Peyman Shahidi

AI agents—autonomous systems that perceive, reason, and act on behalf of human principals—are poised to transform digital markets by dramatically reducing transaction costs. This chapter evaluates the economic implications of this transition, adopting a consumer-oriented view of agents as market participants that can search, negotiate, and transact directly. From the demand side, agent adoption reflects derived demand: users trade off decision quality against effort reduction, with outcomes mediated by agent capability and task context. On the supply side, firms will design, integrate, and monetize agents, with outcomes hinging on whether agents operate within or across platforms. At the market level, agents create efficiency gains from lower search, communication, and contracting costs, but also introduce frictions such as congestion and price obfuscation. By lowering the costs of preference elicitation, contract enforcement, and identity verification, agents expand the feasible set of market designs but also raise novel regulatory challenges. While the net welfare effects remain an empirical question, the rapid onset of AI-mediated transactions presents a unique opportunity for economic research to inform real-world policy and market design.

We study the automation and productivity effects of AI when production is a sequence of interdependent steps with a Leontief structure. Each step can be executed manually by labor, augmented with AI, or automated within contiguous AI-executed steps called “chains.” By characterizing the firm’s cost-minimizing assignment of steps to humans and AI, we show that chaining can overturn comparative advantage logic, since human-advantaged steps may be optimally assigned to AI as part of a chain. Chaining also makes gains from automation depend on how AI-exposed steps cluster in the workflow beyond just how many there are, and can make the returns to improving AI quality non-monotone in a pattern resembling the productivity J-curve. Empirically, we show that existing automation patterns are consistent with the model’s predictions: a step is more likely to be AI-executed when its neighbors are, and AI execution is more common where AI-exposed steps cluster in the workflow.

In a global labor market, changes in relative exchange rates between worker and employer countries could affect labor supply and demand. This novel confluence of topics from international and labor economics is explored using an online labor market and the sharp devaluation of the ruble in 2014. This devaluation increased the on-platform real returns to Russians, as contracts in this market are dollar-denominated. Russian hours-worked increased substantially, primarily through the extensive margin. There was minimal pass-through of the ruble price changes to dollar-denominated wages. To “obtain” more hours, Russians increased their job search intensity, albeit with some reduced efficiency.

I quantitatively study the dynamics of online taxi drivers’ labor supply decisions by exploiting two unique ride dispatching characteristics of Tapsi, an Iranian ride-hailing company. I model Tapsi’s ride acceptance environment for drivers within a continuous-time search framework. I show how drivers’ subjective beliefs about fairness of ride prices, what I call the “experience” component, will affect their ride acceptance behavior. I use Tapsi’s surge pricing mechanism in a regression discontinuity design to decompose the effect of exogenous price shocks on drivers’ labor supply into two components: (1) pure price effect, and (2) experience effect. This decomposition allows me to address the intrinsic endogeneity in taxi drivers’ labor supply elasticity measurement by separating the pure effect of price shocks from the endogenous component whereby drivers tend to self-select themselves into areas with more favorable earning conditions. In simulations, I show how this decomposition explains heterogeneous responses to similar ride offers not only from different drivers but also from the same driver at different times.