Is Shared Computing The Key To Unlocking Better Economic Policies?

Donald Trump has recently imposed new tariffs on goods entering the US from Canada, Mexico and China. While some economic analysis has been done to understand the broader impact not just on the price of goods but also industries like automotive sector, and countries more broadly, those run the risk of not capturing all the ripple effects given the complexity of factors involved. AI models could equip households, businesses, and financial institutions with advanced analysis to anticipate and adapt to economic changes. By processing complex patterns across market behaviors, consumption trends, and financial flows, these systems can identify subtle signals and interconnections that traditional economic models often overlook. Tariffs, trade wars, and economic policies don't operate in a vacuum, they affect every layer of the economy, and every one of us. 

A digital replica of the entire U.S. economy, where some AI agents act as consumers, tweaking their spending habits in response to rising prices or shifting trends, could be the solution we need to start risk-adjusting our day-to-day financial decisions. Other agents could take on the role of businesses, strategizing their way through supply chain chaos or market fluctuations.

The building blocks to a new level of economic modeling are already there: advancements in High-Performance Computing (HPC), the integration of Large Language Models (LLMs), and the rise of AI agents are converging to create a new paradigm in economic analysis. Yet, we're still dragging our feet. It's time to recognize that shared computing is the foundation the agent-based economy needs to stop flying blind in an increasingly complex digital world.

The power of shared computing 

Recent studies, such as those highlighted by IEEE, demonstrate how HPC is revolutionizing Agent-Based Models (ABMs). These models simulate millions of agents, individuals, firms, or institutions interacting within intricate economic networks, resulting in unique insights into economic behaviors and policy impacts. For instance, HPC-enabled ABMs can simulate the impact of a new tax policy across an entire economy, capturing nuances that traditional models miss. Policymakers can use these simulations to avoid catastrophic decisions and craft strategies that actually work. The problem is, these models require immense computational power. Shared computing, with its ability to pool resources, is the only way to make this feasible on a large scale. Without it, we're stuck with oversimplified models that fail to reflect reality.

Smarter agents, better decisions 

The integration of LLMs into ABMs enables even more accurate simulations of economic activities by enhancing the realism of agent behaviors. It means agents don't just follow static rules but can adapt their decisions based on real-time data and contextual understanding. LLMs allow agents to "think" more like humans, making decisions that reflect the complexity of real-world economics. For example, they could analyze social media sentiment to help users decide whether to cut back on spending, considering other factors like job security and upcoming expenses. But again, such sophisticated analysis demands computational resources that only shared computing can provide. Trying to run these models on isolated systems is like trying to power a city with a single battery. 

The coordination challenge

As agents become increasingly integrated in our daily lives, whether it's to assist our financial decision making or assisting workplaces facing staff shortages, one big question remains overlooked: How should they collaborate—with us and with each other? They need to interact, learn, and adapt within a broader ecosystem. Shared computing provides the infrastructure for this interconnectedness, enabling AI agents to function as a cohesive unit rather than a collection of siloed tools. Without it, the potential of AI agents will remain untapped, and the agent-based economy will stall before it even takes off. The real challenge in AI right now isn't autonomy—it's coordination. Shared computing can play a role here too, by enabling transparent and auditable systems that hold AI agents accountable.

Shared computing is the answer to the computational power needed for advanced ABMs, the integration of LLMs, and the seamless operation of AI agents that will power the next wave of industry-defining solutions. As we're navigating uncertainty at levels not seen before in our economy, the time to act is now.