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AbstractArtificial intelligence (AI) may be the next general purpose technology. General purpose technologies, such as the steam engine and computing, can have an outsized impact on productivity through a positive feedback loop between producing and application industries. Along with the discussion of AI's potential to improve productivity come a number of policy concerns related to AI's potential to automate jobs and to create existential risk for humanity. Because of these worries, in March 2023, a widely circulated petition called for a pause in AI research. That letter asked several questions about AI's potential impact on society. This paper examines those questions through an economic lens. It highlights reasons to be optimistic about the long‐run impact of AI, while underscoring short‐run risks. Economic models provide an understanding of where the ambiguity lies and where it does not. Our models suggest no ambiguity on whether there will be jobs and little ambiguity on long‐term productivity growth if AI diffuses widely. In contrast, there is substantial ambiguity on the implications of AI's diffusion for inequality.

This paper explores how digitization and the associated use of customer data have affected the evolution of consumer privacy concerns. We measure privacy concerns by reluctance to disclose income in an online marketing research survey. Using over three million responses over eight years, our data show: (1) Refusals to reveal information have risen over time, (2) Older people are less likely to reveal information, and (3) The difference between older and younger people has increased over time. Our results suggest that the trends over time are partly due to broadening perceptions of the contexts in which privacy is relevant.

We examine US local telephone markets shortly after the Telecommunications Act of 1996. The data suggest that more experienced, better-educated managers tend to enter markets with fewer competitors. This motivates a structural econometric model based on behavioral game theory that allows heterogeneity in managers' ability to conjecture competitor behavior. We find that manager characteristics are key determinants in managerial ability. This estimate of ability predicts out-of-sample success. Also, the measured level of ability rises following a shakeout, suggesting that our behavioral assumptions may be most relevant early in the industry's life cycle. (JEL L96, L98, M10)

We report evidence that Bitnet adoption facilitated increased research collaboration between US universities. However, not all institutions benefited equally. Using panel data from seven top engineering journals, Bitnet connection records, and institution ranking data, we find that middle-tier universities were the primary beneficiaries; they benefited largely by increasing their collaboration with top-tier schools. Furthermore, we find that the magnitude of this effect is greatest for co-located pairs. Thus, the advent of Bitnet – and likely of subsequent networks – seems to have increased the role of middle-tier universities as producers of new knowledge in the national innovation system. (JEL D85, I23, O31, O33)

"The falling costs of collecting, storing, and processing data have allowed firms and governments to improve products and services. These same changes have also yielded databases containing detailed, individual-level data, raising privacy concerns. The Economics of Privacy summarizes the research on the economics of privacy, identifying open questions on the value of privacy; the roles of property rights and markets for privacy and for data; the relationship between privacy and inequality; and the political economy of privacy regulation. Across six chapters, several key themes emerge: It may not be possible to solve privacy concerns by creating a market for the right to privacy, even if property rights are well-defined and transaction costs are low. It is difficult to measure and to value the benefits of privacy, particularly when individuals have an intrinsic preference for privacy. Most previous attempts at valuation have focused only on quantifiable economic outcomes, such as innovation. Defining privacy through an economics lens is challenging: the broader academic and legal literature includes many distinct definitions of it, and different definitions may be appropriate in different contexts. The Economics of Privacy explores a variety of frameworks for examining these questions, offering a range of new perspectives on the role of economics research in understanding the benefits and costs of privacy and of data flows."

Abstract
Recent scholarship on artificial intelligence (AI) and international security focuses on the political and ethical consequences of replacing human warriors with machines. Yet AI is not a simple substitute for human decision-making. The advances in commercial machine learning that are reducing the costs of statistical prediction are simultaneously increasing the value of data (which enable prediction) and judgment (which determines why prediction matters). But these key complements—quality data and clear judgment—may not be present, or present to the same degree, in the uncertain and conflictual business of war. This has two important strategic implications. First, military organizations that adopt AI will tend to become more complex to accommodate the challenges of data and judgment across a variety of decision-making tasks. Second, data and judgment will tend to become attractive targets in strategic competition. As a result, conflicts involving AI complements are likely to unfold very differently than visions of AI substitution would suggest. Rather than rapid robotic wars and decisive shifts in military power, AI-enabled conflict will likely involve significant uncertainty, organizational friction, and chronic controversy. Greater military reliance on AI will therefore make the human element in war even more important, not less.