AI's New Role: From Tech to Economic Player?

Traditional economic models often treat AI as a tool that enhances efficiency by cutting costs or improving human decision-making. This is because traditional AI primarily focuses on specific tasks, such as data analysis or automation. However, Generative AI, particularly Large Language Models (LLMs), can generate original content, possessing their own information sets, preferences, and constraints. This allows Generative AI to act as a virtual consultant with the ability to assist, analyze, and strategize, thus, leading economists to model it as an economic agent rather than just a technology. This shift introduces complexities like agency, objectives, and a limited view, which are not considered in traditional AI models.

Modeling Generative AI as an economic agent highlights several key characteristics. First, AI agents have their own information set derived from training data, prompts, and external sources, which may differ from what human users possess. Second, agency is present, where the AI provides advice, but the user retains decision-making power, with AI recommendations significantly influencing those decisions. Third, AI agents are shaped by objectives and constraints ingrained during training, fine-tuning, and orchestration, leading them to act as though maximizing implicit preferences. Finally, an AI's view is often limited to its interactions with the user, influencing its preferences based on communication transcripts rather than real-world outcomes.

An AI agent's 'information set', derived from its training data, prompts, and external sources, often differs from a human analyst's. The Generative AI, especially LLMs, can have a richer or more comprehensive view of data than a human, giving it a potential advantage in identifying market trends or patterns. Unlike human analysts who rely on their experiences and often limited data access, AI can process vast datasets, enabling it to offer insights that may be unavailable to human consultants. This difference in the information set can lead to varying decision-making approaches and outcomes, requiring users to carefully consider the AI's data sources and the potential for biases or inaccuracies.

The objectives and constraints of an AI agent, ingrained during training, fine-tuning, and orchestration, significantly influence its interactions within economic systems. These objectives, which are like implicit preferences, drive the AI to act in ways that maximize those preferences. However, these objectives may not always align with the human user's goals, which could lead to surprising or counterintuitive results. The constraints also limit the AI's actions, determining what it can and cannot do. For example, an AI might prioritize specific metrics due to its training, potentially leading to different recommendations than a human consultant who might consider a broader range of factors. Understanding these objectives and constraints is crucial for predicting AI's behavior and ensuring that its recommendations align with desired outcomes.

Modeling AI as an economic agent brings forth several critical questions and challenges. It prompts inquiries into how these AI agents affect market equilibrium and overall welfare. There is a need to understand the implications for fairness and the potential for amplifying existing biases within AI systems. Furthermore, there are questions about designing AI systems and platforms to align with human values and promote beneficial outcomes. Addressing these challenges will require collaborative efforts between economists, computer scientists, and policymakers to navigate the evolving landscape of AI and its impact on society, ensuring that AI serves as a force for good within the economy.