Jeremy Yang: How AI Agents Reshape Knowledge Work (SALT & Talk Speaker Series)

Talk abstract

Frontier AI systems are bridging the gap between intelligence and utility by shifting from conversational assistants to autonomous agents that execute tasks end to end. Using data from Perplexity’s Search and Computer products, we study this transition by examining how AI agents accelerate and reshape knowledge work. We adopt an individual-level task-based framework where agents have a higher fixed delegation cost but a lower marginal execution cost per step. This framework predicts that agent access expands the affordable task frontier toward weakly higher-value tasks and weakly increases realized value; when the pre-agent budget binds, surplus and the value-to-cost ratio also weakly increase. Turning to the data, we document three key empirical findings.

First, using matched session pairs with nearidentical initial queries as natural experiments for the same underlying task attempted with both products, Computer performs 26 minutes of autonomous work per user session, versus 33 seconds for Search. Computer automates task decomposition and execution that Search users might otherwise orchestrate and implement manually. As a result, Computer shifts the follow-up query distribution toward higher-order work such as verification and extension. Autonomy also increases execution quality, with per-query medium-to-high dissatisfaction rates 55% lower on Computer than on Search. Second, due to its autonomy advantage, Computer reduces completion time from 269 to 36 minutes on matched tasks, lowering estimated time and cost by 87% and 94%, respectively, compared to humans using Search alone. Third, Computer changes the scope of work that users attempt: Computer queries more often cross occupational boundaries, require higher-order cognition, and draw on broader expertise, take the form of composite tasks that bundle multiple subtasks into a single query, and unlock work activities that are essentially absent from Search usage among the same users. Together, the evidence indicates that AI agents accelerate workflows, enhance output quality, reduce costs, and expand the breadth and depth of automated work.