AWS Emerging Workloads & Technologies


What we are looking for

Company joining consortium as


Partners with expertise in

Circular Economy
We are looking to collaborate with consulting companies with subject matter expertise in Circular Economy to help us design the ABM model and validate its dynamics relative to real data.



AWS Emerging Workloads & Technologies





First name


Last name


Job title

Princial Circular Economy Specialist – AWS Advanced Computing – WWSO Emerging Techonologies


What we can offer

Consortium role as a


Our expertise

The AWS Emerging Workloads & Technologies (ET) team focuses on identifying and incubating new workloads that our customers need to solve their business problems and require a cross-AWS solution that will drive AWS revenue in 3-10 years. Within this remit, the ET team is focusing on use cases related to circular economy. Example workloads for Circular Economy that we engaged in 2023 include: 1/Molecular Design for per- and polyfluoroalkyl substances (PFAS) Destruction, which helps identify biochemical mechanisms to remove forever chemicals (such as PFAS) from the environment, 2/Computer Vision for sorting of products and material at end-of-life, which is crucial for industrializing the revalorization process of used products and materials. 3/ Circular Intelligence, an automated data ingestion and harmonization architecture for measuring Circular Economy KPIs for enterprises. In 2024, with a focus on GenAI, we are interested in collaborating to build circular economy agent based simulations where the agents themselves are Large Language Models tuned to circularity data. Agent-Based Models (ABMs) are powerful tools for understanding complex systems, such as those found in a circular economy. By simulating interactions between individual agents (which could be consumers, firms, regulators, etc.), ABMs can provide insights into the dynamics and emergent behaviors within the system. In the context of a circular economy, which emphasizes the reduction, reuse, and recycling of materials to minimize waste and resource extraction, ABMs can help answer a variety of important questions, including but not limited to: The goal is to have a Circular Economy assistant that is connected to simulation models that can leverage GenAI, powered by AWS Advanced Computing, to calculate and respond to difficult questions such as: 1. **Behavioral Responses**: How might different stakeholders (consumers, companies, regulators) react to policies or incentives aimed at promoting circular economy practices? ABMs can simulate scenarios to predict behavior changes and adoption rates of circular practices. 2. **Supply Chain Dynamics**: How does the adoption of circular economy practices affect supply chains? For example, what happens when a significant portion of materials for production comes from recycled sources? ABMs can help in understanding the resilience, efficiency, and adaptability of supply chains under such circumstances. 3. **Economic Impacts**: What are the macro- and micro-economic impacts of transitioning to a circular economy? ABMs can be used to analyze job creation, changes in market structures, and the economic viability of recycling and reuse businesses. 4. **Policy Evaluation**: What types of policies are most effective in promoting circular economy practices? By simulating different policy interventions (e.g., subsidies for recycled materials, taxes on waste production), ABMs can help in identifying the most promising strategies. 5. **Innovation Diffusion**: How do new technologies or business models that support circularity spread across different sectors and geographies? ABMs can track the diffusion of innovations and identify potential barriers or accelerants to their adoption. 6. **Environmental Outcomes**: How do various circular economy strategies contribute to environmental sustainability goals? For instance, ABMs can help quantify the potential reduction in carbon emissions or waste production resulting from specific circular practices. 7. **Social Impacts**: What are the social implications, such as changes in consumer behavior or impacts on local communities, of moving towards a circular economy? ABMs can explore these aspects by simulating interactions and feedback loops within the social fabric. 8. **Resource Flow Analysis**: How do materials and resources flow through an economic system under circular economy principles? ABMs can help in mapping out these flows and identifying points of inefficiency or potential innovation. 9. **Intersectoral Linkages**: How do different sectors of the economy (e.g., manufacturing, services, waste management) interact in a circular economy, and what are the implications of these interactions? ABMs can simulate interdependencies and synergies across sectors. By answering these and other questions, LLM enabled agent-based modeling can provide valuable insights that help policymakers, businesses, and other stakeholders make informed decisions to support the transition to a more circular economy.