IntoTheBlock CEO: Four major risks of modern DeFi

Author: IntoTheBlock CEO Jesus Rodriguez, CoinDesk; Translator: Tao Zhu, Golden Finance

Decentralized Finance (DeFi) is experiencing new momentum. The activity and high returns of the new ecosystem are similar to the famous DeFi Summer of 2021. The diversity of innovative protocols makes it difficult for investors to keep up, while the impressive growth has raised concerns about the accumulation of risks in the DeFi ecosystem.

You may have heard of doomsday analysis comparing the most successful protocols of this wave (such as Ethena or Eigen Layer LRT) with risk management disasters (such as Terra), but without really providing any credible similar evidence. In fact, the new generation of rapidly developing DeFi protocols is more mature and has invested a lot of effort in risk management. However, there are still great risks.

The biggest risk in the current DeFi market is not based on mechanical failures such as the one that caused the Terra crash, but on three key factors: scale, complexity, and interconnectedness.

The protocols in this wave of DeFi have grown quite large in just a few months, they support more complex financial primitives, and they are incredibly interconnected. This combination of complexity, scale, and interconnectedness has far outstripped the capabilities of current DeFi market risk models. Simply put, There are a ton of risk profiles in the current DeFi market that we don’t have reliable risk models for. And that gap appears to be widening, not narrowing.

The Four Major Risks of Modern DeFi

Risk has been part of the DeFi narrative since the beginning, and it’s easy to discuss it in broad, general terms. This new era of DeFi has brought novel innovations and is growing at a significant rate. As a result, the connotations of risk are different than before. In this DeFi era, taking a first principles approach to analyzing risk highlights four fundamental factors: scale, velocity, complexity, and interconnectedness.

To illustrate these factors, consider the difference in quantifying risk between a basic AMM with hundreds of millions of TVL and an AMM that uses rehypothecation assets and their corresponding points system and introduces its own tokens and points. The risk model of the former can be addressed with basic statistical or machine learning methods. The latter enters the realm of more advanced mathematical and economic branches, such as complexity or chaos theory, which are far from being applied in DeFi.

Let's look at the different factors in more detail.

1) Scale

The principle of the relationship between risk and scale in DeFi is very simple. In financial markets, modeling risk at a smaller scale, such as a few hundred million, is very different from modeling risk at a few hundred billion. There will always be some risk conditions that do not exist at a smaller scale at a larger 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 DeFi wave, attracting billions in TVL in just a few months. The biggest challenge Ethena faces in the current market is adjusting its risk and insurance models to this scale while funding rates remain negative for a long time.

2) Speed

The relationship between risk and speed is the friction between growing too big too fast. As a risk condition, speed is an accelerator of scaling. A protocol that grows from millions to billions in TVL in just a few months may not have time to adjust its risk model to the new scale before unforeseen risk situations arise.

The rapid rise of EigenLayer has sparked an entire LRT movement, some of which have grown to billions of TVL in just a few months, but still lack basic features such as withdrawals. The combination of speed and scale can exacerbate simple decoupling conditions into truly influential 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 inception, because the world economy grew too fast after World War II to meet the requirements of risk models. Modeling risk in simple economic systems is very simple.

In the new wave of DeFi, we have protocols like Pendle or Gearbox that abstract fairly complex primitives like 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

From a risk perspective, a widely interconnected economic system can be a nightmare, as any situation can have numerous knock-on effects. However, interconnectivity is a natural step in the evolution of an economic system.

The current DeFi ecosystem is much more interconnected than its predecessor. We re-collateralize derivatives in EigenLayer and tokenize and trade them in a pool in Pendle, or use leverage in Gearbox. The result is that risk conditions in one protocol can quickly permeate different key building blocks of the DeFi ecosystem, making the construction of risk models extremely difficult.

Shifting from technical to economic risks

Hacking and exploits have been the dominant risk theme in DeFi over the past few years, but this may be starting to change. The new generation of DeFi protocols are not only more innovative, but also more robust from a technical security perspective. Audit firms are getting smarter and protocols are taking security more seriously.

As an evolving financial system, the risks of DeFi appear to be shifting from technical to economic risks. The sheer scale, rapid growth, high complexity, and deep interconnectedness are pushing DeFi into unforeseen areas from a risk perspective. With only a handful of companies working on risks in the DeFi space, the challenge now is to catch up.