Search Results for « Generative AI »

Tom Freston is credited with saying “Innovation is taking two things that exist and putting them together in a new way”. For a long time in history, it has been the prevailing assumption that artistic, creative tasks such as writing poems, creating software, designing fashion, and composing songs could only be performed by humans. This assumption has changed drastically with recent advances in artificial intelligence (AI) that can generate new content in ways that cannot be distinguished anymore from human craftsmanship.

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.

Abstract We study the staggered introduction of a generative AI–based conversational assistant using data from 5,172 customer-support agents. Access to AI assistance increases worker productivity, as measured by issues resolved per hour, by 15% on average, with substantial heterogeneity across workers. The effects vary significantly across different agents. Less experienced and lower-skilled workers improve both the speed and quality of their output, while the most experienced and highest-skilled workers see small gains in speed and small declines in quality. We also find evidence that AI assistance facilitates worker learning and improves English fluency, particularly among international agents. While AI systems improve with more training data, we find that the gains from AI adoption are largest for moderately rare problems, where human agents have less baseline experience but the system still has adequate training data. Finally, we provide evidence that AI assistance improves the experience of work along several dimensions: customers are more polite and less likely to ask to speak to a manager.

Abstract The rapid and widespread diffusion of generative artificial intelligence (AI) has unlocked new capabilities and changed how content and services are created, shared, and consumed. This special issue builds on the 2021 Policy and Society special issue on the governance of AI by focusing on the legal, organizational, political, regulatory, and social challenges of governing generative AI. This introductory article lays the foundation for understanding generative AI and underscores its key risks, including hallucination, jailbreaking, data training and validation issues, sensitive information leakage, opacity, control challenges, and design and implementation risks. It then examines the governance challenges of generative AI, such as data governance, intellectual property concerns, bias amplification, privacy violations, misinformation, fraud, societal impacts, power imbalances, limited public engagement, public sector challenges, and the need for international cooperation. The article then highlights a comprehensive framework to govern generative AI, emphasizing the need for adaptive, participatory, and proactive approaches. The articles in this special issue stress the urgency of developing innovative and inclusive approaches to ensure that generative AI development is aligned with societal values. They explore the need for adaptation of data governance and intellectual property laws, propose a complexity-based approach for responsible governance, analyze how the dominance of Big Tech is exacerbated by generative AI developments and how this affects policy processes, highlight the shortcomings of technocratic governance and the need for broader stakeholder participation, propose new regulatory frameworks informed by AI safety research and learning from other industries, and highlight the societal impacts of generative AI.

What are the effects of recent advances in Generative AI on the value of firms?Our study offers a quantitative answer to this question for U.S. publicly traded companies based on the exposures of their workforce to Generative AI.Our novel firm-level measure of workforce exposure to Generative AI is validated by data from earnings calls, and has intuitive relationships with firm and industry-level characteristics.Using Artificial Minus Human portfolios that are long firms with higher exposures and short firms with lower exposures, we show that higher-exposure firms earned excess returns that are 0.4% higher on a daily basis than returns of firms with lower exposures following the release of ChatGPT.Although this release was generally received by investors as good news for more exposed firms, there is wide variation across and within industries, consistent with the substantive disruptive potential of Generative AI technologies.

This study assesses the potential global exposure of occupations to Generative AI, particularly GPT-4. It predicts that the overwhelming effect of the technology will be to augment occupations, rather than to automate them. The greatest impact is likely to be in high and upper-middle income countries due to a higher share of employment in clerical occupations. As clerical jobs are an important source of female employment, the effects are highly gendered. Insights from this study underline the need for proactive policies that focus on job quality, ensure fair transitions, and that are based on dialogue and adequate regulation.

Generative artificial intelligence (AI) is a potentially important new technology, but its impact on the economy depends on the speed and intensity of adoption.This paper reports results from a series of nationally representative U.S. surveys of generative AI use at work and at home.As of late 2024, nearly 40 percent of the U.S. population age 18-64 uses generative AI. 23 percent of employed respondents had used generative AI for work at least once in the previous week, and 9 percent used it every work day.Relative to each technology's first mass-market product launch, work adoption of generative AI has been as fast as the personal computer (PC), and overall adoption has been faster than either PCs or the internet.Generative AI and PCs have very similar early adoption patterns by education, occupation, and other characteristics.Between 1 and 5 percent of all work hours are currently assisted by generative AI, and respondents report time savings equivalent to 1.4 percent of total work hours.This suggests that substantial productivity gains from generative AI are possible.

Generative AI has taken the world by storm, kicked off for real by ChatGPT and quickly followed by further development and the release of GPT-4 and similar models from OpenAI's competitors. The street has most certainly found its use for generative artificial intelligence (AI), and there is no longer much point in discussing whether generative AI will be influential. It will, and what remains to be discussed it how influential it will be, and what potential harms arise when we use AI to generate text and other forms of content. Technological change entails societal change, and we must always endeavor to ask how new technologies shapes, engenders, or potentially erodes, the “good society”. In this sense, Generative AI is another instance of politically and culturally disruptive autonomous technology, and in this short commentary I highlight some of the key questions to be asked regarding consequences on the micro, meso, and macro level.