, B., Wu, Z., & Zhao, R. (2023) · Journal of Chinese Economic and Business Studies · doi:10.1080/14765284.2023.2245279 · Cited by 190 · see citing works
“For example, Brynjolfsson et al. (2023) demonstrate that generative AI does not require input instructions because it recognizes patterns from training sets.”
BackgroundJackson, I., Sáenz, M. J., & Ivanov, D. (2023) · International Journal of Production Research · doi:10.1080/00207543.2023.2276811 · Cited by 174 · see citing works
“These tools are representative of advancements in Generative AI, a specialised branch of Machine Learning (ML) that focuses on the generation of new content, including images, music, or video, by discerning patterns from existing data (Brynjolfsson, Li, and Raymond 2023).”
BackgroundMethodNew Frontiers: The Origins and Content of New Work, 1940–2018
Autor, D., Chin, C., Salomons, A., & Seegmiller, B. (2022) · The Quarterly Journal of Economics · doi:10.1093/qje/qjae008 · Cited by 277 · see citing works
Uses of Generative AI in the Newsroom: Mapping Journalists’ Perceptions of Perils and Possibilities
Cools, H., & Diakopoulos, N. A. (2024) · Journalism Practice · doi:10.1080/17512786.2024.2394558 · Cited by 180 · see citing works
“These tools generate text, audio, video, or other media and have the potential to disrupt specific work processes in communication and media industries such as marketing, gaming, entertainment, and journalism with the help of AI (Brynjolfsson, Li, and Raymond 2023).”
Applying AI to Rebuild Middle Class Jobs
Autor, D. (2024) · doi:10.3386/w32140 · Cited by 157 · see citing works
“ChatGPT seems too good to be true”: College students’ use and perceptions of generative AI
Baek, C., Tate, T., & Warschauer, M. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100294 · Cited by 150 · see citing works
Generative Artificial Intelligence in Education: Advancing Adaptive and Personalized Learning
Guettala, M., Bourekkache, S., Kazar, O., & Harous, S. (2024) · Acta Informatica Pragensia · doi:10.18267/j.aip.235 · Cited by 190 · see citing works
A Design Space for Intelligent and Interactive Writing Assistants
Lee, M., Gero, K. I., Chung, J. J. Y., Shum, S. B., Raheja, V., Shen, H., Venugopalan, S., Wambsganss, T., Zhou, D., Alghamdi, E. A., et al. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642697 · Cited by 136 · see citing works
Shin, D., Koerber, A., & Lim, J. S. (2025) · New Media & Society · doi:10.1177/14614448241234040 · Cited by 137 · see citing works
“GenAI will not only inun-date the Internet with more misinformation but will also generate convincing disinformation that is targeted at vulnerable users (Brynjolfsson et al., 2023).”
Generative artificial intelligence and engineering education
Johri, A., Katz, A. S., Qadir, J., & Hingle, A. (2023) · Journal of Engineering Education · doi:10.1002/jee.20537 · Cited by 121 · see citing works
“Moreover, the value addition of a trained human will also be harder to justify in many cases (Brynjolfsson et al., 2023).”
BackgroundAddressing 6 challenges in generative AI for digital health: A scoping review
Templin, T., Perez, M. W., Sylvia, S., Leek, J. T., & Sinnott-Armstrong, N. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000503 · Cited by 115 · see citing works
Exploring the Role of Generative AI in Enhancing Language Learning: Opportunities and Challenges
Creely, E. (2024) · International Journal of Changes in Education · doi:10.47852/bonviewijce42022495 · Cited by 113 · see citing works
How Knowledge Workers Think Generative AI Will (Not) Transform Their Industries
Woodruff, A., Shelby, R. M., Kelley, P. G., Rousso-Schindler, S., Smith-Loud, J., & Wilcox, L. G. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642700 · Cited by 126 · see citing works
AUTOGEN: A Personalized Large Language Model for Academic Enhancement—Ethics and Proof of Principle
Mann, S. P., Earp, B. D., Møller, N., Suren, V., & Savulescu, J. (2023) · The American Journal of Bioethics · doi:10.1080/15265161.2023.2233356 · Cited by 107 · see citing works
Deploying artificial intelligence in services to AID vulnerable consumers
Hermann, E., Williams, G. Y., & Puntoni, S. (2023) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-023-00986-8 · Cited by 113 · see citing works
“Due to its advantages, including increased efficiency and cost reduction (Brynjolfsson et al., 2023; Huang & Rust, 2021a; Kunz & Wirtz, 2023), the adoption of AI technologies by companies is increasing rapidly.”
AI hype as a cyber security risk: the moral responsibility of implementing generative AI in business
Humphreys, D., Koay, A., Desmond, D., & Mealy, E. (2024) · AI and Ethics · doi:10.1007/s43681-024-00443-4 · Cited by 115 · see citing works
“Studies have so far looked at the effect of generative AI in areas such as call centres [ 5], on knowledge worker productivity and quality [42], risk management and finance [ 43] and on operations and supply chains [44]. principles to the introduction of new technology in society and into the…”
Generative AI in Marketing and Principles for Ethical Design and Deployment
Hermann, E., & Puntoni, S. (2024) · Journal of Public Policy & Marketing · doi:10.1177/07439156241309874 · Cited by 93 · see citing works
“However, the productivity effects of GenAI for assisting humans differ according to skill level, with highly skilled workers bene fi ting less than low-skilled workers (Brynjolfsson, Li, and Raymond 2023; Noy and Zhang 2023).”
Singh, B. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-1565-1.ch009 · Cited by 85 · see citing works
AI Anxiety: a comprehensive analysis of psychological factors and interventions
Kim, J. J. H., Soh, J. Y., Kadkol, S., Solomon, I., Yeh, H., Srivatsa, A. V., Nahass, G. R., Choi, J. Y., Lee, S., Nyugen, T., et al. (2025) · AI and Ethics · doi:10.1007/s43681-025-00686-9 · Cited by 160 · see citing works
Quantifying the use and potential benefits of artificial intelligence in scientific research
Gao, J., & Wang, D. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02020-5 · Cited by 81 · see citing works
Kim, Y., Blazquez, V., & Oh, T. (2024) · Behavioral Sciences · doi:10.3390/bs14111035 · Cited by 91 · see citing works
Who Is AI Replacing? The Impact of Generative AI on Online Freelancing Platforms
Demirci, O., Hannane, J., & Zhu, X. (2025) · Management Science · doi:10.1287/mnsc.2024.05420 · Cited by 86 · see citing works
Conceptualizing generative AI as style engines: Application archetypes and implications
Riemer, K., & Peter, S. (2024) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2024.102824 · Cited by 63 · see citing works
Generative AI in the Wild: Prospects, Challenges, and Strategies
Sun, Y., Jang, E., Ma, F., & Wang, T. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642160 · Cited by 64 · see citing works
Lubbe, A., Marais, E., & Kruger, D. J. D. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13476-x · Cited by 90 · see citing works
Communications of the Association for Information SystemsPreprint
Marabelli, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5250701 · Cited by 59 · see citing works
Fouad, A. M., Salem, I. E., & Fathy, E. A. (2024) · Tourism and Hospitality Research · doi:10.1177/14673584241293125 · Cited by 76 · see citing works
“According to Brynjolfsson et al. (2023) and Jo (2023), GAI is founded on the creation of new experiences and information.”
Towards a Definition of Generative Artificial Intelligence
Ronge, R., Maier, M., & Rathgeber, B. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00863-y · Cited by 75 · see citing works
“These three aspects, combined with the new capabilities of GenAI, may enable productivity gains (with varying intensity) in interaction with GenAI for a variety of sectors (Brynjolfsson et al., 2023) and almost any field of work “from banking to life sciences” (Chui et al., 2023).”
Systematic literature review on bias mitigation in generative AI
Afreen, J., Mohaghegh, M., & Doborjeh, M. (2025) · AI and Ethics · doi:10.1007/s43681-025-00721-9 · Cited by 66 · see citing works
Al-kfairy, M. (2025) · IEEE Engineering Management Review · doi:10.1109/emr.2025.3534034 · Cited by 61 · see citing works
“Support structures such as training sessions and technical support are crucial for helping employees adapt to new tools,increasing organizational buy-in and facilitating the effective leverage of AI’s capabilities [44].”
Generative artificial intelligence in graduate medical education
Janumpally, R., Nanua, S., Ngo, A., & Youens, K. E. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2024.1525604 · Cited by 65 · see citing works
Bienefeld, N., Keller, E., & Grote, G. (2024) · Journal of Medical Internet Research · doi:10.2196/50852 · Cited by 74 · see citing works
Aldasoro, I., Armantier, O., Doerr, S., et al. (2024) · Economics Letters · doi:10.1016/j.econlet.2024.111814 · Cited by 56 · see citing works
Public perception of generative AI on Twitter: an empirical study based on occupation and usage
Miyazaki, K., Murayama, T., Uchiba, T., An, J., & Kwak, H. (2024) · EPJ Data Science · doi:10.1140/epjds/s13688-023-00445-y · Cited by 62 · see citing works
Copiloting the future: How generative AI transforms Software Engineering
Banh, L., Holldack, F., & Strobel, G. (2025) · Information and Software Technology · doi:10.1016/j.infsof.2025.107751 · Cited by 66 · see citing works
Empowering generative AI through mobile edge computing
Ale, L., Zhang, N., King, S. A., & Chen, D. (2024) · Nature Reviews Electrical Engineering · doi:10.1038/s44287-024-00053-6 · Cited by 54 · see citing works
“…marketing, it could simplify dynamic customer engagement 67 ; in medicine, it could facilitate advanced diagnostic tools 6,68 ; in education, it could provide personalized learning experiences 69 ; and in daily work assistance, it could enhance productivity through intelligent task management 70 .”
Ulfsnes, R., Moe, N. B., Stray, V., & Skarpen, M. (2024) · Generative AI for Effective Software Development · doi:10.1007/978-3-031-55642-5_10 · Cited by 62 · see citing works
Kaplan, T. T., & Cankar, M. (2024) · Dental Traumatology · doi:10.1111/edt.12999 · Cited by 55 · see citing works
Global water scarcity footprint by mineral
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/62d19e32-en · Cited by 49 · see citing works
Breakdown of working population by type of occupation in Southern Africa, 2021
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/da1c9bf1-en · Cited by 49 · see citing works
Vulnerable employment, labour productivity and education spending in West Africa, 2000-22
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/8451ad35-en · Cited by 49 · see citing works
Policies for productive and inclusive skills development in Africa
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/0abbeb17-en · Cited by 50 · see citing works
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/603e54b5-en · Cited by 50 · see citing works
Vulnerable employment, labour productivity and education spending in Southern Africa, 2000-22
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/d0e983cf-en · Cited by 49 · see citing works
Employment by education level in mining and mining-related manufacturing in South Africa
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/4d4c0e28-en · Cited by 49 · see citing works
Breakdown of working population by type of occupation in North Africa, 2021
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/9e545b33-en · Cited by 49 · see citing works
Skills for renewable energies in North Africa
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/184b6873-en · Cited by 49 · see citing works
Vulnerable employment, labour productivity and education spending in East Africa, 2000-22
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/662cc03d-en · Cited by 49 · see citing works
Skills for mining in Southern Africa
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/2c2bb158-en · Cited by 49 · see citing works
Export of services in information and communications technology by selected East African countries
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/c7de3aaf-en · Cited by 49 · see citing works
Migrants by level of education, origin and destination (East Africa, 2020)
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/69884fbf-en · Cited by 48 · see citing works
Average years of schooling and learning-adjusted years of schooling in Southern Africa, 2020
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/c7fff4c9-en · Cited by 48 · see citing works
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/e1f48c16-en · Cited by 48 · see citing works
Migrants by level of education, origin and destination (Central Africa, 2020)
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Average years of schooling and learning-adjusted years of schooling in East Africa, 2020
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/c7d8f102-en · Cited by 48 · see citing works
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/1245413f-en · Cited by 48 · see citing works
Breakdown of working population by type of occupation in West Africa, 2021
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/52c5da51-en · Cited by 48 · see citing works
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/b1826e51-en · Cited by 48 · see citing works
Goh, A. Y. H., Hartanto, A., & Majeed, N. M. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100845 · Cited by 65 · see citing works
Trust in AI and Top Management Support in Generative-AI Adoption
Korzyński, P., Silva, S. C. L. D. C. E., Górska, A. M., & Mazurek, G. (2024) · Journal of Computer Information Systems · doi:10.1080/08874417.2024.2401986 · Cited by 61 · see citing works
“For example, Brynjolfsson, Li and Raymond 5 found that the utilization of generative AI technology leads to a notable 14% enhancement in productivity levels.”
Babšek, M., Ravšelj, D., Umek, L., & Aristovnik, A. (2025) · AI · doi:10.3390/ai6030044 · Cited by 78 · see citing works
“Another sub-technology of AI is generative AI, which can generate content such as text, images, music, or videos by analyzing patterns in existing data [40].”
Trusting in Generative AI: Catalyst for Employee Performance and Engagement in the Workplace
Marimón, F., Mas‐Machuca, M., & Akhmedova, A. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2388482 · Cited by 67 · see citing works
“Brynjolfsson et al. (2023) come to the same conclusions about the increase of productivity especially of the lower-skilled workers and suggests improvements in worker learning and increases in employee retention levels that occur thanks to the use of GenAI.”
InfluentialKuang, E., Li, M., Fan, M., & Shinohara, K. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642168 · Cited by 49 · see citing works
Ma, Y., Zheng, S., Yang, Z., Pan, H., & Hong, J. (2025) · International Journal of Production Research · doi:10.1080/00207543.2025.2472298 · Cited by 60 · see citing works
Nyberg, A. J., Schleicher, D. J., Bell, B. S., Boon, C., Cappelli, P., Collings, D. G., Dalle Molle, J. E., Feuerriegel, S., Gerhart, B., Jeong, Y., et al. (2025) · Journal of Management · doi:10.1177/01492063251325188 · Cited by 47 · see citing works
Sidra, S., & Mason, C. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2543997 · Cited by 70 · see citing works
“Consequently, we developed five self-report items to assess the range of benefits that have been documented for collaborative AI tools (Brynjolfsson et al., 2023; Choi & Schwarcz, 2025; Dell ’ Acqua et al., 2023; Noy & Zhang, 2023; Zhang et al., 2022).”
The impact of AI on education and careers: What do students think?
Thomson, S. R., Pickard-Jones, B., Baines, S., & Otermans, P. C. J. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1457299 · Cited by 43 · see citing works
Generative AI in Marketing: Promises, Perils, and Public Policy Implications
Kumar, V., Kotler, P., Gupta, S., & Rajan, B. (2024) · Journal of Public Policy & Marketing · doi:10.1177/07439156241286499 · Cited by 61 · see citing works
“These articles have explored GAI ’ s use in understanding labor productivity (Brynjolfsson, Li, and Raymond 2023), uncovering the prospects and challenges in multiple disciplines (Ooi et al. 2023), managing content marketing (Wahid, Mero, and Ritala 2023), improving marketing functions (Thakur and…”
Jetha, A., Bakhtari, H., Rosella, L. C., Gignac, M. A. M., Biswas, A., Shahidi, F. V., Smith, B. T., Smith, M. J., Mustard, C., Khan, N., et al. (2023) · American Journal of Industrial Medicine · doi:10.1002/ajim.23517 · Cited by 39 · see citing works
The Crowdless Future? Generative AI and Creative Problem Solving
Boussioux, L., N. Lane, J., Zhang, M., Jacimovic, V., & Lakhani, K. R. (2023) · doi:10.2139/ssrn.4533642 · Cited by 343 · see citing works
Winners and losers of generative AI: Early Evidence of Shifts in Freelancer Demand
Teutloff, O., Einsiedler, J., Kässi, O., Braesemann, F., Mishkin, P., & del Rio-Chanona, R. M. (2025) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2024.106845 · Cited by 39 · see citing works
The use of LLMs to annotate data in management research: Foundational guidelines and warnings
Carlson, N. A., & Burbano, V. (2025) · Strategic Management Journal · doi:10.1002/smj.70023 · Cited by 52 · see citing works
Liu, X., & Li, Y. (2025) · Behavioral Sciences · doi:10.3390/bs15020206 · Cited by 58 · see citing works
“Brynjolfsson et al. (2023) also found that adoption of suggestions provided by generative AI significantly contributes to continuous skill enhancement among workers.”
Generative AI Meets Open-Ended Survey Responses: Research Participant Use of AI and Homogenization
Zhang, S., Xu, J., & Alvero, A. (2025) · Sociological Methods & Research · doi:10.1177/00491241251327130 · Cited by 48 · see citing works
The Paradigm Shifts in Artificial Intelligence
Dhar, V. (2024) · Communications of the ACM · doi:10.1145/3664804 · Cited by 45 · see citing works
Generative Artificial Intelligence Literacy: Scale Development and Its Effect on Job Performance
Xin, L., Zhang, L., & Wei, X. (2025) · Behavioral Sciences · doi:10.3390/bs15060811 · Cited by 71 · see citing works
Humanizing GenAI at work: bridging the gap between technological innovation and employee engagement
Manresa, A., Sammour, A., Mas‐Machuca, M., Chen, W., & Botchie, D. (2024) · Journal of Managerial Psychology · doi:10.1108/jmp-05-2024-0356 · Cited by 49 · see citing works
AI in Dental Radiology—Improving the Efficiency of Reporting With ChatGPT: Comparative Study
Stephan, D., Bertsch, A., Burwinkel, M., Vinayahalingam, S., Al‐Nawas, B., Kämmerer, P. W., & Thiem, D. G. E. (2024) · Journal of Medical Internet Research · doi:10.2196/60684 · Cited by 37 · see citing works
Exploring the Impact of ChatGPT on Business School Education: Prospects, Boundaries, and Paradoxes
Vâlcea, S., Hamdani, M. R., & Wang, S. (2024) · Organizational Behavior Teaching Review · doi:10.1177/10525629241261313 · Cited by 36 · see citing works
“LLMs generate coherent, grammatically accurate sentences by learning to predict words based on the statistical representation of the co-occurrence patterns in human-produced text (Brynjolfsson et al., 2023).”
Mobilising new frontiers in digital transformation research: A problematization review
Ashrafi, A., Constantinides, P., Mehandjiev, N., & Thatcher, J. B. (2025) · Information Systems Journal · doi:10.1111/isj.12531 · Cited by 33 · see citing works
A Critical Analysis of Generative AI: Challenges, Opportunities, and Future Research Directions
Uddin, M., Laghari, S. U. A., Alanazi, F., Hussain, S., Mazhar, T., & Rahman, M. A. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10355-z · Cited by 47 · see citing works
Canagasuriam, D., & Lukacik, E. (2024) · International Journal of Selection and Assessment · doi:10.1111/ijsa.12491 · Cited by 33 · see citing works
AI emerges as the frontier in behavioral science
Meng, J. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2401336121 · Cited by 31 · see citing works
“More broadly, in the labor market, LLM can significantly narrow the performance gap between lay - men and experts, making opportunities more equal (17).”
Background
- Authors
- Brynjolfsson, E., Li, D., & Raymond, L.
- Affiliations
- National Bureau of Economic Research (US)
- Venue
- National Bureau of Economic Research
- Publisher
- National Bureau of Economic Research
- Published
- 2023-04
- OpenAlex
- W4366817968
- Cited by
- 995
- References
- 61
- Citations by year
- 2026: 287 · 2025: 301 · 2024: 352 · 2023: 51 · 2022: 1 · 2021: 1 · 2019: 1
- Type
- report
- Language
- en
- Open access
- green
- doi.org
- Locations
- National Bureau of Economic Research (publishedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Labor market dynamics and wage inequality
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Keywords
- generative AI, chatbots, productivity, employee experience, tacit knowledge, knowledge transfer, employee retention, customer sentiment, management interventions, artificial intelligence adoption
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read (extract-v1) on 2026-09-25.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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