Making Sense of Chaos: Why Complexity Economics Illuminates Today’s AI Investing

A guest post by Dr. Cecilia Dones

You can follow Dr. Cecilia Dones on her substack here. Or connect with her on LinkedIn.

As AI steadily moves from promise to pervasive reality, its impact on the economy and the way investors understand risk grows more intricate. While I am not an investor myself, I am immersed in the AI space, driven by a deep curiosity about how its maturation reshapes the ways people perceive, interact, and make decisions in society. I offer this perspective not to forecast markets, but to highlight how complexity economics can help us notice and wrestle with the subtler dynamics at work. This is especially important as artificial intelligence rewires our assumptions.

Context and History: Complexity Economics Comes of Age

Traditional economics often focuses on representative agents and equilibrium. But over the past several decades, a community of researchers who sought to build on insights from computation, physics, and behavioral sciences has foregrounded the study of complexity.

Thought leaders like J. Doyne Farmer, whose new book Making Sense of Chaos: A Better Economics for a Better World synthesizes years of research, argues convincingly that the economy is less a machine and more a living system, full of diverse actors, network effects, and unpredictable feedback loops.

One crucial insight from complexity economics is the outsized impact of initial conditions. In complex systems, small differences at the start—such as who gains early access to a technology, or how data sets are formed—can snowball into significant and sometimes irreversible consequences. As AI platforms mature and penetrate different sectors, these early choices and distributions matter enormously, often shaping competitive landscapes, regulatory responses, and patterns of social adoption for years to come.

Why Now? The Maturing of AI and Shifting Risk

AI's increasing maturity is becoming a fundamental part of how decisions at all levels get made. This transformation raises new questions for investors. The challenge is not the mere presence of risk—which investors are always attuned to—but the difficulty of tracking how risks move, interact, and sometimes amplify through densely connected systems.

Widespread dependence on shared AI infrastructure, a handful of dominant platforms, and the possibility for rapid feedback and propagation mean that minor shocks can cascade across portfolios and sectors. These features reflect the complex, nonlinear behaviors that complexity economics seeks to understand and that matter acutely to anyone assessing systemic risk.

Broader Societal Implications

As AI matures, it's not just financial systems that are affected. The ways people perceive information, interact with platforms, and even relate to one another are shifting. Transparency, accountability, and bias in AI-driven models all carry consequences in multiple dimensions: economic, social, and ethical. The ripple effects reach far beyond investor returns, influencing trust in institutions and shaping power relations.

Reflective Questions for Navigating Complexity

To make systemic interdependencies and ethical dimensions more visible, I suggest a few questions adapted from complexity economics and ongoing conversations among AI researchers and policy thinkers:

Systemic Interdependencies

• How do minor early choices in technology deployment shape long-term patterns of adoption and competition?

• Where are the hidden points of overlap or reliance between shared platforms, data sources, or supply chains across investments?

• Can small shocks propagate in unpredictable ways, amplifying risks through networks rather than remaining isolated incidents?

Ethical and Societal Considerations

• Is there transparency and accountability in how AI systems are developed and used?

• Who benefits most and least from AI adoption in my areas of interest?

• Are mechanisms in place to address and learn from unintended biases, errors, or social harms?

Resilience and Flexibility

• How often do I revisit assumptions about technologies or markets in light of new data and evolving trends?

• Is there a system for ongoing learning, adaptation, and ethical review in the investments or platforms I support?

An Invitation to Deeper Inquiry

As complexity economics shows—and as Farmer's work in Making Sense of Chaos illustrates—the patterns shaping the economy and our society are dynamic, contingent, and routinely surprising. Initial conditions matter. Systemic connections matter. And as artificial intelligence matures, so do the questions we must ask about risk, value, and impact.

My hope is that, whether you are an investor or simply a participant in the AI-driven world, this lens provokes a deeper inquiry into not just what is likely to happen, but why. The choices we make today about technologies have an influence on our future possibilities.