The arrival of a GPT leads to an increase in workplace automation, which has two effects on labor. First, automation substitutes labor, a process historically confined to tasks that can be fully formalized and codified. An example is office computing, which performs tasks by following a set of pre-specified rules, a limitation that does not apply to generative AI.9 Second, automation complements labor by augmenting human skills that involve problem-solving, adaptability, and creativity.10 In decision-making, office computing complements labor by making information available, but it is left to humans to find associations, draw inferences, and exercise discretion.
Generative AI has the potential to substitute labor in tasks related to decision preparation and to complement labor in the task of decision-making. LLMs can be employed to prepare the body of knowledge relevant to a decision, be it related to an underwriting submission, a claim, or an insurance contract, and then to engage the decision-maker in a conversation with this corpus.11 Generative AI can make associations and draw inferences, which enables these systems to deliver draft decisions in situations that generalize beyond the data they have been trained on.12
Over the past two centuries, as automation has increasingly substituted labor in routine tasks, humans have allocated an increasing share of their working time to decision-making. The result is greater scarcity of labor at high skill levels, the skills for which are acquired through academic education, professional training, and industry experience. It has been posited that generative AI may reduce this scarcity by moving decision making to less highly skilled labor, reallocating talent to decision-making from decision support.13 This way, generative AI has the potential to lessen the shortage of high-skilled talent.
At the level of the insurance company, the arrival of a GPT initiates a process of discovery for productive use. The ability of generative AI to perform tasks heretofore the domain of human intelligence suggests opportunities for task-level substitution. Although generative AI can deliver productivity benefits at the task level, the value of a GPT derives primarily from system-level substitution. A defining characteristic of a GPT is that it complements innovation while increasing the financial return on developing new production processes, organizational structures, and products. This process of co‑invention creates a feedback loop between the downstream application of the GPT and the technology itself. The feedback loop unfolds over an extended period, as demonstrated by the adoption of electricity, and more recently office computing.14
The arrival of a GPT has the potential to increase firm heterogeneity. There is little predictability of how a new GPT gets adopted, and firms may differ in their paths to discovery. Also, the costs and benefits of co‑invention may vary across firms in the industry owing to differences in organizational structure.15 An empirical study on the adoption of Human Capital Management (HCM) software by corporations demonstrates the role of complementarities and the importance of co‑invention.16 Analyzing firm-level data on the implementation of HCM, incentive systems, and practices related to Human Resources (HR) analytics, the study documents that firms tend to have a higher rate of HCM software adoption when they have also implemented performance pay and HR analytics practices. The analysis shows that the adoption of HCM is linked to significant productivity gains when deployed as part of a comprehensive system of organizational incentives but less so when implemented stand‑alone.”
The market for insurance balances consumer demand for coverage with supply. The resulting prices and quantities determine in important ways the social value that the insurance industry generates. Automated, algorithmic decision-making on the part of insurers has the potential to adversely affect the nature of competition and the resultant market outcome. This can happen when pricing algorithms tacitly collude across competitors by factoring in each other’s pricing behavior. The discourse on the effect of algorithmic pricing on market outcome extends beyond specific industries and is reflected in several discussion papers issued by government agencies.17
A team of economists conducted an empirical study of the effect of algorithmic pricing on market outcome. The study subject is the German retail gasoline market where software for automated pricing became widely available in 2017.18 The retail market for gasoline is local in nature. In seeking an alternative to the closest station, consumers must overcome distance and in doing so, incur travel costs. These travel costs lend gas stations a degree of market power in geographic areas served by a single gas station (monopoly) or two gas stations near one another but distant from all other stations (duopoly). The study finds that algorithmic pricing increases margins in duopolistic and (previously) competitive markets—in these markets, algorithms respond to each other’s pricing decisions. No increase in margin was observed in monopolistic markets, where no such interaction occurs. Although these empirical findings do not generalize beyond the German retail gasoline market, the study shows that algorithmic pricing has the potential to change the market outcome, potentially in an adverse way.
As MIT economist David Autor observed, “[s]ocietal adjustments to earlier waves of technological advancement were neither rapid, automatic, nor cheap. But they did pay off handsomely.”19
Historical experience suggests that the successful adoption of generative AI is a purposeful undertaking that requires an investment and demands time. Further, analyses of previous arrivals of GPTs indicate that the bulk of productivity gains delivered by generative AI can be expected to originate in redesigned workflows rather than task-level improvements.