DeFi
The changing face of risk in DeFi
Decentralized finance (DeFi) is experiencing new momentum. Activity in new ecosystems and high returns resemble the famous DeFi Summer 2021. The variety of innovative protocols makes it incredibly difficult for investors to keep up, while at the same time the impressive growth raises concerns about risks that are accumulating in the DeFi ecosystem.
You may have heard the doomsday analyzes comparing the most successful protocols of this wave, like Ethena or the Eigen Layer LRT, with risk management disasters like Terra, without really providing credible evidence of the parallels. The fact is that this new generation of fast-growing DeFi protocols is much more mature and a lot of thought has gone into risk management. However, there are still many risks.
Jesus Rodriguez, CEO of IntoTheBlock, is a speaker on the AI Stage at Consensus 2024from May 29 to 31.
The biggest risk in today’s DeFi market lies not in mechanical failures like those that caused Terra to collapse, but rather in three key factors: scale, complexity, and interconnectivity.
The protocols in this DeFi wave have become quite large in a matter of months, they enable more complex financial primitives, and they are incredibly interconnected. This combination of complexity, size, and interconnectivity has significantly exceeded the capabilities of risk models in today’s DeFi market. Simply put, there are many risk conditions in today’s DeFi markets for which we do not have credible risk models. And this gap seems to be growing instead of narrowing.
Risk has been part of the DeFi narrative since the beginning, and it is very easy to discuss in broad, generic terms. This new era of DeFi brings new innovations and has grown considerably quickly. As a result, risk takes on a different connotation than before. Taking a first-principles approach to analyzing risk in the DeFi era highlights four fundamental factors: scale, speed, complexity, and interconnectivity.
To illustrate these factors, consider the differences in risk quantification for a basic AMM with a few hundred million TVL versus an AMM that uses reinvested assets with their corresponding points systems and introduces its own tokens and points. The first risk model can be solved with basic statistical or machine learning methods. The latter enters the realm of much more advanced branches of mathematics and economics such as complexity or chaos theory, which are far from being applied in DeFi.
Let’s look at the different factors in more detail.
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1) Scale
The principle of the relationship between risk and scale in DeFi is incredibly simple. In financial markets, modeling risk at a smaller scale, say a few hundred million, is very different from modeling risk at a few hundred billion. On a larger scale, risk conditions still appear that were not present on a smaller scale. This principle certainly applies to DeFi as a parallel financial system with many interconnected primitives.
Ethena is one of the most innovative projects in the current wave of DeFi and has attracted billions of TVL in just a few months. The biggest challenge for Ethena in today’s market is adapting its risk and insurance models to this scale in the event of negative financing rates for a long period of time.
2) Speed
The relationship between risk and speed is the traditional friction between growing too much and too fast. As a condition of risk, speed acts as an accelerator of evolution. A protocol that scales from a few million to a few billion TVL in just a few months may not have time to adjust its risk models to the new scale before unforeseen risk conditions arise.
The rapid rise of Clean diaper sparked a whole movement of LRTs, many of which reached several billion TVL in just a few months while still lacking basic features like withdrawals. The combination of speed and scale can exacerbate simple disaggregating conditions into truly impactful risk factors in some of these protocols.
3) Complexity
The entire field of complexity theory was born to study systems that escape the laws of predictive models. Economic risk has been at the center of complexity theory almost since its beginnings, as global economies rapidly outpaced risk models after World War II. Modeling risk in a simple economic system is simply simple.
In the new wave of DeFi, we have protocols such as Pendle or Gearbox, which abstract away quite sophisticated primitives such as yield derivatives and leverage. The risk models for these protocols are fundamentally more difficult than those of the previous generation of DeFi protocols.
4) Interconnectivity
Widely interconnected economic systems can be a nightmare from a risk perspective, as any situation can have many cascading effects. However, interconnectivity constitutes a natural step in the evolution of economic systems.
The current DeFi ecosystem is much more interconnected than its predecessors. We have takeover derivatives in EigenLayer that are tokenized and traded in pools in Pendle or used with leverage in Gearbox. The result is that risk conditions within a protocol can quickly seep into different key building blocks of the DeFi ecosystem, making risk models incredibly difficult to construct.
Hacks and exploits have been the dominant risk theme in DeFi in recent years, but that could start to change. The new generation of DeFi protocols is not only more innovative but also much more robust from a technical security point of view. Auditing firms have become smarter and protocols take security much more seriously.
As an evolving financial system, the risk of DeFi appears to be shifting from technical to economic. Rapid, large-scale growth speed, increasing complexity, and deep interconnectivity are moving DeFi into unforeseen territories from a risk perspective. With only a handful of companies working on risk in DeFi, the challenge now is to catch up.