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AI As a Catalyst For a Circular Economy

Updated: Mar 31

Farouk Adeleke



Following the end of the UN 28th Conference of Parties (COP28), representatives from major tech companies such as Microsoft and Salesforce met with the UNESCO Business Council as part of its series on artificial intelligence (AI) and ethics. This event makes up one of five discussions led by the council to consider the goals of AI technology in a sustainable economy. Critics of emerging automation trends are cautious about adopting AI as a sustainability tool. However, a retroactive approach to the issue highlights the role of AI in streamlining a circular economy, notably in material efficiency and product utilization.

What is a circular economy and why is a swift transition to one necessary? In most economic paradigms, value diminishes after the initial use of a product. This loss represents a linear way of consumption and production, shown by a "take-make-dispose" model that leads to resource depletion and waste accumulation. A circular economy highlights the importance of regenerating natural systems to aid sustainability and capture value from recycled materials. For example, in 2021, the combined worth of imports and exports of raw materials between the EU and other nations hit €178 billion. With exports at €71.3 billion falling short of imports at €106.8 billion, a trade deficit formed, amounting to €35.5 billion. In a circular system, the reliance on imported raw materials lessens significantly, fostering a more robust economy. 

In a linear economy, product utilization follows a single cycle of consumption, leading to a used product being unable to contribute to the economy. Luckily, the accuracy of predictive models and LLMS (Large Language Models) are becoming increasingly pinpoint. They enhance the competitive advantage of current circular economy models and can even suggest new ones. Through a mix of real-time and historical data, AI can enhance product utilization through predictive analysis of patterns in consumer trends and behaviors. 

A remarkable strategy for Apple’s iPhone follows an intuitive approach to battling linearity in its products. Today, the iPhone dominates 80% of the secondhand market, making up about 300 million phones. Marcelo Claure, former CEO of Sprint, attributes this success to the robustness of the iOS operating system. Through never-ending updates, Apple ensures users can access the app ecosystem and new features, regardless of their device. Additionally, advancements in data and tracing technology have improved information flow, enhancing this business model. With this trajectory, we can anticipate increased residual value and utilization of used products. 

Material efficiency means maximizing the value derived from production materials, benefiting both the environment and the producer. While this is nothing new to engineers with years of experience in manufacturing, there is a level of untapped potential when optimizing the composition of a product to fit the circular economy. These issues necessitate sophisticated, technology-driven strategies like integrating machine learning (ML) and LLMS. Better efficiency, creativity, and alignment with environmental responsibility and sustainability are all potential benefits of this integration. Moreover, it has the potential to completely transform the field of material science and create novel ideas for industries such as oil and gas, which supports 10.3 million jobs in the United States and makes nearly 8 percent of our nation’s Gross Domestic Product

In an ever-changing world where the effects of AI in an expanding economy are unsure, we can take what we know works and mold it into a lasting solution. Instead of remaining trapped in the frustrations of the present, we should rethink and redesign our future using the tools we have garnered over centuries of human ingenuity.

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