AI's Economic Tipping Point: Will It Uplift Everyone or Just a Select Few?

Economists are adapting traditional models like the Cobb-Douglas production function to account for AI's unique characteristics. This involves creating AI production functions where AI development is tied to factors such as data, talent, computing power, time, and investment. While these modified functions face criticism for potential oversimplification, alternative approaches like Wardley Maps are also being explored to better describe and map technological capabilities. Trammell and Korinek's research focuses on AI's impact on output growth, wage growth, and the labor share. Integrating AI into international trade theory remains limited, although Hazari et al. have made an attempt using a Jones and Manuelli production function to introduce automation and AI effects into an international trade framework.

AI production functions are used to determine AI development by relating it to various inputs and indicators. These functions typically consider factors such as data, talent, compute (computing power), time, and investment as key determinants of AI progress. The idea is that the level of AI development is a function of the availability and quality of these inputs. While offering a structured way to analyze AI development, some argue that these functions may oversimplify the complex dependencies and interactions between these factors. The goal is to quantify the relationship between investments in these areas and the resulting advancements in AI capabilities.

Wardley Maps provide an alternative approach to understanding and mapping technological capabilities, which is particularly useful in the context of AI due to its complex and evolving nature. Unlike traditional economic models like the Cobb-Douglas production function, which focuses on quantifiable inputs and outputs, Wardley Maps use a three-step process: describing the case, defining technological capabilities, and ordering capabilities on a map. This helps to visualize the landscape of AI technologies, identify strategic opportunities, and understand the dependencies between different components. While traditional models aim to quantify economic impacts, Wardley Maps offer a more qualitative and strategic perspective on technology adoption and evolution.

AI has the potential to significantly impact global trade patterns through automation, increased efficiency, and the creation of new products and services. While integration of AI into international trade theory is limited, Hazari et al. (2022) have made a notable attempt by using a Jones and Manuelli production function to introduce automation and AI effects into an international trade framework. Further research is needed to understand how AI will reshape comparative advantages, affect trade flows, and influence the competitiveness of nations. The implications could be far-reaching, potentially leading to shifts in global supply chains and the emergence of new trade dynamics.

A calculated and deliberate approach to AI adoption is crucial to ensure that its benefits are shared more equitably because the transformative potential of AI could exacerbate existing inequalities and create new challenges for developing countries. Policymakers must prioritize AI safety, foster access to AI technologies for developing nations, and focus on augmenting human capabilities rather than simply replacing them. A slower, more controlled adoption allows for careful management of the AI revolution, enabling the harnessing of its power to create a more prosperous and inclusive future for all. Without this approach, there is a risk that the benefits of AI will be concentrated among a select few, widening the gap between developed and developing nations.