The Changing Face of Risk in DeFi

Decentralized finance (DeFi) is experiencing renewed momentum. The activity and high returns in new ecosystems resemble the infamous DeFi Summer of 2021. The diversity of innovative protocols makes it incredibly difficult for investors to keep up, while at the same time the impressive growth raises concerns about the risks accumulating in the DeFi ecosystem.

You may have heard doomsday analyzes comparing the most successful protocols of this wave, such as Ethena or Eigen Layer LRTs, to risk management disasters such as Terra, without actually providing reliable evidence of the parallels. The truth is that this rapidly growing new generation of DeFi protocols is much more mature and a lot of thought has been put into risk management. But there are still many risks.

Jesus Rodriguez, CEO of IntoTheBlock, is a speaker on the AI ​​Stage at Consensus 2024 May 29-31.

The biggest risk in the current DeFi market lies not in the mechanical failures that caused Terra to collapse, but in three key factors: scale, complexity, and interconnection.

The protocols in this DeFi wave have grown considerably in just a few months, enable more complex financial primitives, and are incredibly interconnected. This combination of complexity, size, and interconnections has vastly outpaced the capabilities of risk models in the current DeFi market. Simply put, there are many risk conditions in current DeFi markets for which we do not have reliable risk models. And that gap appears to be widening rather than narrowing.

Risk has been part of the DeFi narrative since the beginning, and it’s very easy to discuss it in broad, general terms. This new era of DeFi brings new innovations and has grown significantly fast. As a result, risk takes on a different meaning than before. Taking a first-principles approach to analyzing risk in this era of DeFi highlights four key factors: scale, speed, complexity, and interconnection.

To illustrate these factors, consider the differences in quantifying risk for an underlying AMM with a few hundred million in TVL and an AMM that uses restake assets with corresponding point systems and promotes its own tokens and points. The old risk model can be solved with basic statistical or machine learning methods. The latter falls within the domain of much more advanced branches of mathematics and economics, such as complexity or chaos theory, which cannot be implemented in DeFi at all.

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. Modeling risk in financial markets on a smaller scale, say a few hundred million, is very different from a few hundred billion. On larger scales, risk conditions always surface that are not present on smaller scales. This principle certainly applies to DeFi as a parallel financial system with many interconnected principles.

Ethena is one of the most innovative projects of the current DeFi wave and has attracted billions of TVL in just a few months. The biggest challenge for Ethena in the current market is to adapt its risk and insurance models to scale if funding rates remain negative for a long time.

2) Speed

The relationship between risk and speed is the traditional friction between growing too fast. Speed ​​as a risk condition acts as a scaling accelerator. A protocol that goes from several million to several billion in TVL in just a few months may not have time to adjust risk models to the new scale before unforeseen risk conditions arise.

The rapid rise of EigenLayer triggered the movement of entire LRTs; Some of them reached billions of dollars in TVL in just a few months and still lacked basic functions such as withdrawals. The combination of speed and scale can turn simple depegging conditions into truly effective 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 early days, as world economies rapidly outgrew risk models after World War II. Modeling risk in a simple economic system is quite simple.

In the new DeFi wave, we have protocols like Pendle or Gearbox that abstract quite complex primitives like yield derivatives and leverage. Risk models for these protocols are fundamentally more difficult than for previous generations of DeFi protocols.

4) Interconnection

Widely interconnected economic systems can be a nightmare from a risk perspective; because any given situation can have multiple cascading effects. But interconnection is a natural step in the evolution of economic systems.

The current DeFi ecosystem is much more interconnected than its predecessors. We have re-equity derivatives that are tokenized on EigenLayer and traded in pools on Pendle or leveraged on Gearbox. As a result, risk conditions in a protocol can quickly permeate different fundamental building blocks of the DeFi ecosystem, making risk models incredibly difficult to build.

Hacks and exploits have been the dominant risk theme in DeFi for the past few years, but that may be starting to change. The new generation of DeFi protocols are both more innovative and much more robust in terms of technical security. Audit firms have become smarter and protocols take security much more seriously.

As an evolving financial system, risk in DeFi appears to be shifting from technical risk to economic risk. Massive scale, rapid growth rate, increasing complexity, and deep interconnection are taking DeFi into unpredictable territory from a risk perspective. Since there are only a handful of companies working on risk in DeFi, the challenge now is to keep up.