Author: William Starr

Co-Author and Review: Parker Jay-Pachirat, Sean Lippel

DeFi @ FinTech Collective. We are a dedicated, crypto-native team of investors at FinTech Collective. Our DeFi Funds are stage-agnostic, investing in both equity & tokens — from white paper to liquid markets. We support our portfolio through fundamental research, on-chain governance, code review, product & token design, liquidity mining, & more…

Introduction: Investing in GLIF

We are excited to announce our investment in GLIF, a non-custodial, smart-contract enabled capital markets protocol built for the Filecoin network. GLIF has created a simple and flexible way for data storage providers to finance their infrastructure costs on Filecoin, while allowing holders of Filecoin’s native token (FIL) to earn native yield on their holdings.

We believe GLIF will quickly expand over the coming years to become the de-facto financial layer of Filecoin.

Filecoin: Redefining Decentralized Storage

To understand GLIF, one must first understand Filecoin. Below is a quick primer. (Familiar with Filecoin? Skip ahead to ‘The Current Market for Storage Providers’).

A. What is Filecoin? The TLDR:

• Filecoin is a decentralized storage network running on a global, open market. It aims to act as the foundation for data-intensive Web3 services through verifiable storage, content retrieval, compute, and programmable apps.
• Filecoin, a “peer-to-peer version of Amazon S3”, ensures cryptographically guaranteed data availability and consistent storage verification.

B. Why is Filecoin needed?

The Problem:

The growth of the Internet and artificial intelligence has created the need for vast amounts of data storage. Yet, traditional cloud data solutions centralize control, raising privacy and security concerns for users of these services. The majority of global consumers and enterprises rely on centralized hosted services today, losing varying amounts of control of their data and its integrity in the process.

The Solution: Filecoin!

Filecoin addresses these challenges by distributing data across a global network of ‘Storage Providers’, ensuring no single entity controls the network’s data. This decentralization enhances security and privacy, fostering a competitive storage market to lower costs.

C. How does Filecoin work?

Using a dynamic supply-and-demand pricing model, Filecoin enables data storage deals priced in $FIL, Filecoin’s native token. It aims to create a decentralized storage market by encouraging global contribution of unused storage space. This contrasts with traditional, centralized cloud storage such as Google Storage, Amazon S3, or Apple’s iCloud.

Note: People who contribute their storage space to the Filecoin network are known as “Storage Providers.”

D. Who benefits from Filecoin?

The Filecoin storage network is ideal for organizations, institutions, government bodies, and enterprises. Why? These actors manage large amounts of sensitive data (such as personal identity data, medical data, and scientific research). There has even been a strong movement in the AI/LLM community to create decentralized warehouses for the model data.

Many institutions, including the City of NY, USC Shoah Foundation, OpenSea, SETI Institute, and Solana, already use Filecoin for data storage.

The Current Market for Filecoin Data Storage

Filecoin’s storage market is growing, with contributions from diverse participants. ‘Storage Providers’ range from individual hobbyists to professional, enterprise-grade data centers. Filecoin rewards storage providers in FIL. Despite the increasing number of storage deals being closed on Filecoin each year, there are still untapped participants. Providing storage on Filecoin has capital-intensive barriers to entry, including hardware costs and technical knowledge.

Several additional problems exist for both buyers of data storage (the demand side) and storage providers (the supply side):

1. Storage providers need a large amount of upfront FIL for pledge collateral to provide storage. There is no straightforward way to borrow FIL from multiple sources without fracturing liquidity and complicating operations.
2. Storage providers do not have an adequate way to hedge against the price fluctuations of FIL, which is also the means in which they are compensated. As with any business operation, uncertain costs and revenues are not constructive to financial forecasting.
3. The average holder of Filecoin has no simple, easy, and secure way to lend their FIL and earn native yield. No smart contract enabled DeFi protocols have been built to-date by experienced teams with deep knowledge of the intracies of the Filecoin ecosystem.
4. The Filecoin ecosystem struggles to incentivize parallel objectives including data retrieval and green energy mining.

Enter: GLIF Pools

GLIF addresses network inefficiencies inherent to Filecoin and resolves many of the pain points storage providers face today. GLIF pools are:

1. Scalable: Enables the deployment of hundreds of new multiple capital markets on Filecoin.
2. Customizable: GLIF modular technology stack enables fully customizable Filecoin markets to be built on top of it. GLIF’s purpose built oracle system monitors storage provider data in real time.
3. Interoperable: GLIF enables storage providers to borrow from multiple pools simultaneously without fracturing liquidity. Users can also deploy into one or more pools of their choice.
4. Composable: Providers can increase their borrowing power over time and leverage GLIF’s composability, allowing them to build new business models on top of the protocol.

The Launch of the FVM: Why The Time For GLIF Is Now

GLIF is not just another protocol in the DeFi space; it’s a pioneering liquid leasing platform designed for the Filecoin network. With the recent Filecoin Virtual Machine (FVM) launch, the network has embraced smart contract capabilities, opening new avenues for decentralized finance. GLIF leverages this innovation, enabling a non-custodial DeFi ecosystem that caters specifically to the needs of the Filecoin community.

The protocol’s unique approach to liquid leasing, inspired by the success of liquid staking protocols in other ecosystems such as Lido on Ethereum, allows Filecoin ‘Storage Providers’ to borrow against their collateral. This model boosts capital efficiency and democratizes access to FIL, the native token of Filecoin, for a broader range of participants.

GLIF’s Leading Position in the Market: A Look at the Numbers

GLIF’s impact on the Filecoin ecosystem is evident through its impressive early traction:

• Filecoin liquid leasing market has 3x’d since July 2023 (in absolute / $FIL terms).
• GLIF, now 2x larger than its closest competitor, stands out as the market leader, capturing over 58% of FIL market share in smart contract based Total Value Locked (‘TVL’).
• Over 10% of the total Filecoin Storage Mining power is pledged through GLIF.
• GLIF boasts more than 1,500 unique depositors, highlighting its widespread acceptance and trust within the community.
• With 190 unique miners pledged to the protocol, GLIF demonstrates a robust and growing network of participants benefiting from its services.

These statistics highlight GLIF’s successful launch and adoption, demonstrating strong early signs of product-market fit. The numbers validate our thesis that GLIF efficiently fills a market need with superior operational excellence and insight. This early success suggests GLIF’s scalability potential and future role in boosting Filecoin usage.

Why We Are Excited About GLIF

Our decision to invest in GLIF stems from a confluence of factors:

• ‘0-to-1’ DeFi Innovation: GLIF is not marginal incrementalism. The protocol represents a leap in DeFi innovation on Filecoin, providing a critical piece to the puzzle of capital efficiency and liquidity within the Filecoin ecosystem.
• ‘The Team, The Team, The Team’: Cue Bo Schembechler circa 1983, the founding team of GLIF is first-class, and the most talented team we have encountered building on Filecoin. Moreover, they have a proven track record together of building mission critical applications and tooling for Filecoin since 2018 including wallets, explorers, and multisigs.
• Technical Excellence: The GLIF team’s ability to navigate the complexities of Filecoin and create a bespoke solution tailored to its unique challenges reflects a level of technical expertise and innovation prowess. The protocol has been audited extensively via formal review at Consensenys diligence, as well as Sherlock. The team has financial and distribution backing from both institutional venture investors and Protocol Labs / Filecoin Foundation.
• Vision for the Future: GLIF’s extensive product roadmap is ambitious, and is aligned with protocols that look to capitalize on their dominant and trusted positions to launch complementary and adjacent solutions to service their users.

As we look towards the future, our investment in GLIF is not just a testament to our belief in its current value proposition but also an investment in the evolving narrative of DeFi’s role in reshaping the Filecoin network. We are excited to partner with GLIF as they continue to innovate, scale, and contribute to a more inclusive and efficient financial ecosystem.

Investing in GLIF: Efficient Capital Markets for the Future of Data Storage and Compute was originally published in DeFi @ FTC on Medium, where people are continuing the conversation by highlighting and responding to this story.

EigenLayer, and staked ETH rehypothecation in general, have been getting a lot of attention lately, and for good reason. Although the idea is basic — how can we leverage the security of Ethereum for other things — an application of it hasn’t ever been tried at this scale until now. So, what makes EigenLayer tick, how does it take advantage of Proof of Stake (PoS), and most importantly, what can go wrong and how can we think through these issues? We’ll explore the math and economics behind what will definitely become the most controversial and widely anticipated launch related to Ethereum’s security since PoS went live.

Background

Before we go into the meat of staked ETH rehypothecation, let’s get a quick refresher on Ethereum’s PoS consensus mechanism, where validators secure the network by staking their precious ETH. Also, we’ll touch on rehypothecation in traditional finance, where assets are reused as collateral for multiple loans.

Ethereum’s Proof of Stake (PoS) Consensus Mechanism:

Ethereum’s PoS consensus mechanism, known as Ethereum 2.0 or Eth2, is an upgrade from the existing Proof of Work (PoW) model. In PoS, validators are chosen to propose and validate new blocks based on the amount of cryptocurrency (ETH, in this case) they hold and are willing to “stake” as collateral. Validators lock up their staked ETH, which serves as a security deposit, and they are rewarded with newly minted ETH and transaction fees for their honest participation in the network.

The PoS mechanism is designed to be more energy-efficient, secure, and decentralized compared to PoW. However, it also introduces new complexities and potential risks, such as the risk of losing staked ETH due to malicious behavior or technical issues.

Rehypothecation in Traditional Finance:

Rehypothecation is a practice in traditional finance where a financial institution reuses collateral posted by clients as collateral for its own borrowing or lending activities. For example, when a client deposits securities with a bank or brokerage firm, the institution may use those securities as collateral to obtain a loan from another party. This practice allows financial institutions to generate additional revenue and improve their balance sheets, but it can also introduce systemic risk, as multiple parties become interconnected through the same collateral (FYI this is why almost every systemic banking crisis ever has happened).

EigenLayer: An Overview

EigenLayer is a restaking collective for Ethereum that leverages staked ETH. By opting into EigenLayer, validators can secure various software modules built atop Ethereum, like consensus protocols, oracle networks, bridges, and more. This creates a shared security pool, boosting the value of ETH and promoting permissionless innovation.

In the context of EigenLayer and staking ETH, rehypothecation refers to the process of leveraging staked ETH to secure multiple layers, modules, or applications within the Ethereum ecosystem (or beyond). You may be wondering how these software modules or protocols are secured today, and the answer is very simple: they need to create their own economic security from scratch, almost always with a native token. This has created a lot of fragmentation and exploits as often these models are either unsound or just not able to scale enough. By allowing the same staked ETH to provide security for various applications, rehypothecation can lead to an aggregation of security, benefiting the entire ecosystem.

Basically, validators that use their ETH to secure the Ethereum network by proposing/validating blocks can now take that same staked ETH (details on exactly how TBD but will most likely involve pointing the withdrawal address to a smart contract) and use it to secure something else which may or may not be related to Ethereum at all. In doing so, those validators will get a boost to their earnings, and other ecosystems/protocols can leverage ETH security instead of having to bootstrap their own. This is definitely a powerful concept; when done correctly.

The Economics of Rehypothecation:

The end goal of rehypothecation as we just said is to increase the utility of that staked ETH as much as we can until any more would be unsafe to do so. Luckily for us it’s actually pretty easy to figure out what that theoretical limit is. The harder part comes in trying to figure out how to price those rewards and risks, as often these will be relatively amorphous and dynamic.

Essentially, we want to find the right balance between rehypothecation’s benefits, like better security and additional revenue opportunities, and its risks, such as potential losses or increased dependency on other parties. With that in mind, the optimal rehypothecation rate becomes a measure of how much staked ETH is used as collateral for other financial activities. By analyzing this rate and the factors that influence it, we can find the sweet spot where the benefits are maximized while the risks are kept at a manageable level.

Consider a model where R represents the amount of staked ETH rehypothecated, and let S be the total staked ETH in the system. We can define the rehypothecation rate r as:

The objective function U(r) represents the overall utility of the system, which takes into account the benefits and risks of rehypothecation. We aim to maximize U(r) subject to the constraints 0 ≤ r ≤ 1, ensuring the security and stability of the system. We can model U(r) as:

where B(r) represents the benefits of rehypothecation, such as increased security and fee-sharing opportunities, and C(r) represents the costs or risks associated with rehypothecation, such as counterparty risk, dependency risk, and leverage risk.

We can represent the benefits and costs as functions of r:

where α, β, γ, and δ are positive parameters that capture the magnitude and sensitivity of benefits and costs to the rehypothecation rate r.

To find the optimal rehypothecation rate r*, we need to solve the following optimization problem:

To find the optimal rehypothecation rate r*, we can set the first derivative of U(r) with respect to r equal to zero:

Calculating the first derivatives, we get:

Setting the derivatives equal to each other:

Solving this equation for r* will give us the optimal rehypothecation rate that maximizes the overall utility of the system while balancing the benefits and risks. But, this information is meaningless unless we can get into the real stuff (aka the α, β, γ, and δ). Unfortunately as we mentioned this is where things become more complicated, and where some serious mistakes could be made.

Advantages of Rehypothecating Staked ETH in EigenLayer:

Now that we’ve proven there’s a way to know if you’re maximizing your utility as a leveraging validator, why should you even bother doing this? And why should anyone else even care about it as well? The use cases are infinite, you could reuse ETH security to secure a bridge, a decentralized sequencer, or even an entirely new chain. In the brave new world of EigenLayer, rehypothecating staked ETH comes with some serious perks:

• Amped-up security for software modules and DApps: By pooling security resources across multiple modules, EigenLayer elevates the level of protection for all the software modules and decentralized applications (DApps) that rely on them. As long as you model out where your security is coming from correctly, you end up with more security than relying on a single validator set, especially as you introduce varying security measures for each layer.
• More money for validators: When stakers validate multiple modules, they can rake in fees from various sources, not just the original staking yield provided from Ethereum. While it’s unclear what that additional revenue will look like, this is where the assumption on the loan “returns” are, as by delegating to and EigenLayer operator to run a service, you will get rewarded with additional yield.
• Ethereum as a staging ground for next-gen innovations: With EigenLayer, cutting-edge innovations like Danksharding and proposer/builder separation can be battle-tested in real-world scenarios before they’re integrated into the Ethereum mainnet. This makes Ethereum a living lab for the future of blockchain tech.

Disadvantages and Risks of Rehypothecating Staked ETH:

We won’t go too much into this here as that’s what the next section will be on, but there are certainly some negatives involved with rehypothecating staked ETH, as you’re essentially selling (and reselling) the excess security that has accumulated on the native PoS consensus. Your ability to do that is capped by how well you (in this case EigenLayer protocol) can diversify that risk.

• Counterparty risk: defaulting borrowers bringing the pain: If a borrower defaults on their obligations, it can trigger a cascade of losses for everyone involved. When you’re playing with rehypothecated staked ETH, you’re always exposed to the risk of counterparty default, which could from OG Ethereum PoS validator exposure, but also from whatever new form of risk these modules take on (securing new chains, DeFi protocols, etc). Keep your eyes peeled for red flags!
• Dependency risk: domino effect in interconnected modules: With staked ETH securing multiple layers and modules, a single point of failure could cause a devastating domino effect. If one module goes down, it could drag others along with it, potentially inflicting massive losses and destabilizing the whole system.
• Leverage risk, market volatility going turbo: Rehypothecation can lead to increased leverage in the system, making it more susceptible to market volatility. When the markets take a nosedive, highly leveraged positions can get liquidated, causing a chain reaction that could wipe out even the most HODLer portfolios.
• Dilution of security, spreading ETH security thin: Rehypothecating staked ETH means using the same collateral to secure multiple layers and modules. While this can be efficient, it also stretches the security provided by staked ETH, potentially weakening the overall stability and safety of the network.

Risk and More Risk, Especially Dependency Risk

Similar to how we modeled out rehypothecation, let’s see if we can similarly dive a bit deeper into what risk means for EigenLayer, specifically for validators who will use it, and also theoretically Ethereum’s security at large if Eigenlayer becomes big enough, and what are the levers used to change it to better understand the potential pitfalls.

To model the risks, we’ll use the following notation:

Rslashing: The risk of slashing due to participation in the Ethereum consensus layer.

RiEigenLayer: The additional slashing risk for the i-th validator introduced by EigenLayer.

Rtotal: The total risk of slashing for the i-th participant.

β: The risk sensitivity parameter.

The risk of slashing in the Ethereum consensus layer for a participant can be quantified using a utility function, U(Pi), where Pi is the percentage of ETH staked by the i-th participant (as each staker’s risk is directly correlated to their probability of being selected):

Here, Cslashing is the cost of slashing per unit of staked ETH, and the utility function U(Pi) models the risk aversion of the participant. An important point to note is that this is not the actual risk of slashing, but the perceived risk of slashing for each participant. This becomes equal to the utility function as each validator models the risk/reward tradeoff (utility function) based on your own risk exposure. These factors could include the participant’s staking history, the performance of the validator they are associated with, and their overall network behavior.

Now, let’s calculate the additional slashing risk introduced by EigenLayer for the i-th participant. We can use a similar utility function, U(PiEigenLayer), where PiEigenLayer is the percentage of ETH restaked by the i-th participant in EigenLayer:

Here, CiEigenLayer is the additional cost of slashing per unit of restaked ETH for that participant, which depends on the specific modules they validate and the interconnected risk associated with those modules.

To calculate the total risk of slashing for the i-th participant, taking into account both the Ethereum consensus layer and EigenLayer, we can use a combination of the risks and a risk sensitivity parameter, β, which captures the participant’s risk tolerance:

The risk sensitivity parameter, β, ranges between 0 and 1. A higher β value indicates greater sensitivity to the additional risks introduced by EigenLayer, while a lower β value suggests a higher risk tolerance.

Dependency as a Graph:

EigenLayer can best be thought of as an interwoven graph of modules, leverage, and utility, each pooling from the same stream of native Staked ETH (although technically this isn’t true, as validation is not a strictly homogeneous activity!). Within that graph, we have different vectors of dependency risk:

• In-degree centrality: The number of incoming edges to a node, indicating the number of modules that depend on a specific module. A higher in-degree centrality implies that more modules depend on this module, increasing the overall dependency risk.
• Out-degree centrality: The number of outgoing edges from a node, indicating the number of modules a specific module depends on. A higher out-degree centrality implies that the module depends on more modules, increasing its exposure to dependency risk.
• Betweenness centrality: A measure of the extent to which a node lies on the shortest paths between other nodes in the graph. A higher betweenness centrality indicates that the module plays a more critical role in the flow of dependencies between modules, increasing the overall dependency risk.
• Eigenvector centrality: A measure of the importance of a node in the network, taking into account not only its direct connections but also the connections of its neighbors. A higher eigenvector centrality implies that the module and its neighboring modules are highly interconnected and play a significant role in the overall dependency structure, increasing the dependency risk.

Let’s start by representing the modules as nodes in a directed graph G = (V, E), where V is the set of nodes (modules) and E is the set of directed edges (dependencies). We can define a dependency matrix D, where Dij represents the degree of rehypothecation dependency of module i on module j.

Using a risk propagation formula, we’re now able to get deeper into some of these characteristics. Let Ri be the initial rehypothecation risk associated with module i, and let R’i be the propagated risk after accounting for dependencies. We can then compute R’i using the following formula:

where the summation is over all modules j that module i depends on.

To analyze the overall dependency risk in the network, we can compute the total risk Rtotal as:

We can further examine the network structure by calculating various graph metrics, such as in-degree centrality and betweenness centrality. In-degree centrality Cin(i) for module i can be calculated as:

Betweenness centrality CB(i) for module i is given by:

where σst is the total number of shortest paths between nodes s and t, and σst(i) is the number of shortest paths between nodes s and t that pass through node i. High betweenness centrality values indicate that a module plays a critical role in the network.

Finally, we go to the aptly named eigenvector and its centrality risk. Let A be the adjacency matrix of the network, where Aij represents the connection between nodes i and j, and let xi be the eigenvector centrality of node i. Then, the eigenvector centrality can be defined by the eigenvector equation:

Here, λ is the eigenvalue corresponding to the eigenvector x. The eigenvector centrality is the eigenvector associated with the largest eigenvalue of the adjacency matrix A. It can be found using the power iteration method or other eigenvalue decomposition techniques.

The eigenvector centrality xi of node i represents its importance within the network, taking into account the connectivity of its neighbors. In the context of EigenLayer, a higher eigenvector centrality implies that the module and its neighboring modules are highly interconnected in the rehypothecation process and play a significant role in the overall dependency structure, increasing the dependency risk.

Dependency by Example, Why This Can Go Wrong Very Quickly:

Going through the abstract of dependency risk is one thing, but it’s another entirely to see just how easy it would be for this to happen with real ETH. Assume that the EigenLayer network has three participants: A, B, and C. Initially, all participants have 100% of their staked ETH as collateral. Assume that the EigenLayer network has three participants: A,B, and C, and 2 services/middleware running strategies: Y and Z. Let’s assume that each participant has staked 10,000 ETH.

1. Participant A is an operator, and borrows 5,000 ETH from Participant B, collateralizing it with another asset, Asset X, and uses the borrowed ETH to stake in module Y. Now, Participant A has 15,000 ETH in staked assets and owes 5,000 ETH to Participant B, while Participant B has only 5,000 ETH in staked assets but holds a claim on Participant A for 5,000 ETH.
2. Participant A then lends 3,000 ETH to Participant C, who uses the asset for strategy Z. Now, Participant A has 12,000 ETH in staked assets and owes 5,000 ETH to Participant B, Participant B still has 5,000 ETH in staked assets and a 5,000 ETH claim on Participant A, and Participant C has 13,000 ETH in staked assets but owes 3,000 ETH to Participant A.

Let’s say that, somehow, Module Y, where Participant A staked the borrowed ETH, experiences a severe fault in its operation and gets slashed by 40%. This would have a cascading effect on the other participants in the rehypothecation chain:

1. Module Y’s staked ETH drops from 5,000 to 3,000 (40% slashed).
2. Participant A had borrowed 5,000 ETH from Participant B and staked it in Module Y. Since the total of the staked ETH in Module Y has dropped, Participant A’s “loan” is now “under-collateralized” to what it is supposed to secure and Participant B has lost a piece of their stake. To maintain the required collateralization ratio, Participant A must either provide additional staked ETH or reduce the exposure of what it is securing. In this case, let’s assume they increase staked ETH to make up for the lost amount.
3. Participant C had borrowed 3,000 ETH from Participant A. With the need for further staked ETH, there is a high chance that Participant A will require Participant C to repay some or all of the borrowed ETH. Let’s say Participant A demands 1,000 ETH back, reducing Participant C’s borrowed amount to 2,000 ETH. Now Participant C also faces a difficult decision: they must either provide additional collateral (staked ETH) or reduce their exposed security in the service.

In this scenario, the slashing of staked ETH in Module D has a cascading effect on the rehypothecation chain. The consequences of the slashing are felt not only by the module directly affected (Module Y) but also by other modules that depend on it (Participants A, B, and C). It may seem impossible practically for such an event to occur, but remember that this is the world of crypto, where things move fast, and everything is connected. The modules and participants in the real environment will be much more convoluted and leveraged, and these events could happen without anyone realizing. We also could theorize that eventually there will be incentive structures created for getting the most staked ETH into a service, which will further compound areas of attack.

Ways to Stop This:

So now that you know it’s really easy for something to go wrong in the world of leveraging staked ETH, let’s talk about what we can actually do to mitigate all of these issues.

• Diversifying dependencies: Reducing the reliance on a single module by connecting to multiple, independent modules that provide similar functionality. This approach distributes the risk across various components, making it less likely that a single point of failure will lead to catastrophic consequences.
• Monitoring and assessing risks: Continuously evaluating the risk levels of individual modules and their dependencies using real-time monitoring tools, data analysis, and stress testing. By proactively identifying potential issues, participants can take preventive measures, such as reallocating resources or adjusting their staking strategies, to minimize exposure.
• Implementing redundancy measures: Building in redundancies within the system, such as alternative data sources or backup validation processes, to provide a fallback in case a primary module fails or experiences issues. This ensures that the system can continue to function even when faced with unexpected challenges.
• Encouraging responsible rehypothecation practices: Ensuring that the rehypothecation of staked ETH is conducted responsibly and within acceptable risk parameters. This may involve setting limits on the amount of staked ETH that can be rehypothecated or implementing strict requirements for validators to participate in EigenLayer.
• Establishing a robust governance model: Implementing a well-designed governance structure that allows for efficient decision-making, promotes transparency, and fosters collaboration among participants. This can help the community respond effectively to emerging risks and maintain the integrity of the system.

As you can tell, all of this becomes a lot more of an art than science, and there will surely be a lot of hiccups along the way as people start to test the boundaries of restaking ETH. As a safe approach, it’ll be critical to not overexpose yourself too much to modules that are taking on a high amount of leveraged risk to volatile or new protocols, and to be proactive with managing specific positions.

Eigenlayer has already made some commentary on what their approach will be in protecting against some of these risk vectors, including creating a “reputation-based committee” to act as a backstop of last resort if things go awry. This would be done by vetoing a slash on a module’s staked ETH if there was some anomalous activity that was not the fault of the restakers. There is certainly more to explore though and a lot of education will need to be done in order to make people fully aware of the risks attached. As a next step for research, it could be useful to map out a stochastic model of the system’s behavior, where the state transitions reflect the different states of each network (essentially their “health”). It could create a stronger theoretical structure for how to manage risk.

Conclusion

EigenLayer, an innovative approach to rehypothecating staked ETH, offers significant potential benefits for validators, developers, and the broader Ethereum ecosystem. By leveraging the security resources of multiple modules, EigenLayer can amplify security, increase profits for validators, foster innovation, and create a surge of novel protocols and applications in the DeFi space.

However, leveraging staked ETH also introduces new risks and challenges that must be carefully managed to ensure the long-term stability and security of the network. These risks include counterparty risk, dependency risk, leverage risk, and the dilution of security. It is crucial for participants to understand these risks and take appropriate steps to mitigate them.

To minimize the risks associated with restaked ETH, participants should diversify dependencies, monitor and assess risks, implement redundancy measures, encourage responsible rehypothecation practices, and establish a robust governance model. By adopting these strategies, the EigenLayer community can effectively navigate the complex world of leveraging staked ETH and unlock its full potential in the Ethereum ecosystem.

Financial markets are a complex set of interlinked systems. Decentralized finance (DeFI) markets are even more complex: they move faster, are global in nature, and are infinitely more composable. These characteristics, unique to DeFi markets, have always been a feature, not a bug. And they’re especially critical to those who have been underserved by traditional finance. The goal of DeFi is to build financial rails that are open, transparent, and accessible to anyone in the world.

However, complex interlinked systems like decentralized finance are at greater risk of suffering from cascading events. Cascading events are events that take place through a chain reaction and are often uncontrollable and happen in quick succession. Cascading events can occur in a part of an ecosystem and quickly snowball to impact the broader ecosystem. An apt metaphor is when one domino falls, the next one follows, so on and so forth.

Web 3 users, builders, investors, community members, contributors and etc, can learn alot from cascading events: everything from protocol design to market psychology to how complexity builds upon different aspects of the market.

Today, we look at Lido’s stETH token, specifically the stETH depegging from its social and market consensus peg to ETH that happened in June 2022 as a case study to understand cascading events. Particularly, looking at how cascading events can occur in a part of the ecosystem and quickly snowball to create far-reaching impact on the broader ecosystem. From this case study we also share some observations and learnings.

Let’s jump right in!

What is Lido?

Lido is a DeFi protocol that allows anyone to stake their ETH to secure the Ethereum Network. In exchange, the staker is paid a management (service) fee taken as a percentage of the rewards. This fee is a small percentage of the rewards allocated to your portion of staked ETH.

Lido is a very important part of the Ethereum ecosystem. The company was created to make staking on Ethereum more accessible to the average token holder and validator. In a Proof of Stake system (which Ethereum is soon moving to), token holders who stake their tokens earn rewards for securing the network. See [here] for a primer on what the move to Proof of Stake means for Ethereum.

Without Lido, the requirements to participate in Ethereum’s Proof of Stake are significant :

• A token holder needs to stake 32 ETH to create a validator node. At current prices (August 2022), 32 ETH is about ~$50K+ USD. At all time highs (ATH), that’s closer to $150K USD. Not an insignificant amount for the average token holder.
• There are technical requirements to spin up a node (e.g. deploying code)
• Previous operational knowledge is necessary to run and optimize a node

If a validator messes up or makes a mistake, their entire stake can be removed by the network — also known as slashing.

So… the criteria for participation is strenuous, and the stakes are high. All this can be a tough pill to swallow for the average token holder looking to be a validator.

In short, Lido lowers the barriers to entry to become a network validator, allowing more token holders to participate in securing the Ethereum blockchain. Lido allows users to become network validators by staking any amount of ETH in their protocol (rather than the typical 32 ETH required), no technical know-how needed.

The Lido protocol is decentralized. Rather than using a centralized approach, Lido uses a consortium of verified and trusted validators to “manage” the actual staking and node operation on behalf of the users. Users can also influence the project’s direction through the $LDO token, which enables users to participate in governance over the protocol itself.

stETH: Liquidity on Top of an Illiquid Asset

Currently, ETH is staked only on the beacon chain and cannot be withdrawn. Withdrawals of ETH staked will NOT be available until months after the merge has occurred (during the Shanghai hard fork).

To allow users to exit their position now, Lido created $stETH — a liquid tokenized version of the staked ETH users deposit. When a user locks up (or ‘stakes’) 1 ETH in Lido, they receive 1 stETH in return. In this way, stETH acts like a redeemable receipt. stETH represents the same value as whatever amount of ETH it was deposited for, and functions similarly.

stETH is yield generating because of the underlying staked ETH it represents. But stETH has another utility feature, too: it can be traded.

Have your cake and eat it too.

In a perfect world, then, 1 stETH should be worth at least 1 ETH, if not more when accounting for the underlying staking yield attached. (Of course we don’t live in a perfect world, and 1stETH usually trades at a slight discount to 1 ETH, mainly due to (1) stETH is not redeemable for the underlying ETH and (2) stETH is newer and haven’t been as tried and tested compared to ETH).

All this enables further liquidity in the broader DeFi market. Liquidity is critical to healthy DeFi markets. Liquidity literally enables markets to exist. And when you have a token like stETH, whose key feature is liquidity on top of an illiquid asset, a market forms and with it, arbitrage opportunities arise.

Lido has seen strong demand for the products and services described above, commanding a significant amount of market share. In fact, stETH token accounts for ~90% of liquid staked ETH and almost ⅓ of all ETH staking goes through Lido’s network.

This all sounds great: A fledgling ecosystem created to secure the Ethereum network in a trustless and decentralized manner by increasing user participation in ETH staking.

Where could it go wrong?

Chasing Yield with stETH

Nothing in crypto exists in a vacuum. It’s important to contextualize stETH in the broader financial market itself.

Over the last few quarters, CeDeFi (Centralized DeFi) institutions and crypto funds started taking on leveraged bets with stETH and ETH to maximize yield. CeDeFi products allow customers to gain exposure to crypto while managing custody and interaction with the underlying tech themselves, in-house. The institution is centralized, but they provide products to customers that inherently depend on DeFi.

Like many trading strategies, CeDeFi institutions combined a variety of DeFi Lego blocks (See this post to learn more about Money Legos).

These leveraged bets were typically conducted on Aave/Instadapp, and worked like this:

1. Stake ETH in Lido, receive stETH in return.
2. Deposit stETH into Aave and borrow ETH.
3. Rinse and Repeat.

By levering up their exposure to stETH, CeDeFi institutions were able to greatly compound the interest they would earn as compared with just simply staking their ETH. This, however, came at the cost of resilience. Levering exposure to stETH made CeDeFi institutions significantly more sensitive to any potential issues that could arise, no matter how unlikely they seemed at the time.

All of that leveraged CeDeFi institutions created during this time was predicated on the assumption that 1 stETH is relatively equal to 1 ETH, even if it was technically not yet redeemable.

If the “1 stETH is relatively equal to 1 ETH” assumption is true, which it would be under normal market conditions, the leveraged plays would be just fine… right?

Assumption Breaks: stETH depegs from ETH

By May 2022, the crypto markets were already very shaky. Macro conditions had taken a turn for the worst, and on-chain yield opportunities were drying up as people started trading less. On top of that, the Terra blockchain crash had an immediate and dramatic effect on the entire DeFi ecosystem. See here for a detailed timeline of Terra. By mid-June 2022, the Terra blowup contagion reached stETH. Even though Terra is a completely different chain, the composability across different chains created a delicate interlinked system that came back to bite us when things blew up.

One of the products offered on Anchor (Terra’s lending protocol) was taking wrapped stETH and using that as collateral on their chain. This collateral essentially became trapped because 1) nobody could repay those loans to get their collateral (wrapped stETH) back on Terra, and 2) the Terra network shut down completely so it was impossible to access those tokens anyways.

The people and entities using the Terra network were also the same ones using these leveraged yield products for stETH. Not only did they lose access to those funds, but a majority of their capital (including a “stable” asset) evaporated from the Terra fallout. This put those funds in a precarious condition that could lead to a “bank run.”

The only way to unwind this was to sell stETH to cover their positions — creating selling pressure on stETH.

This started the stETH price depegging causing liquidations in the positions held on Aave.

The fundamental assumption for the stETH:ETH peg was being challenged.

With 1stETH no longer closely equaling 1 ETH, cascading liquidations quickly ensued. As little as a 10% variance was enough to cause the collateral ratio to fall below the liquidation cutoff, due to how leveraged the loans were that these institutions had created. The cascading effects start with the riskiest and most leveraged ETH borrowers (who have the highest loan-to-value ratio) getting liquidated and having their stETH automatically sold on the open market. This selloff caused low liquidity in the stETH:ETH Curve Pool, which then created further sell pressure. This then led to more cascading liquidations, so on and so forth.

The market couldn’t unwind this trade.

Under normal circumstances, lenders were happy to lend out ETH as long as stETH was provided as collateral: the large demand and established consensus on price parity made for a comfortable situation. But, as the lending became more risky, all that liquidity dried up because no one wanted to lend out ETH — a truly liquid asset — for stETH, an asset representing an illiquid version of non-redeemable ETH.

Who would want to lend ETH in this scenario? Would you choose to at all?

Under stable conditions, when a price depegs on a DeFi Protocol like Curve, market makers step in and arbitrage by market selling ETH for stETH. In this situation, there was greater demand for ETH than stETH. While there’s still no true way to redeem ETH with stETH, there was established market consensus that the two assets closely followed each other in price. Under small swings, market markers can arbitrage and make a quick profit. However, in this case, stETH began dropping even further relative to ETH. Mercenary capital quickly cut their losses and sold their positions, or removed their liquidity from the pool entirely.

Market makers have to protect their own books — it’s existential. If you don’t cover your book (a.k.a. your own leverage positions) then you go under. To cover their losses, the market makers involved in the blowup had to sell stETH at a price less than 1 ETH.

The sell pressure this put on stETH caused the price to crash further, leading to a series of cascading events:

First, reserves in the Curve protocol crashed. Everyone wanted to trade their stETH for ETH, and in a short amount of time, the quantity in the stETH pool was greater than ETH in the Curve pool. At one point, only 20% of the liquidity pool was ETH, compared to a normal 50%. Everything was out of balance… 1 stETH was no longer 1 ETH, and anyone trying to sell stETH would face extreme slippage.

Second, this then led to a near liquidation of Celsius’ (a CeDeFi institution with one of the largest positions of stETH) position in Aave almost being liquidated. Once those first bad debt positions started to get hit and unwind, the on-chain price oracle dropped precipitously.

Narrowly avoiding complete liquidation and catastrophe of the position, Celsius has fully paid back the loan on Aave, and no longer has an active stETH collateral position. This has allowed the peg to return to a healthy variance (~2%). The markets have now recovered relatively well from the turmoil of these past few months.

Observations: Cascading Events and Downward Spirals

Similar to historical corrections in the crypto market, few could predict the significance (and rapid succession) of these events.

It almost always feels unprecedented.

What learnings and observations can we come away with?

Market and consumer psychology can break fundamental assumptions.

Market and consumer psychology is very volatile. Even only a slight depegging of stETH:ETH could cause FUD (Fear, Uncertainty and Doubt) and rattle a fragile market further. Everything about 1stETH equaling (at least) 1ETH makes sense, yet in fragile market conditions, people were willing to sell 1stETH for less than 1ETH to cover their losses. The market psychology at the time was not strong enough to handle the stETH:ETH-depegging and defaulted to the natural instincts of self-preservation.

Dual Nature: The key feature can also be the key bug.

The critical feature of stETH became the critical bug in stETH. Liquidity is good, but liquidity to over-leverage on one thing can be bad. It’s good to have money legos, but sometimes the strongest feature may end up being the weakest link. It depends on market conditions. Complex, interlinked systems create greater opportunities for the acceleration of widespread contagion.

In Web 3, it is important to create open forums to gather community feedback, pressure test product design and continuous testing. And even then, you may not know how things will work in the wild until you inevitably ship the product. The design phase should also focus on designing stop gaps and guardrails.

Game theory got the best of everyone. Everyone wanted to protect their book, not the system.

Not my system, definitely my book.

Many of the token economics during the past few years have focused exclusively on ways to increase Total Value Locked (TVL): the amount of money a DeFi protocol is managing at any given time. This gamification of narratives and hype cycles creates a toxic environment penalizing the last one “holding the bag.” While there is value in protocols optimizing to drive interest and usage through their tokenomics, this needs to be done sustainably and with user protection in mind, otherwise it’ll always be self-preservation over optimization for a healthy functioning system.

Moving forward, a significant topic of discussion will center around strategies that allow protocols to take advantage of reflexive bull market cycles — and how they can protect themselves during that same reflexivity when things aren’t going well. Of related importance is the urgency for protocols to design with inherent protection in mind, ideally baking guardrails into the protocol.

Contagion always surprises us

The hardest part about contagion in any market-based system is that often, it’s difficult to digest the complex implications that arise from an impacted interlinked system. This, coupled with the fact that market behavior sometimes is unexpected and hard to predict.

Murphy’s law is real.

While it sometimes may seem like nothing can go wrong, there is always a chance it does. This chance increases with increasing complexity in protocol design. A system-wide cascade can be caused by a variety of factors that originated in various parts of the system unidentifiable to you. And even after successfully identifying the origin of the problem, you must then embark on a tedious and exertive journey: convincing others you’re right. Seeing contagion in the purview of cultural milieu requires building conviction across multiple parties and entities, all while accounting for their different drivers and incentives.

A healthy market is about balance. When there is an imbalance, expect a correction. When there are significant imbalances, expect significant corrections. In this way, financial markets are similar to systems in nature.

Nature always finds a way to self-correct. It seeks balance.

Balance is the way.

Moving Forward

Lido is an important part of the Ethereum ecosystem. It’s a piece of critical infrastructure that allows for greater participation in decentralized systems like Ethereum.

In our next installment, we’ll take a look at Lido’s governance model. Specifically, we’ll explore:

• Community proposals — What is the community proposing?
• Pre vs. Post-Merge — How will Lido’s governance differ Pre-Merge versus Post-Merge?
• Conditions to thrive — What are the ideal states of a player like Lido, which provide a key piece of infrastructure in liquid staking and the broader Ethereum ecosystem?
• Need for adaptation — What do you do as a project when the underlying ecosystem you are supporting is always changing and constantly in-flux?
• Scaling & velocity — How do you balance achieving scale versus running the risk of becoming “too big too fail”?

Thanks so much for reading. See you next time!

Author: William Starr

William is an investor and technical lead at Fintech Collective. He’s been a builder, adviser and investor in Web 3 and DeFi since 2017.

Social links: Medium; Twitter; LinkedIn

Author: Liang Wu (LJW)

Liang is a Web 3 Research Fellow at HBS and a builder/operator in Web 3 since 2018. He leads Life in Color, a writing collective that explores and breaks down different topics in Web 3.

Social links: Life in Color Blog; LinkedIn

Special thanks to Parker Jay-Pachirat on the Fintech Collective Team for reviewing and providing feedback and edits on this essay.

Disclaimer: This article was written to explore underlying issues and insights in the Lido ecosystem and written for purely educational purposes. Nothing written in this article should be taken as financial advice or advice of any kind.

I recently had the opportunity to participate in one of the largest and longest running Ethereum events in the world — ETHDenver.

This was certainly a year to remember for ETHDenver, the energy was palpable. Given the last in person Ethereum event was held two years ago due to Covid, there was a lot of pent up enthusiasm. Over 12k registered people attended the event, a 2x increase from 2020. The vast majority of those individuals were creators.

Camilla on Twitter: "Context is important:🏔️ETHDenver '18 @ 3,000 ppl (ETH = $600, post-ICO mania)🏔️ETHDenver '19 @ 4,500 ppl (ETH = $100, deep bear market)🏔️ETHDenver '20 @ 6,000 ppl (ETH = $300, pre-March Covid crash)🏔️ETHDenver '22 @ 12,000 ppl (ETH = $3,000, after 2yrs of growth)@BanklessHQ / Twitter"

Context is important:🏔️ETHDenver '18 @ 3,000 ppl (ETH = $600, post-ICO mania)🏔️ETHDenver '19 @ 4,500 ppl (ETH = $100, deep bear market)🏔️ETHDenver '20 @ 6,000 ppl (ETH = $300, pre-March Covid crash)🏔️ETHDenver '22 @ 12,000 ppl (ETH = $3,000, after 2yrs of growth)@BanklessHQ

I headed out to Denver a week before the conference started, with the sole purpose of reconnecting with the source of energy and purpose that you can only get by talking with builders as they innovate and create in real-time.

The momentum I felt being present at the hackathon inspired me. Over the week, I contributed to two projects: one winning the DeFi track, and another winning a sponsored bounty. Most importantly, my time at ETHDenver and experience participating in the hackathon gave me some time to reflect a bit on the state of being a developer in the crypto space.

Here are some unvarnished thoughts:

The power of hackathons & hacker houses / communities

Communal building events are the lifeblood of the crypto ecosystem. You cannot replicate being able to jam in real-time with people on seemingly impossible ideas — and then, working feverishly in a constrained period of time to attempt to make them come to life through code.

ETHDenver is the ultimate showcase of the power of these events and their importance to the Ethereum community as a whole. Vitalik, the core creator of Ethereum, even showed up himself to pepper the development teams with questions (disguised in a Bufficorn costume, of course).

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In my current role as a VC and Technical Lead at FinTech Collective’s DeFi Fund, participating in and supporting these events is a true differentiator. Engagement in these events is not just about finding alpha — it serves as a strong signal of value alignment to builders in an industry where “open contribution” is a core tenet.

Protocol and application development are becoming core value propositions for key players in the crypto venture ecosystem. A16z recently invested in Launch House, mtnDAO launched a Solana-focused hack house with $400K+ in rewards for developers, and Delphi & Jump assembled a Terra focused hacker house. We may even see some FinTech Collective sponsored events soon™️. Testing out new ideas at the earliest stages and working on the front lines of a startup’s journey is a core part of FinTech Collective and where I personally enjoy playing a role in helping.

People always ask how devs are. But what are they excited about & building?

Being both a builder and a VC gives me the unique opportunity to see founders’ activities from three very different vantage points:

1. What devs are building
2. How these devs are pitching their creations to VCs
3. And what are VCs actually funding

Across these three dimensions, crypto has the highest level of variation I’ve seen in any industry. The high level of variation in how innovation is communicated and perceived across parties can be attributed to two primary factors:

I. Creators must have a stronger direct connection to their end users than ever before

II. A lot of what is being hacked / built is fundamentally “uninvestable” (and is often done so on purpose).

I’ll touch on the former more later, but it cannot be stressed enough: the community element of Web3 is fundamentally different than anything we have seen before. It’s almost as if giving all participating stakeholders the proper incentives encourages contribution — a strange concept, I know. Another contributing factor is the time to liquidity for a Web3 project is dramatically shorter, decreasing the need for “long term aligned” investors.

And when I reference “uninvestable” tech being built on purpose; builders find themselves in a different world in Web3 — one where grants are seemingly limitless and oftentimes fundamentals do not matter. Gone are the days when someone would beg for donations to their passion project whenever you tried to use their code, or over-engineer for monetization so investors would find the project interesting. An example of this recent shift is the initiative Gitcoin Grants has taken to make it exponentially easier to identify, support, use, and share projects building in public. Now, builders can feel comfortable focusing on what they do best: creating cool products– and leave the monotonous task of monetization to others.

This year’s hacks at ETHDenver are a strong attestation to this. Nearly half of the projects at ETH Denver were public goods focused– products with no explicit monetization strategy and generally “free to use”, including developer tooling and “open-source” concepts. The other half of the projects were geared towards “investable” ideas. Regardless of focus, all of the projects painted a picture of what is coming next in Web3:

• Privacy and data protection
• Enhancing NFTs through financialization, ancillary data, etc
• Derivation of core DeFi primitives
• Lowering the barrier to entry for the next 1bn users of crypto
• Unlocking social & community using new incentive and governance designs
• SCALE — infrastructure to scale everything: companies (DAOs), networks, and tech stacks

Old theoretical ideas are now finding practical implementations

Another common theme throughout the conference and hackathon was applying old theories and ideas to new problems. This is the basic premise underlying the development of zk-proofs, and their implementations around coordination. It was also the main thesis behind my team’s hack for the DeFi track.

MEV (miner extractable value) is a massive industry-wide problem and the focus of research for many crypto protocols and applications, where front-runners can see transactions before they are submitted and perform arbitrage opportunities on top, such as sandwich attacks. Unfortunately, all currently known solutions seem to sacrifice either efficiency or decentralization.

My team’s approach was simple: apply well-known mathematical theory called VDF (verifiable delay function — a popular concept a few years back that fell out of favor from not demonstrating practical utility) to doing swaps on an AMM (automated market maker). VDFs work by becoming a “function of time” where no matter what, in order to produce the proof you have to spend t amount of time calculating it. A very useful idea for preventing front-running attacks, and they were originally applied to tamper-proof randomness generators. When we used that same logic for submitting swaps, we could control the speed you can submit a transaction by forcing each swap to include a proof that they calculated a VDF using parameters from the transaction.

So, how does it actually work in practice? When you try to conduct a swap on Uniswap, the application will compute a mini math problem. This results in just a minor wait time for you, but is a complete inhibitor for MEV attacks. The reason why is that since each user now needs to wait t seconds, a front-runner would not be able to perform a MEV attack as they too will have to wait t seconds before they can submit, at which time the user’s transaction would already be included in the block The idea worked, and we believe that it can similarly be applied to any situation where MEV is present (but more to come on that soon).

I believe reapplying old theory is a large area for innovation. I expect to see homomorphic encryption, old mechanism design principles, distributed key generation, prediction markets, futarchy, and more resurface soon in the future. Who knows — we may see token curated registries make a comeback 😎.

New innovations in developer tooling & what is still missing

If you are building anything to help scale crypto in a meaningful way, I want to talk to you! There is still massive room for growth, especially within dev tooling. That being said, it is mind-blowing how far we have come from the inception of smart contract development in 2015.

The past two years have produced a greater acceleration of change than ever before. We now have access to environments that allow you to simulate your protocol in the “real-world”, robust smart contract testing frameworks, and in-depth analytics and data. Most importantly, many of the intricacies related to smart contract development are becoming abstracted at the higher level code (web and mobile applications). All of that abstraction greatly reduces build times and increases the possibilities for composability. We are slowly getting to the point where for a product / front-end developer, blockchain data is just like any other backend (h/t to FinTech Collective portfolio company The Graph for their pivotal work in this area). Products like Boto, another FinTech Collective portfolio company, are similarly pushing the ecosystem further into ubiquitous, composable, and no-code solutions for interacting with blockchain data.

Still, there is much work to do before we can fully unlock the power of Web3 — both for the general user and for the builder. This year’s ETHDenver reminded me of the many pain points I had first hand when I first started 6 years ago and still have now. In no particular order, these are some of the things I would personally love to see that would make my life -or any Web3 dev’s life- easier:

▹ Anything related to smart contract security. This is such a well-known problem space. We are at the limits of what smart contract auditor companies can do. There is simply too much to check and too little time. Status quo auditing has always been a temporary solution to a permanent problem. What I envision includes (but is definitely not limited to):

• Insurance products
• Applied AI for attack vector detection
• Black swan simulation tests
• Standardization stacks for common state flows
• “Front-running-as-a-service” for anomalous transactions
• Much stronger logging and tracking system for on chain protocols in general

▹ New models for directly baking in security into your dApp (decentralized application) UI. Not only for managing private keys, but for creating guardrails for users within Web3

▹ Visualization tools for bridging technical/product gaps. This was the idea behind my other team’s hack at ETH Denver — Ice Vision, how to track what is actually happening visually in your smart contracts, especially as they become more composable

▹ A more robust and decentralized way to “upgrade” smart contracts without painful migrations

▹ Creating better value alignment incentives for community contributions and testing

Open-source and community can be your best friend (if you let it)

Web3 is changing our experience with technology in almost every way imaginable. This is a truth that extends from the end user all the way down to the developer enabling the experience.

One of the most powerful changes from Web2 to Web3 is how we all interact with what is built. Web2 was about protecting and siloing your proprietary innovation — while Web3 is all about open-access, transparency, and composability.

This transition brings up two important considerations for a builder:

1. A highly engaged community in the building process = greater long-term success. While it’s easy to assume that leaving everything open-source risks greater inefficiencies, it’ll only continue to become clear just how inconsequential individual contributions are in comparison to the efforts of the collective. Creating with a community not only builds a better overall product, but makes it much easier to see what is coming next.
2. Open source is a survival mechanism. If you don’t conform, you will be left behind. Nobody is saying you can’t be protective before launch or create proprietary software, or that you can’t profit off of what you’ve made. But, ultimately the rate of innovation will far outpace your own contributions. You either need to accept the fact that people will fork and build off of you (and find a way to capitalize off of that), or fail. I highly recommend watching this movie to understand why.

As a builder in crypto, you should also be expecting a far more symbiotic relationship from any VC you partner with. We need to do better as an industry to catch up to the realities of what Web3 means, and adopt our own model to fit.

Investors must do their part as well. This means actively participating in governance, contributing to the building process, and coming to the table with clear and tangible ways to help.

Some closing thoughts: Blood in the streets, time to build

While the rest of the market is worrying about the price of tokens, creators are furiously building out the next wave of tech to advance Web3.

The only constant within crypto is that it’s impossible to keep up with the fervor of innovation that people dream up and that the rate of change only seems to be increasing. The reason why is quite simple — everyone cares deeply. This is the fundamental basis behind Web3’s meteoric success, and the source of my own personal excitement. As a community, we are all united by a common goal to create something better than what came before us.

All in all, it has never been a better time to create, and I’ve never been more excited to be involved in crypto as an investor and builder.

If we are to define the optimal blockchain governance, we must understand what governance means, what is good governance, and who the players are within governance structures. It is our hope that by breaking down the abstract concepts of governance and how economics helps shape that definition, we can build a better solution.

What is Governance?

In its strictest definition, “governance signifies the exercise of political, economic and administrative authority to manage a group’s affairs comprising the complex range of mechanisms, processes, relationships and institutions through which citizens and groups articulate their interests, exercise rights and obligations and mediate differences. Governance is not the sole prerogative of the leader but its functions could be assumed by or delegated to specified institutions and organizations in the private sector and the civil society. Such organisms operate in a legal or policy framework defined by the leader but having autonomous existence and exercise political, economic and administrative authority[1].”

Put simply, governance is the process and institutions through which decisions are made and authority in a group is exercised. It can be as complex as a nation state, or as simple as the family dynamics at play within your home.

What is Good Governance?

There are four main values for a good governance structure.

• Participatory: All members have a voice in the decision making process
• Transparency: The procedures and methods of decision making should be available to all members
• Sustainability: Changes are balancing in their effects over members
• Equity and Equality: Everyone receives the same outputs for the same inputs, and there is an active effort to provide equal inputs

It comes down to two things: inclusivity, and accountability.

Inclusivity factors in having an equitable, participatory, and equal structure, where each member has equal directive in the flow of governance.

Accountability is what allows us to verify and input our voice on what we believe should happen. This has both an internal and external component, which crucially allows us to have transparent and sustainable processes. The sustainable aspect will be the central focus for most of the paper, especially how to align all participants into a long term net positive system.

Who Are the Participants in Governance?

For our purposes, we will focus on the three core groups, the participatory members (think citizens of a country, or members of a family), the government itself, and all external forces (non-human/other countries/ the environment).

For any government we must make sure it is:

• Service oriented
• Responsive to hopes and aspirations
• Facilitative and enabling
• Regulatory rather than controlling
• Dealing with temporal issues

Our participatory members, on the other hand, have:

• Accountability
• Equal expression of opinion
• Ability to audit the process of decision making

Tax

A governance body’s greatest weapon is the ability to tax its participants. This tax can come in many forms, and is used to not only redistribute wealth, but as a way to maintain sustainable use of all a group’s resources. When applied correctly, it provides a perfect economic balance between regulation and growth, and avoids unnecessary controlling forces. In the example of a family, tax can be thought of as weekly chores, spending time with extended family, or even actual payments (perhaps a swear jar).

Unfortunately, there are inefficiencies within tax, particularly tax evasion/tax avoidance. This violates fiscal equity, as now not everyone is contributing their fair share. Above that, we now have to introduce enforced compliance, since we can no longer rely on voluntary contributions. The enforcement adds another layer of cost, which greatly diminishes the value of tax incentives, affects redistribution programs, creates artificial biases in macroeconomic tax indicators, and increases the tax burden for everyone else. If someone within a family fails to perform their duties, we can easily see how this leads to the issues mentioned.

Methods to Combat Tax Avoidance

The most obvious methods involve cross checking information submitted, auditing the tax trail to ensure proper adherence to procedure, and enforcement of penalties. At scale, these become increasingly expensive to maintain, especially as the tax structure becomes complex. This is why it is important to have tax systems that are more efficient, effective, equitable and fairly comprehensible.

The last method is a reliance on indirect taxes. These are far more abstract, and much more difficult to calculate economic values for. In a family dynamic, indirect taxes would be a buildup of a dirty dishes in a sink, making it impossible to eat, where the “tax” is a product of not following the rules. In a world view, the most prolific example is the effect we have had on the environment, and what that will mean for future generations.

Fortunately for us, blockchain actually makes the direct methods much more easy to implement, allowing us to accurately model the indirect effects into the taxing structure and reduce the cost of tax inefficiencies.

Conclusion

The basic first step of a developing governance is to evaluate its resources and formulate a strategy for economic stability. All key players, resources, and growth strategies must be coordinated, especially private sector development standards within the ecosystem. Establishing relationships between private and public entities nurtures mutual growth instead of opportunistic dealings.

For any governance model, we should constantly be reevaluating the efficacy of our financial management reforms, including structured planning, creating a unified budget, integrating budgeting and accounting, and encouraging financial accountability.

How Does This All Relate to Blockchain?

If you haven’t already, I encourage you to read Fred Ehrsam’s piece on Blockchain Governance. There were many parallels that were drawn between a nation-state government and a blockchain protocol, while also providing concrete reasons why the connection is so important to think about. The following section will be an extension of that, as well as a comparison to our previous definitions of governance.

Governance to Blockchain

Using our simple definition, governance within blockchain refers most broadly to the consensus of state, but because the blockchain’s job is to survive ad infinitum, we must consider all resources that have long term effects on the state, as well as the taxing-incentive structure that maintains sustainable value for all key participants.

The participants

When first designing Bitcoin, Satoshi imagined a world where the miners were users, users were miners, and users actively contributed to development. This quickly unraveled into separate classes of members, with other protocols following relatively similar guidelines.

• Users: Those who transact or store the currency, active participants in a group
• Miners: Voters on the state of the system
• Developers: Rule makers, and in some cases, the first round of voters for new proposals
• 3rd Party Contributors (like ASIC Creators or Infura): More generally, these can be seen as the private institutions that are hired to augment the government, or fix gaps that the government is ill-equipped to handle itself
• Betters: Market makers on the value of the ecosystem, it is arguably in their best interest to have longevity, even for short sellers, since they want to be payed out

Bitcoin and Ethereum.

Whenever we think of governance in blockchain, it is fair to say that Bitcoin is very much akin to the United State’s own system. Unfortunately, just like in the country, there are several inherent problems Bitcoin faces.

Participatory: While you could argue that everyone has a right to express their opinion on the protocol, it is certainly not true that everyone’s opinion has equal weight, or that there are systematic ways to handle opinions. The codebase is maintained by less than 5 active core developers, from which any change has to be accepted by an extremely small cohort of miners.

Transparent: Putting aside the simple fact that a tiny fraction of people are capable of assessing whether a code change is valuable, it is still difficult to find what the code change is, with people currently having to rely on esoteric BIP request email chains.

Sustainable: The most damaging issue is misalignment of all participants on long term value. As Fred pointed out, developers, other than the core early adopters, have little to no incentive to create new initiatives, since they have no stake within the system. Miners prefer short term gains over long term growth, and have been unwilling to adopt updates for fear of losing profits. And little thought has been given to external forces, from 3rd party wallets, to the environment and government.

Equity and Equality: Due to the incentivization model, miner’s stake and influence on the protocol compounds as they participate, making it more centralized, and creating monopolistic barriers to enter. ASIC creators have become the bottleneck of the industry, as the reliance on a next release puts those without access at a disadvantage, with no other alternative than to succumb to their wishes.

The same can be said for Ethereum, although it is in an even more precarious state of benevolent dictatorship, where there is an over-reliance and trust in a leader to decide the fate of the system.

Bitcoin and Ethereum’s key fault was creating too rigid of a governance structure, and not accounting for all its resources and participants. Things such as economies of scale, freeloading, loss aversion, and tax evasion have severely hindered the longevity of the platforms, and have created gross misalignments of value.

So, What Makes a Good Blockchain Governance?

Whenever we pictured what good governance looked like, there was one point that constantly drove our decisions, uncertainty. Uncertainty of environment, society, behavior, regulation, and technology for a system that should theoretically last forever. Because of this, our main concern is for a simplified and robust system that relies on information that is fully participatory, objectively provable, and transparent, such as participant happiness, or measures of natural resource. What we present in the following sections are backed by weak math proof, with the promise of more formal iterations to follow.

We will touch on a more prescriptive outlook[3] to Neoclassical Economics, with a reference to ‘strong sustainability’ as opposed to the more rigid ‘weak sustainability’ models. We will also examine Futarchy, quadratic voting, taxes, and genuine savings as a base for intergenerational sustainability.

As we go into the nuances of the prototype, keep in mind that what is discussed is with the intention of being the governance layer for a blockchain, in the sense that by using it, we are hoping to create a more sustainable system that will last generations, is amenable to changes, and takes into consideration any unforeseeable events that may occur. It will be the basis for proposals on the system, ranging from changing miner rewards, code updates, to reworking the governance itself. Again, an important thing to remember when developing a formal economic model is to use caution when creating a single, optimal path. Environment, market sentiment, ethics, and information is constantly changing and it is in our best interest to still weigh in ethical implications, uncertainty in consequences of depleting the natural environment, and the role of natural environment in maintaining sustainable development[4]. No formal model can ever perfectly predict the future, and it is much more valuable to be malleable in our approach than all-encompassing.

A primer into sustainability, and the thoughts around different approaches

To understand sustainability in economics, we have to go back to the 1960s when a new wave of environmental concerns emerged in popular, political and social-scientific consciousness. In the beginning, economists took a very formalized, mathematical approach, called “Neoclassic,” or “Marginalist” economics, where the value of something is not in the cost to make it, but of how much someone is willing to pay for it. This creates a microeconomic model, as it is fundamentally a theory of resource scarcity in a static economy. By applying this to environmental economics[5], it created a rigid structure where economic agents exist, their choices are made in fully integrated markets, and they have full and relevant knowledge of their decision problems[6].

Once the 1980s hit we saw a shift into sustainable economics, which was a combination of long term economic, environmental, and social sustainability. Economists were still relying on neoclassic models where optimal growth could be sustained by allowing non-declining welfare in perpetuity. As we now have better understanding of non-renewable resources, as well as resources that have negative growth, this assumption has been disproven[7].

It was the Hartwick rule, and subsequent improvements upon it by Pearce, Atkinson[8] and Hamilton[9], that led to the modern implementation of utilitarian economics of sustainability, where we calculate net investment of produced, natural, and human capital as genuine savings. In essence this was a way to link what was produced to what we have consumed that cannot be renewed. As long as genuine savings was above zero, we are creating an efficient and comparatively better resource than what was already there. This is allowed due to the assumption that one unit of a natural resource is of equal value to one unit of produced capital.

Arguments have been made on this assumption, which is where the distinction needs to be made between “weak sustainability” and “strong sustainability” and why for our purposes we have chosen a strong sustainability model. While we will be building on an implementation of the Hartwick Rule, we will use relaxed discount rates to reflect strong sustainability.

Within weak sustainability, economists operate under the condition that all resources are equally interchangeable, and that while there are risks, each can be optimally modeled. Although this makes it much easier to calculate and predict environmental degradation, it fails to account for unknowns, and more importantly it is a rigidly utilitarian approach to ethical issues. Such rigidity is only possible if we as humans are able to perfectly value the maximal utility of the world or system.

Strong sustainability, on the other hand, follows the line of thinking that there are no easy substitutes for natural capital[10]. We are still allowed to apply a capital approach to sustainability, but the overall cost-benefit calculus of environmental economics must be pared back, because the shadow price of critical natural capital is, by definition, infinite. The main pushback of strong sustainability is that it is reliant on a less formalized precautionary approach, which in turn leads to unnecessary cost in the eyes of utilitarian economists, as we now are accounting for unknowns that are not provably calculable. For our purposes however, we are more concerned with longevity and intergenerationality of the system, and while there may be potentially net positive proposals (compared to market interest rate) that will fail due to our discount rate, it is more valuable to ensure survival over absolute growth.

Futarchy

For our base model, we will be using Ralph Merkle’s Futarchy system, but as we will soon work out, there are issues that need to be addressed.

Futarchy, in summary, is a way to abstract voting complexity away from the average participant within governance. Instead of having to vote for intricate problems, people are asked, on a scale from 0 to 1, how they felt the last year went (referred to as Annual Collective Welfare or ACW). We then create a put and call on the aggregate predicted decaying sum of opinions for the next ~20 years. Whenever there is a new proposal, betters will shift the market to where they feel member opinion will go to with the new proposal (i.e. a proposal that has net positive on the system will push the scale closer to 1).

This has the hopes of solving 5 key problems:

• Low incentive for members to vote: Statistically a vote has no effect on the result (low per person net positive value)
• Voters must spend significant time and effort to fully understand what they are voting for: By forcing people to vote, they will do so pseudorandomly, or based on incomplete information, leading to suboptimal voting decisions
• Vote “influencers” (lobbyists, PACs, political parties) provide systemic misinformation which may lead voters to vote net negatively
• What you are voting for (even if it is well intentioned) may not be what turns out happening
• Voting gives everyone equal say, which goes back to problem (2) of voters who have incomplete or incorrect information voting, leading to mediocre decisions at best

First, what it does solve.

This makes it drastically easier for members to participate, as even if they have incomplete information, their happiness will be relatively equal to the true state of the system. For example, a group does not need to truly understand the benefits of free healthcare for it to improve their life over the next 20 years, which is what the market is in turn betting on occurring. Because it is much easier, it alleviates issue (1) and (2) as a person’s opinion is much more in tune with what will happen, and objective measure of collective happiness simplifies what a proposal has to accomplish. Issue (4) is solved by the fact that proposals can be rolled back as quickly as they are entered, and that market forces will take into consideration the probability of proposals being accomplished, just as we already do with merger/acquisition requests.

Now, what it does not solve, and corresponding solutions.

The most obvious problem is that tyranny of the majority is still a comparatively large issue, where if a minority group is passionate about a certain topic, but most of the group is indifferent or slightly net negative, it might not be passed. We believe that by using quadratic voting, we can better align weight of opinions to match value gained.

Our implementation of quadratic voting.

• Create a tax system for submitting your opinion: We will set 0.5 on a scale of 0 to 1 as a baseline. By submitting 0.5, you pay (1) unit.
• Any opinion aside from 0.5 has a cost that grows quadratically, modeled by (x-0.5)²+1. This means that a vote of 0 or 1 will cost (1.25) units, while 0.6 will only cost (1.01) units.
• At the end of the voting period, all the funds are redistributed back to the voters equally.

This results in a marginal cost increase of 2x-1, a linear growth that can more evenly match user perception of value. We are now incentivizing people to be indifferent when possible, and to only stray when they feel particularly strong about a topic.

The second issue is that vote influencing can still happen, and while you can argue that due to the happiness put/call being a projection over ~20 years, short-term perceptions of happiness would be caught out by long-term net negatives, there are certain attack vectors where this would not be the case. Particularly with complex proposals where there is great asymmetry in understanding of what it does. As noted by Merkle, a discount rate is inherently flawed, and there are certain things that we cannot predict, even a week from now, while other things in the future can be seen as near certainties. For this, we propose a more non-uniform approach to discounting, while also expanding the base decay time to be over ~50 years, where certain events that are perceived to be more or less certain can have their weight reflected by that. This is where we are taking more a prescriptive approach, giving less of an opportunity to “game” the system since we are weighing in uncertain and unforeseen risk. While for now we will directly relate this to our ACW calculation, we will switch this to our more general NAS formula.

Now that we have implemented a tax system and a non-uniform decay period we can prove mathematically that it is no longer possible to game members against their best interests. Let’s take the example a friend brought up: the wealthy voting against universal healthcare.

Say there are 10 members in a group, 9 of them make (100) units a year, and 1 makes (1,000) units a year. They currently all pay out 20% of their income to a universal healthcare solution, meaning (20) and (200) units a year respectively. Because of this, each receives (38) units for healthcare. Every year, the members vote on their perception of last year, putting in (10) units, and if each is indifferent, receives (10) back (the cost is fairly irrelevant, just the scale of cost is important).

Clearly, the rich is paying much more than she is receiving, and would like for this to change. She submits a proposal to get rid of this, and votes 1 for the proposal, paying in (12.5) units. In this setting, the other 9, as rational members, still want to maintain their universal healthcare. Each member only needs to submit a vote of less than 0.4444, which in turn leads to a per person cost of ~(10.03) units (a cumulative extra cost of ~(0.27) units), canceling out the proposal and at the end receiving the benefits of the return tax from the rich person’s vote.

What if the rich person decided to “pay off” the other members to vote for the bill? Let’s just assume any average happiness value above 0.5 will do the trick, it doesn’t matter how far above the cutoff it goes, just that in the end the proposal is passed. Because of both the tax, and the inclusion of weighting happiness across ~50 years (the base decay rate would be sufficient since there are no uncertainties in our test), the rich person would have to pay out the exact present value of what each member would receive from the universal healthcare at the opportunity cost rate + the tax uptick (slightly negligible). Anything less and the betting markets would calculate a decrease in member happiness, which in turn would lead to the proposal not being passed. Paying out the right number of people to pass the proposal works out to be more expensive than the present value of the (162) units our rich person stands to lose every year (162 units since she will receive 38 units as well from the healthcare).

Another problem is lack of incentive for net positive proposals to be submitted, as well as currently the ability to make money submitting “bad” proposals, or ones with net negative effects, since submitters of bad proposals can take advantage of the betting market. The solution is fairly speculative, and can be changed quite easily given the nature of Futarchy. For the two issues, we introduce the idea of a reward system for submitting net positive proposals, where the submitter receives 1% of the positive value tax due to her proposal.

Say, in a 1,000 person group, a user submits a proposal that causes a net gain of 3% per user on average. This would result in each user submitting 0.53, and paying out (0.0009) units more on average. The submitter would receive 1% of this extra amount from each user, resulting in getting (0.009) units for their hard work. As user count scales, this number increases linearly, and as net gain increases, the reward increases quadratically.

Sustainability

The last issue with Futarchy is twofold, with both centralizing around the issue of sustainability and whether happiness, even expressed over ~50 years is an accurate portrayal of sustainable development. For the rest of the paper, whenever we reference sustainable development, we will be referring to the idea of sustaining the world’s (or system’s) capacity to meet human needs and provide human welfare.

Currently, the only true negative side effect of net negative proposals being accepted is the idea that at some point, if enough negative proposals are pushed through, we will see a collapse of the system itself, meaning nobody, not even the short sellers, could cash in. While this could theoretically be enough if we had perfect understanding of all negative effects, in truth, such an assumption could result in an over-reliance on descriptive[4] pricing, and fail to consider unknown risks.

Futarchy makes the assumption that even with a simple question like how did the last year go, that humans can accurately arrive at a collective position of how the system stands. It is usually the case, however, that a person’s perception of how things are is not a realistic reflection of what has happened, something that becomes increasingly apparent as we try to gauge into the future. We as humans may be collectively happy for the next 50 years, but could be doing irreparable damage to natural resources or future welfare that we have no knowledge of. This is why we must introduce a more standardized calculation of sustainability, one that focuses on intergenerational value. Again, our aim is to avoid indirect taxes, and get as close as possible to mirroring the real world while still being capable of changing the formula.

Adjusted Net Savings

Our implementation of the Hartwick Rule will be based off of the Adjusted Net Savings Calculator used by the World Bank to assess how sustainable countries are acting every year[11]. The formula can be found below.

• Gross national saving. Calculated as the difference between GNI and public and private consumption.
• Consumption of fixed capital was subtracted. This represents the replacement value of capital used up in the process of production.
• Current spending on education was added. As a lower-bound first approximation, the calculation thus included current operating expenditures in education, including wages and salaries and excluding capital investments in buildings and equipment.
• Rent from the depletion of natural resources was subtracted. Rent was measured as the market value of extracted material minus the average extraction cost.
• Damages from Carbon Dioxide emissions were subtracted. This calculation effectively expands the notion of a national “asset” yet further to include its unpolluted air.

By using this, countries could see if their consumption of natural capital was equally matching production of other resources. Our job will be to now take this, and define what “resources” and “products” will comprise a blockchain system. While it would be nice to just rely on products in our calculation, such as using our ACW, we need to take into account that there are costs with running the system, and if one person is gaining value but it costs millions of dollars, this is not a sustainable protocol.

Resources:

• All fixed, depreciating capital used to maintain state of the system. Mining machines, buildings, and other assets that have long life spans. This closely resembles D in the formula.
• Rent from depletion of natural capital in a blockchain system will represent two things, the first being the difference between mining/transaction rewards and the operational cost of receiving that reward. We can include electricity and costs of maintaining the network between nodes. The second rent calculation will be storage rent of placing new information onto the blockchain multiplied by the amount of new information placed in the year.
• In the future we may include more long-tail resources related to running a protocol, such as environmental effects, or human capital. For our simplified version, Rent and Depreciation will be enough.

Products:

• The biggest change we are making to the formula is in the calculation of Gross National Saving, as well as Gross National Income. If we think about what we are aiming to maintain in blockchain system, there are two things. The first is overall happiness (ACW), or Consumer Capital[12]. The issue is that currently this has no monetary value, which we can fix by using the quadratic taxing formula we defined earlier (this will now be known as Annual Collective Welfare Tax or ACWT). GNI will now be a summation of all participant’s tax submissions subtracted by the 1% we give to proposal submitters (Net Annual Collective Welfare Tax or NACWT). Consumption will become the Average Happiness Indicator (1 Unit * number of participants: AHI).
• The second is a system where no one entity has control over any aspect of state. There are a few ways we can mirror sufficient decentralization. We could calculate the probability of a random node winning a reward (to show decentralization in state progression) multiplied by total number of non-pooled nodes competing, or it could be a focus on the number of proposals submitted by different people (which would also be a representation of a system where education is encouraged). Whatever this is based on, it will be auxiliary indicator of an uncompromisable blockchain. This part will need the most discussion on, especially around what it means to be decentralized, and what level of decentralization is necessary (do we need equal participation or just assurance that nobody can take control).

Using these resources and products, we can now create our own version of the formula:

From this, we can now calculate a per yearly NAS that is reflective of the current state of the system. Any proposal will have either a negative or positive affect, with markets reacting accordingly if we prescribe to the efficient-market hypothesis. Betters will be able to create long-form bets predicting into the infinite future (for our purposes ~50 years) of what will happen with a given proposal, assuring us that any proposal above the current NAS will have long term benefit, and thus should be accepted.

You’ll notice that we do not include Market Capitalization, or any invariant of financial worth of the system, into our formula. This has been chosen deliberately both from a social economics and sustainability perspective. There has been a provably low correlation between GDP (and individual income) and happiness [12][13], and since it is our job to make sure we are actively ensuring participant happiness over financial growth, it is in our best interests to use ACW as a measure of success over market capitalization.

After we take an NAS calculation at year 0, we then must project all future NAS’ ~50 years into the future using our exponential weight decay formula to compute a present value, taking into consideration any varying rates due to unknowns. Once we have this, we can now see whether the particular proposal has sufficient sustainability to be accepted.

In Practice

Imagine a blockchain system much like Ethereum, but instead of the current governance model we have this new formula for accepting proposals. We are looking to implement Proof of Stake, and we submit a proposal outlining the code change as well as projections into changes of resources and products.

Currently, our measures are as follows:

• ACWT = 10,169,000 (~1.017 ETH (0.63 Average CW) * 10,000,000 users)
• NACWT = 10,167,310 (10,169,000 ETH-(169,000 ETH * 0.01)
• AHI = 10,000,000 (1 ETH * 10,000,000 users) ETH
• DS = 200,000 (~5 ETH reward per node/yr * 40,000 active nodes) ETH
• R(electricity) = 120,000 (~3 ETH cost per node/yr * 40,000 active nodes) ETH
• R(storage) = 125,000 (~0.005 ETH cost/byte stored * 25,000,000 bytes stored) ETH
• D = 40,000 (~1 ETH cost per node/yr * 40,000 active nodes) ETH

Using this, we can see we have an NAS of 0.0081, slightly above our cutoff of 0, which shows we are positively sustainable for this year. But, we need to take into account that we need to project all following NAS’ for the next ~50 years, and as we continue to use Proof of Work, we see a consolidation of nodes, leading to decreases in user happiness as well. Electricity per node goes down, as does depreciation due to economies of scale. After calculating all future savings, we arrive at a present value of -0.05 (fictional value for the purposes of this example), meaning that our current system is unsustainable.

Now we will introduce our new measures from the PoS implementation:

• ACWT = 10,327,240 (~1.033 ETH (0.68 Average CW) * 10,000,000 users)
• NACWT = 10,323,968 (10,327,240 ETH-(327,240 ETH * 0.01)
• AHI = 10,000,000 (1 ETH * 10,000,000 users) ETH
• DS = 200,000 (~5 ETH reward per node/yr * 40,000 active nodes)
• R(electricity) = 80,000 (~2 ETH cost per node/yr * 40,000 active nodes)
• R(storage) = 125,000 (~0.005 ETH cost/byte stored * 25,000,000 bytes stored)
• D = 20,000 (~0.5 ETH cost per node/yr * 40,000 active nodes)

Proof of Stake gives us immediate NAS net positive results, as we now have a value of 0.029. More importantly however, is that Proof of Stake allows for a much slower consolidation of power (as they accumulate more rewards, they can use it to get even more rewards), since in PoW you have to factor in that as you get more rewards, you can build faster machines putting you at an even further advantage over your competitors. Due to this, happiness remains above 0.50 for longer, leading to a present value projection of 0.12 (again, fictional value for the purposes of this example). This projection is higher than our previous value of -0.05 and puts us into sustainable territory. Because of this, the proposal will be accepted and implemented.

Arguments

One argument you may have is that this requires many subjective projections into how proposals will affect our ecosystem. While this is true, as we have discovered in the trading of public companies, we are fairly accurate at predicting futures, and as betters want to optimize their earnings, we will develop fairly sophisticated models for each point, especially since we focused on making them as objectively provable and transparent as possible. There have already been many calculations into electricity usage costs, and while happiness is a subjective frame of mind, we can find optimizations with comparing what it means to be happy.

Another possible negative is that there may be other factors that we have not taken into consideration, and most likely that is true. The benefit of this model is that you can vote on another model altogether with a proposal. A model changing proposal would only be accepted if people calculate that it will lead to a more sustainable protocol, mitigating the chance of someone submitting one try to make short term gains. The market would react with a projection of NAS’ leading to lower overall values since it will not be as efficient as the current model. A better model, however, will move to higher sustainability since it will raise future NAS, leading to it being accepted as the new formula for calculating acceptance of proposals.

Conclusion

The practice of calculating sustainability, especially indefinite sustainability, is more of an art than science, and we are making no claims to be an all-encompassing path to solving that problem. What we are doing however, is laying the groundworks for a new frame of thinking, a robust way for us to objectively pick our own future.

What is most valuable is that for the first time, we are valuing Subjective Well Being as our source of choice. We are not concerned with transaction fees, scalability, market value, or ease of use, because those are not the objective end goal. Those are the stimuli for our well-being. If we have a poor servicing protocol that raises our happiness, in our minds that is enough to ensure sustainability. Because as long as people draw value from what you are building, then that is enough. That is the cypherpunk way. That is Satoshi’s dream.

Thanks to all those in the Economics and Crypto/Blockchain community as well for their passion and dedication in creating the next evolution of humanity.

1. https://publicadministration.un.org/publications/content/PDFs/E-Library%20Archives/2000%20Economic%20Governance_Guidelines%20for%20Effective%20Financial%20Management.pdf
2. http://siteresources.worldbank.org/INTWBIGOVANTCOR/Resources/1740479-1149112210081/2604389-1149699431307/edouardpresentation_munich_inwent.pdf
3. http://mudancasclimaticas.cptec.inpe.br/~rmclima/pdfs/destaques/sternreview_report_complete.pdf
4. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.365.6128&rep=rep1&type=pdf
5. https://supreme.justia.com/cases/federal/us/471/343/
6. https://sites.google.com/a/akajok.space/cailinopkeloise/mathematics-for-economists-an-integrated-approach-by-e-roy-weintraub-1985-11-27
7. https://academic.oup.com/restud/article-abstract/41/5/29/1522050?redirectedFrom=fulltext
8. https://www.sciencedirect.com/science/article/pii/0921800993900399?via%3Dihub
9. https://www.sciencedirect.com/science/article/pii/0301420794900485
10. http://dieoff.org/page88.htm
11. https://siteresources.worldbank.org/INTEEI/1105643-1115814965717/20486606/Savingsmanual2002.pdf
12. https://www.ru.nl/publish/pages/516298/nice-12108.pdf
13. http://docenti.unisi.it/stefanobartolini/wp-content/uploads/sites/19/2016/03/Happy-for-how-long-How-social-capital-and-economic-growth-relate-to-happiness-over-time_2014_Ecological-Economics.pdf

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