Many people believe that Crypto will slowly change the nature of capital markets. While the 2016-2020 period in crypto can be vaguely characterised as an aimless exploration of smart-contract platforms to explore PMF, the DeFi summer of 2020 changed things.

Since the beginning of DeFi summer, we have turbo-run/experimented with various arcs of off-chain finance and learnt several lessons. Most of these experiments were cheap imitations of off-chain finance. We saw the rise and fall of pool-2 farming, Lending protocols, Decentralized exchanges, Perpetual exchanges, Liquidity bonding protocols, Index funds, and more. We also saw several iterations of algorithmic stablecoins, which, in some sense, always broke due to the excess leverage embedded in their system, akin to some of the meltdowns in the financial world. Today, in the decentralized onchain world, only a few of these original protocols survive and have become crucial to the DeFi ecosystem. We can bucket these protocols in several micro granular buckets and abstractions. Still, broadly speaking, the ones that have survived and/or currently thriving are decentralized exchanges, lending protocols, yield trading protocols, perpetual exchanges, and RWA protocols. This is not a comprehensive list by any means, but it broadly covers all grounds.

DeFi has slowly matured, making systems more efficient. While many have repeated these words, a vision of crypto was banking the unbanked, democratizing access to financial instruments and building the future of France compared to the archaic, slow and rigid system of the current financial world. Talking about the DeFi Renaissance and the second coming of crypto’s original PMF is an undertaking that merits its own article. In this blog, we will be talking about something else.

While we have grown the pie since the early DeFi summer days, there are still many key pieces missing from this story to make it a complete alternative to the financial world.

One core piece that has always been missing from the DeFi landscape is undercollateralized lending. I thought undercollateralized lending would be explored in the coming years in my lists from early 2022 and early 2023 on open problems in DeFi and Crypto. What makes it difficult is, ultimately, an entity is represented as an address onchain. This is all the information you have about its existence. They may have multiple wallets, but none of this adds any helpful information about their credit health. To develop a credit history in the real world, you usually use credit cards often, repay dues on time to get a credit score ascribed to you, and do similar things with borrowing-repayment activities. You are eligible for loans when you have a credit score above a certain level. Better credit scores lead to better loan terms, but they are not fully secured and can be secured by a part of the loan. Undercollateralized loan systems are the heartbeat of the financial world, and when you look at it through that lens, credit is the heartbeat of the modern capital world.

The protocol name from my second list, Wildcat Protocol, is what we will discuss in this blog. I will try to give a brief overview of the protocol itself, its structure, and the various things it allows its users to do. However, around and after this exploration, I would like to weave in the importance of Private credit markets for ecosystems, how they’ve grown and become critical indicators of market health in financial ecosystems, why undercollateralized lending has been challenging, and how Wildcat took a thoughtful yet obvious-in-hindsight approach to bring this instrument on-chain. With Wildcat, we close a loop on DeFi as a decentralized mechanism that utilises blockchains as coordination tools for financial capital among various off-chain parties. In short, it lays the foundation for decentralized finance. Future protocols will be advancements of these building blocks, already seen as order books make their way to on-chain Dexes, better lending markets of different flavours in the form of Morpho and Euler, etc. Future protocols will either be in the spirit of making things more efficient for the base protocol, unlocking more users by making usage seamless with better/advanced UX, or reinventing some of these core cogs in the DeFi machinery with a different lens.

The Allure of Credit: From tradfi to crypto

Before we delve into Wildcat, pondering the nature of credit is worth considering. Credit is more than just financial plumbing; it's often the invisible hand guiding market sentiment, a powerful indicator of where we stand in the economic cycle. Its expansion fuels booms, inflating asset prices and fostering a collective belief in perpetual motion. Its sudden and sharp contraction signals the hangover, the brutal return to reality. As seen below, something different about this cycle till now has been the absence of credit, which has essentially been credit-starved, which can also be seen in the severe contraction of the Alt market and the expansion of bitcoin dominance in crypto. When credit flows freely, markets soar; when it dries up, they crater, especially when funding smaller parts of the market.

This isn't a new phenomenon. History is filled with examples. The South Sea Bubble, the railway manias of the 19th century, the Roaring Twenties, the dot-com bubble, and the 2008 Global Financial Crisis all bear the fingerprints of credit excess. In the book Devil Takes the Hindmost, the author notes about the 1822-1825 credit cycle: “The boom of 1822-25 can be understood as the product of easy credit conditions; During the boom the restricted growth of credit caused asset prices to rise, stimulating further credit creation”. During credit cycles, "people become convinced the prosperity will last forever". Declining yields on traditional safe assets and excess capital can make speculative ventures seem particularly attractive.

The ascent of private credit from a niche market segment to a significant force in the financial ecosystem has been a defining trend, particularly in the years following the Global Financial Crisis (GFC). Initially, the growth was primarily a response to a shifting regulatory landscape. Post-GFC regulations (like Dodd-Frank and Basel III) imposed stricter capital requirements and risk aversion on traditional banks. This led banks to curtail lending to specific segments, particularly middle-market companies and more complex or esoteric credits, creating a substantial funding gap. This created a vacuum, a "mismatch of supply and demand," where private credit providers stepped in to fill the financing gap. The persistent low-interest-rate environment following the Global Financial Crisis (GFC) also played a crucial role, compelling institutional investors, such as pension funds and insurers, to seek higher yields, which private credit, often with its floating-rate structures, could offer. Private credit funds stepped in to fill this void, offering an alternative source of capital for businesses that found it increasingly difficult to secure financing through conventional banking channels.

Today, private credit is recognized as a cornerstone of the modern financial ecosystem, not merely as an alternative but as an essential component of capital provision. Its importance stems from its role in financing a significant segment of the economy that might otherwise struggle for adequate funding. For investors, it offers the potential for attractive risk-adjusted returns, portfolio diversification, and current income, often with floating rates that provide a hedge against rising interest rates. Regulatory shifts continue to create opportunities for private credit to expand into new areas as banks step away from certain types of lending, further cementing their role in the financial landscape.

Private credit funds operate with less regulatory oversight and offer bespoke, flexible terms. The market evolved into a multi-trillion-dollar force, driven by institutional investors seeking higher yields in a low-interest-rate environment. However, this growth came with inherent risks, many of which stemmed from the market's opacity. Valuing private assets is challenging, often relying on less frequent, more subjective models, which can obscure underlying volatility and delay the recognition of losses. This lack of transparency means systemic risks can build up unnoticed until a shock exposes the vulnerabilities.

Crypto's recent history provides a hyper-accelerated version of these credit cycles. The CeFi lending boom saw platforms like Celsius, BlockFi, and Genesis become titans, their loan books swelling in lockstep with soaring crypto valuations. At its peak, the combined CeFi and DeFi lending market was valued at over $64 billion. But much of this growth was fueled by practices like massive unsecured loans, rampant asset-liability mismatches, and the acceptance of increasingly esoteric and illiquid collateral. The lack of transparency was staggering; the extent of leverage and interconnectedness across these CeFi giants was largely unknown until they began to topple like dominoes in 2022. The collapse of Terra/Luna, followed by the failures of 3AC, Voyager, Celsius, BlockFi, and Genesis, wiped out tens of billions in value and revealed a system riddled with unsustainable risk. The very entities that marketed themselves as the sophisticated bridge between traditional finance and crypto were often operating with less prudence than a casino. For more, refer to Galaxy Research’s “The state of crypto lending”.

The story of lending onchain is a story of growth. Looking at the following two charts together, it is evident that DeFi lending has ushered into a bigger cycle and is growing, taking over the share/heavy lifting of the role of CeFi lending in a post-FTX world. However, DeFi lending is essentially overcollateralized, so the crypto world still suffers from a credit crunch.

Wildcat: The Protocol

Against this backdrop of opaque CeFi meltdowns and the inherent limitations of purely pseudonymous DeFi, Wildcat emerges.

Today, Credit in crypto is not managed openly. If you have the connections and ability to form deals, you might be able to strike some over-the-counter (OTC) deals in Telegram channels, through private deals, etc. There are no longer credit lines like those that existed in the previous cycle. Wildcat is ultimately an attempt to bring them into the open, on-chain, and let this transparency also support and bolster confidence in investors, sparking a new crypto credit cycle, one that we can see.

Note: I refer to the Wildcat Protocol V2 whenever mentioned in this article.

As I mentioned, facilitating undercollateralized loans in the onchain world is difficult, exacerbated by anonymity and the general lack of trustworthy information. While explorations into ZK-proofs of identity or credit scores are slowly emerging, there's another way to approach this: by focusing on institutions. Institutions operate on a different spectrum of trustworthiness compared to the challenge of assessing an individual, anonymous address. They are typically incorporated entities, allowing for basic legal contracts to be written and memorialized onchain, underpinning the legality of any arrangement. While bankruptcy remains a distinct possibility, pursuing recourse for fraud is potentially more straightforward. This institutional focus offers a middle ground between traditional finance's opaque, audit-reliant systems and the fully transparent but often overcollateralized nature of existing onchain loans. Businesses constantly seek capital to expand, become more efficient, and find alternative credit lines, and there's a corresponding supply of lenders looking to diversify and achieve specific return profiles.

The failures in the crypto space over the last few years, particularly the collapse of several centralized finance businesses often mired in fraudulent practices, underscore the need for alternatives. While onchain systems are not a panacea, if someone is determined to commit fraud, they will do so; they can offer an additional layer of defence. Transparency can help identify issues before they escalate catastrophically. We've often viewed crypto protocols as mere facilitators, but at their core, they are also powerful coordination tools. They can help solve the discoverability problem: where does capital demand meet capital supply? Wildcat steps directly into this, aiming to make on-chain credit more transparent, accessible, and, crucially, customizable.

Observing Wildcat's mechanics, its approach facilitates direct credit relationships between identifiable, KYB-verified business entities and their lenders, using Ethereum as a transparent settlement and coordination layer. This isn't about anonymous lending pools; it's about enabling specific, documented credit lines where the terms are explicit, and all activity is recorded onchain. One might then consider this structure: in practice, it begins to resemble a marketplace for something akin to liquid bonds, with the distinction that the debt instruments are formed through deposits into these onchain vaults. It also becomes a tool to mitigate the daily operational complexities of managing their existing credit lines.

It's crucial to understand that Wildcat facilitates undercollateralized credit. This means lenders are directly exposed to counterparty risk. While the protocol provides lender protections like penalty APRs for delinquent borrowers and clear mechanisms for managing withdrawals (even if they exceed reserves, using a pro-rata system and a queue), it does not underwrite any loans or shield against defaults. It functions primarily as middleware that coordinates and facilitates these loans. The inherent risk of default often justifies the potential for higher returns.

The power of Wildcat lies in the extensive control it gives borrowers. They can define nearly every aspect of their credit markets: the asset they wish to borrow, capacity, interest and penalty rates, reserve ratios, withdrawal cycle lengths, minimum deposit amounts, whether the debt tokens are transferable, and lockup durations. Borrowers can also manage their markets, adjust parameters (with some protections for lenders, like constraints on APR reduction), and eventually close them by repaying all debt.

This profound customization is made possible by the pre-transaction hook system. Think of hooks as customizable security checkpoints that borrowers can set up at the entrance to their lending markets. More technically, these are tiny, programmable "yes/no" gatekeepers that run checks before any core market action, like a deposit, withdrawal, APR change, or token transfer, hits the main contract. Borrowers select hook templates (covering access control, fixed terms, minimum deposits, or transfer restrictions) from an approved library. When a market is deployed, the "Hook Factory" clones these templates into immutable "hook instances" specifically bound to that market. These instances can enforce various conditions, from a simple OFAC check to requiring a zero-knowledge proof of off-chain revenue, all without bloating the base market contract. Lenders, in turn, can see exactly which hooks guard a market and decide if the baked-in rules suit their risk appetite.

To put it simply, pre-transaction hooks are:

• Defined by Borrowers: The creators of the credit market set these rules.

• Varied in Complexity: They can be straightforward (e.g., checking a sanctions list) or more intricate (e.g., verifying possession of a specific soulbound token or membership in an approved lender list via a Merkle tree).

• Purposeful: They grant borrowers granular control over who can lend to them and under what conditions, effectively gating access or extending the market's functionality before any funds move or core actions are taken.

For lenders, Wildcat aims to offer choice and transparency. Once a lender obtains the necessary credentials for a market (which can be configured by the borrower to be anything from open access to highly restrictive), the interaction resembles that of a standard DeFi vault. They can assess the terms various borrowers offer—market makers, crypto banking startups, hedge funds, and DAOs. Lenders can simply choose to lend to someone else if the terms aren't suitable or if a borrower seems too opaque or risky (a critical consideration in a post-FTX world). Wildcat plans to support this with borrower profiles for disclosure and template loan agreements to provide legal recourse in default scenarios. While debt tokens issued by markets are generally tradable on secondary markets, borrowers can also restrict this if needed.

The range of potential borrowers is broad. Market makers and hedge funds are obvious candidates, as are DAOs and protocol foundations. Today, foundations often raise money through token sales, which can be locked for years or create immediate selling pressure on their asset. As onchain protocols mature and find sustainable revenue streams, an alternative form of credit, where funds are lent at a decided-upon interest rate, will be a game-changer. For instance, if a successful protocol like Hyperliquid decided to raise funds for development through, say, 14% interest vaults on Wildcat, one can imagine significant interest.

It's possible to view undercollateralized lending as contrary to DeFi's "don't trust, verify" ethos. However, as protocols mature, exploring possibilities and pushing constraints is vital. DeFi is not just about transposing the traditional financial world onto a blockchain; it's also about democratizing access to these powerful instruments. Frankly, what is more decentralized than opening up mass access to one of the most essential components of an economy? Wildcat creates infrastructure by bringing credit relationships on-chain with transparent terms, automatic enforcement mechanisms (within the protocol's logic), and democratized access. In this system, credit can flow more efficiently through digital currency networks. Whether this represents DeFi's natural evolution or a hybrid approach blending traditional finance with blockchain benefits is for the market to decide. Ultimately, Wildcat is implemented in a way that allows for extensions, including ZK proofs of assets or reserves on the borrower side, making the protocol more straightforward and, in a sense, future-proofing itself to benefit from technological advancements.

The vision for Wildcat extends beyond serving existing DeFi users. It's about opening up access to a vital part of the economy for everyone to access. In the future, as tokenization becomes more widespread and more individuals and businesses participate in the decentralized financial economy, it's conceivable that even a local bakery with access to tokenized euros could crowdfund and manage a credit line on-chain with minimal friction, backed by off-chain legal agreements. It’s about leveraging the transparency and efficiency of blockchain to build a more open and accessible credit system for everyone. Additionally, the ECB notes that opaqueness and strong growth in private markets may give rise to financial stability risks. Valuation of businesses can be a murky art, with assets marked to market less frequently and under more subjective assumptions, potentially concealing losses and actual volatility. Crypto offers a transparent alternative, and as ZK-based financial auditing grows, it will probably become the standard. The only way to participate in private credit.

Against Wildcat: Challenges for Onchain Undercollateralized Credit

The path is not without significant hurdles. Scepticism is warranted.

The core challenge remains: how do lenders accurately assess the creditworthiness of borrowers, even KYB'd ones, in a space still wrestling with information gaps? For all their size, traditional private credit markets face similar issues of opaqueness. Crypto's past is riddled with platforms that faced difficulties when their borrowers (often SMEs or entities in emerging markets) defaulted. Wildcat's reliance on borrower disclosure, template legal agreements, and the potential future integration of ZK-proofs for financial data takes the right direction. However, building true, verifiable creditworthiness onchain is an ongoing endeavour.

Another significant challenge lies in the transparency and potential for manipulation within on-chain lending platforms. While on-chain data offers some visibility, it doesn't always paint a complete picture. For instance, borrowing volumes (and thus perceived platform success or Total Value Locked) can be artificially inflated if borrowers use self-lending loops, repeatedly depositing and borrowing the same capital. While Wildcat aims to provide detailed transaction histories to expose such behaviour, the initial allure of high, albeit manipulated, volumes can be misleading. Furthermore, the on-chain nature of these protocols doesn't inherently solve the problem of off-chain opacity. The complex, often private, trading strategies and asset/liability structures of large institutional borrowers (like the former 3AC) would largely remain hidden, meaning the protocol might not, on its own, prevent collapses stemming from undisclosed off-chain risks.

I think that’s the ultimate question, would wildcat have stopped 3AC? Unlikely to prevent determined fraud. But it could have made their demise less of a black swan. 3AC’s downfall was exacerbated by the opacity of their borrowings. Had their credit lines been on Wildcat, the terms, repayment schedules, and any defaults or refinancing struggles would have been onchain data points, providing earlier stress signals. Wildcat does not mitigate fraud or avoid it, but given the transparent nature of the protocol and onchain transactions, every transaction would have added more information than was otherwise available. Making debt tokens transferable could also allow for quicker price discovery of distressed assets. The 3AC AUM letter was a prime example of how little real information was available.

Additional challenges include the complexities of cross-jurisdictional legal enforcement—what happens when a borrower in one country defaults on lenders spread across multiple jurisdictions? The reliability of KYB processes also varies significantly, and sophisticated bad actors might find ways to appear legitimate. There's also the risk of borrower collusion, where multiple seemingly independent entities could coordinate to extract value from lenders.

Conclusion

Credit is a fundamental part of our financial world, from home mortgages to business loans. It’s everywhere. On-chain credit also exists in cryptocurrency, but it often operates behind the scenes—through private deals, exchange-specific services, or opaque OTC arrangements. It’s time to give it some limelight, and Wildcat is an attempt to do justice to it.

The key is building systems that foster transparency, allow for granular risk management, and provide clear avenues for recourse while enabling the essential flow of capital that drives innovation and growth. The shift from public to private markets in TradFi and the parallel universe of crypto's often-opaque dealings point to the need for better infrastructure.

Wildcat is not a silver bullet. It won't eliminate risk or single-handedly prevent the next market downturn. It offers sophisticated, transparent, and customizable tools for institutional credit to operate onchain. Allowing borrowers to define their terms and lenders to make informed choices aims to create a more efficient and discoverable market for debt.

In traditional finance, the behaviour of private credit markets, particularly credit spreads and the sheer volume of capital flowing into them, has become a vital barometer of broader market health and investor sentiment. When credit spreads tighten, it signals growing confidence, a willingness to embrace risk, and generally looser lending conditions, often preceding or accompanying economic expansion. Conversely, widening spreads and a pullback in private credit availability indicate rising fear, a flight to quality, and a tightening of financial conditions that can foreshadow economic slowdowns or heightened default risk. The sheer volume of capital flowing into and out of private credit and the terms at which it is offered thus provide valuable insights into the underlying strength of businesses and the prevailing sentiment among institutional investors, often acting as a leading indicator for conditions that will eventually surface in more visible public markets.

This dynamic mirrors the crypto world; the explosive growth of often opaque CeFi lending during crypto bull runs, with its aggressive terms and leverage, directly reflected a period of extreme risk appetite. The subsequent deleveraging and credit contraction during downturns, alongside fluctuating borrowing rates and liquidity on DeFi platforms, similarly act as potent indicators of crypto market health, investor confidence, and the overall availability of speculative capital within the digital asset ecosystem. The challenge and opportunity in crypto credit lies in enhancing the transparency of these flows so they can serve as clearer, more reliable signals for the entire market. And I think Wildcat is the right step in this direction.

The journey of DeFi has been one of rapid experimentation, often mirroring the trial-and-error of financial history, but at an accelerated pace. The primitives that have survived—DEXs, collateralized lending, stablecoins—have proven their utility. Undercollateralized lending, particularly for institutions, has remained the elusive next frontier. With protocols like Wildcat, we are not just building another DeFi application but laying down foundational rails for a more mature, more integrated onchain financial system. The credit must and will flow. Wildcat’s contribution is to help direct that flow with greater clarity, precision, and purpose than before onchain. The rest, as always, will depend on the prudence and diligence of those who choose to participate.

As blockchains mature, a critical question that arises at the heart of these networks: tokenomics. In the roughly 16 years of their existence since Bitcoin's inception, we have seen a variety of experiments in this case along the lines of mining coins, premine sales, ICOs for percentages, liquidity pool mining, and recently airdrops becoming the primary means of distribution while teams building the protocols/investors get vested. Several protocols are reaching maturity, i.e.initial team and investor vesting schedules are concluding. These protocols can primarily be categorised into two main groups: network protocols, such as Ethereum and Solana, with their on-chain economies, protocols, and businesses, and DeFi/business protocols that serve users to achieve various on-chain objectives.

A crucial thing for network protocols on a long enough horizon is good tokenomics. They make or break the chain that they are the base asset for. High inflation erodes the value of the asset and makes it undesirable. For perspective, Solana’s inflation rate is 4.6% today, while Ethereum’s base issuance is ≈approximately 0.6 %. The asset is also used to secure the chain, so significantly underpaying or eliminating the rewards of chain validators is not a viable option. High gross inflation dilutes non-stakers, even if the net supply is flat after burns. A frequent or poorly communicated changes in the issuance of base assets could break the social contract of those who hold the assets as investments and wish to participate in the network's growth. The right issuance change could turn around the story of a network and give it the impetus to become successful as well. Ultimately, tokenomics dictates the longevity and utility of a chain’s base asset, preventing scenarios where network demand exists but the underlying token has faded into irrelevance.

In this blog, we explore some recent proposals of issuance change, we also look at some research that relates the staking rate to other things like the lending rate etc. We specifically look at SIMD228, Ethereum issuance conversations (including Minimum Viable Issuance concepts and specific EIP research) & the paper “Competitive equilibria between staking and on-chain lending” and other adjacent work, before finally talking about some crucial considerations in the world of base network assets. While SIMD-228 ultimately did not pass its governance vote, the underlying questions it raised persist and are likely to resurface.

Note: This post explores key debates and proposals primarily from the 2023-2024 period, a critical time for tokenomic evolution, updated with perspectives as of early 2025.

Solana: SIMD-0228 (failed Mar 2025)

SIMD-0228 proposes transitioning Solana’s token issuance model from a fixed, time-based schedule to a more market-driven mechanism that adapts to real-time conditions—specifically, the fraction of total SOL supply that is staked. Under the current approach, token emissions were static, meaning they were fixed-schedule emissions that decreased predictably over time without regard to network dynamics. While we won’t initially discuss whether it should include network dynamics, the proposed formula ties issuance rates to the amount of the supply already securing the network. If a large portion is staked, the protocol can safely emit fewer new tokens, thereby reducing dilution. Conversely, if the staked fraction decreases, the system automatically increases emissions to incentivise more validators and protect network security. In other words, the staking issuance rate was going to be tied to the staked amount of tokens. Another motivation behind this change is that validators were already earning increasing revenue from MEV (maximal-extractable value), so they depend less on block rewards alone; hence, the network no longer needs as high an inflation rate for staking to remain attractive.

The current inflation schedule starts at 8% and decreases by 15% per year until it reaches 1.5% in ~2030. Under SIMD-0228, this issuance curve uses a function of the fraction staked plus a constant term, calibrated so that inflation remains low when staking is high and rises when staking dips below a threshold.

The proposed market-based emission mechanism employs a dynamic formula:

• s is the staking rate

• r is the static inflation rate (derived from a time-decay function starting at 4.68% and declining to 1.5%).

• c is a constant that fine-tunes the inflation adjustment. The choice of this constant essentially determines the target staking rate.

• i is the issuance/interest/inflation rate

This equation would dramatically reduce inflation when stake rates are high while providing automatic protection if network security becomes threatened. At current stake levels (~70%), this would decrease Solana's inflation rate from 4.68% to approximately 0.74%, resulting in a reduction of annual emissions by billions of dollars. The formula incorporates an elegant security threshold that becomes increasingly aggressive below 50% staked tokens, surpassing current emission rates if staking falls to 33%—a critical security threshold for consensus.

What does this ensure? By tying rewards to actual security needs and recognising that validators can earn outside income (e.g., MEV, tips), the proposal aimed to minimise unnecessary inflation (and its associated selling pressure) while preserving robust participation in validation. The ultimate goal is a more intelligent mechanism that evolves in response to network usage rather than a one-size-fits-all curve fixed in advance.

An extensive analysis of the effects of SIMD 228 is not the intent of this blog. You can take a look at it in SIMD-228: A critical analysis. Some broad observations reveal that SIMD-228 functions efficiently in high-stake scenarios, reducing unnecessary dilution while maintaining validator yields when combined with growing MEV revenue, which increased from $60– $ 70 M (full-year 2023) to $430M in 2024. However, simulating low-stake scenarios raises concerns about potential feedback loops, where reduced profitability could trigger validator exits, potentially destabilising the network. A 50-epoch (approximately 100-day) phase-in period was introduced later to mitigate immediate economic shocks, but it still represented a significant validator yield reduction of roughly 84% (4.68% → 0.74% = −84%) at current stake levels. SIMD 228 would have minimally impacted validator economics, as roughly half of the network, ~45–53% (30-day range) of validators, charge 0% commission on staking rewards and would, therefore, be unaffected by inflation changes.

Solana’s token issuance exists to achieve two core goals as is true about any layer 1 network: (1) incentivize validators to stake more SOL, thereby increasing security, and (2) encourage honest participation so that validators run their infrastructure reliably. The challenge is that higher emissions, in theory, secure these goals by “overpaying” for validator participation; however, they also come with a real cost—taxes, commission fees, or general market inefficiencies, as explained by Max Resnick in this blog. Over time, if Solana’s inflation rate remains disconnected from the actual state of security and validator performance, the network risks overspending on rewards while unnecessarily diluting the non-staking community. This is especially evident today, when block producers already earn a substantial income from MEV, and performance metrics, such as timely voting, show little room for further improvement. In other words, if security is largely sufficient and validator behaviour is already near optimal, continuing to issue large amounts of SOL is likely wasteful.

Beyond technical security, SIMD-228 also offered broader ecosystem benefits that align with Solana's vision as a high-performance financial system. As we will see, the staking rate is tied to the DeFi lending rate of a base asset. High inflation effectively penalises active SOL usage in DeFi, NFTs, and other on-chain activities, as non-staked tokens continue to suffer from continuous dilution. This creates an artificial pressure to hoard rather than utilize the network's native asset—precisely the opposite of what a vibrant blockchain ecosystem requires. The proposal's market-driven approach removes arbitrary human decisions from monetary policy, allowing natural market forces to determine optimal staking yields while releasing more SOL for productive applications.

We will now explore the rhetoric and research on issuance work in Ethereum land; some of the motivations to change remain the same.

Ethereum Issuance

Minimum Viable Issuance (Anders Elowsson) – Oct 2023

In "Minimum Viable Issuance" by Anders Elowsson, the concept of MVI is introduced and motivated. “Minimum viable issuance” (MVI) is the concept that Ethereum should not issue more ETH than is strictly necessary for security purposes. Issuance beyond the minimum required is viewed as an inflationary tax on regular users, compelling them to stake just to avoid dilution. Under Proof of Work, Ethereum followed MVI by reducing block rewards from 5 to 3 to 2 ETH to curb excessive miner profits. Under Proof-of-Stake (PoS), MVI refers to maintaining a stake (deposit) ratio that is high enough for security but not excessively high. Users shouldn’t feel compelled to stake out of fear of inflation or censorship; staking should reward security, not penalize non-stakers. Every ETH holder benefits if the issuance is minimized without hurting security. Anders argued that lowering issuance (thus staking yield) can increase overall utility for both stakers and non-stakers as long as security remains viable. High yields require stakers to lock up liquidity, expend resources, or trust third parties, which has an opportunity cost. Reducing issuance eases these burdens and reduces dilution of non-stakers, improving network value for everyone. MVI frames a pledge to users that Ethereum will only pay for “just enough” security. Over-issuance is perceived as undermining Ethereum’s utility and decentralisation in the long run.

Properties of Issuance Level (Anders Elowsson) – Jan 2024

In Properties of Issuance Level , some questions around issuance are considered such as “if issuance can be reduced while still retaining consensus stability, proper incentives, and acceptable conditions for solo staking”, “To what extent can Ethereum stop issuing more tokens than what is needed for security” etc.

By early 2024, ~25% of all ETH was staked (post-Merge and post-Shapella), and 28% as of April 2025. Anders argued that Ethereum may be overpaying for security at this level. Both The Merge (which added MEV/execution rewards to staking) and Shapella (which improved withdrawal liquidity) pushed up equilibrium staking participation. This raised the question: Can Ethereum lower issuance (reducing yields) and still maintain a stable and secure consensus?

Anders highlights that the protocol’s base reward factor F is the main “knob” controlling total issuance. Currently fixed (at 64), it yields an idealized protocol APR of

Under the current curve, as total stake D grows, yields drop off proportional to 1/sqrt{D} – but not fast enough to cap participation. In fact, nothing stops essentially all ETH from being staked profitably under status quo issuance.

The post analyzes how different issuance levels affect consensus incentives (e.g. honest behaviour, penalty risks) and reward variability. It asks if we could adopt a reward curve that either flattens or even turns downward beyond a certain stake level, D. For example, could we target an ideal total stake by adjusting yields once that level is reached? Alternatively, could we have negative net rewards beyond some point to disincentivise excess staking? These questions set the stage for exploring alternative issuance formulas that better adhere to MVI.

Anders concludes that Ethereum should indeed decrease issuance at high staking levels. He proposes changing the reward function to a “tempered” curve. This adjustment would cause rewards (and yield) to decline more aggressively as D grows, eventually halting further growth in stake. (In other words, beyond a point, adding more stake yields drastically lower returns, naturally finding an equilibrium stake level.) This concept was the basis for the later “Electra” proposal.

Endgame Staking Economics: A Case for Targeting (Ansgar Dietrichs & Caspar Schwarz-Schilling) – Feb 2024

In Endgame Staking Economics: A Case for Targeting & Electra: Issuance Curve Adjustment Proposal, an issuance curve change was suggested (Validator churn-limit cap EIP-7514 already slows further growth.). They observed February 2024, ~30M ETH (25% of supply) was staked, and climbing (Ansgar and Caspar argue that under current policy this trend will continue unabated – eventually, most or even all ETH could become staked. Critically, the rise of liquid staking tokens (LSTs) like stETH has reduced the friction and opportunity cost of staking, making high participation much more feasible.

The authors foresee a future where most ETH is staked via LSTs and outline why that is problematic. Some of these reasons were also elucidated in The Risks of LSD:

• “Winner-takes-most” LST dominance: LSTs exhibit strong network effects (the more people hold a particular LST, the more useful and liquid it becomes). This could lead to a single, super-dominant staking provider or token. Such an LST might replace ETH as the de facto currency within Ethereum’s economy – exposing all users to that provider’s risks and governance by default. A dominant LST could become “too big to fail,” introducing a new centralized point of failure or control.

• Centralization pressures: High staking ratios amplify economies of scale for staking service providers. Larger providers can operate more efficiently, attracting even more stake. This makes it harder for solo stakers to compete, eroding the decentralization of the validator set. In short, the higher the fraction of ETH staked, the more stake tends to concentrate in big pools.

• Risk to ETH’s role and users: If an LST overshadows ETH as money, users holding plain ETH (for trustlessness) might still be indirectly “taxed” – e.g. missing out on staking yield while suffering dilution. Users may feel compelled to stake or hold LSTs (with added smart contract and trust risks) just to keep up, which undermines Ethereum’s credible, neutral, and self-custodial ethos. Additionally, more validators mean more network overhead (in terms of traffic and state) for nodes to handle.

• Proposal – Stake Ratio Targeting: To avoid an over-staked future, they argue the “endgame” issuance policy should target an optimal staking range, e.g. around ~25% of ETH. Instead of letting all ETH eventually stake, Ethereum would actively adjust issuance to maintain a stable staking ratio. This ensures that security is sufficient but not excessive, thereby limiting the externalities as mentioned above. In their view, targeting a low-to-moderate staking percentage strikes the best balance between security and decentralisation.

Initial Analysis of Stake Distribution (Julian Ma) – Mar 2024

The work, "Initial Analysis of Stake Distribution," asked questions such as: Does the level of staking yield (issuance) affect how people stake (i.e., via solo node, decentralised pool, centralised exchange, or not at all)? There was a legitimate concern that lowering rewards might encourage more people to use liquid staking or centralised providers to maximise yield. Julian’s analysis models investor choices to investigate this concern.

The post outlines a simple utility model in which an investor allocates their ETH to the option that yields the highest combination of benefits, taking into account both yield and personal preference. Preferences represent non-monetary considerations, such as decentralisation, convenience, or trust. Hypothesis 1: An investor’s preference ranking for staking methods is independent of the yield level – meaning, lowering overall APR doesn’t suddenly make a decentralization-lover choose a centralized exchange, or vice versa. They will stake or not stake based on whether the yield meets their threshold, but their decision to stake is driven by other factors, such as technical expertise, values, etc.

The model finds that if an investor decides to stake at all, they will go all-in on the single option that maximises their utility, rather than splitting their stake. Crucially, it shows that the mix of staking modalities (solo vs. pooled) remains roughly the same regardless of issuance level. This result counters the fear that reducing issuance (and thus lowering yields) would deter people from solo staking and drive them toward LSTS or exchanges. It suggests that reducing rewards won’t necessarily decrease the share of solo stakers. Investors who highly value self-custody or trustlessness will continue solo staking even with lower returns, and those who prefer convenience will use services even at higher returns. Stake vs. not stake is influenced by yield, but how to stake is relatively insensitive to it.

The analysis assumes fixed preferences and zero friction. In reality, extreme changes or secondary effects could introduce nuances (e.g. if very low yields mean only large operators cover costs). But as a first-order approximation, it supports the argument that a moderate issuance reduction won’t centralize staking by itself. Other measures, such as education and tooling, are needed to boost solo staking, but we do not need to overpay on issuance to preserve the current distribution.

“Reward Curve with Tempered Issuance” (Anders’ EIP Research Post) – Apr 2024

In “Reward Curve with Tempered Issuance,” Anders Elowsson presents the case for adopting a “tempered” reward curve in Ethereum’s protocol, essentially laying the academic groundwork behind the Electra change. Anders notes that after The Merge and Shapella, staking participation grew significantly, and “there is broad consensus that the current deposit size keeps Ethereum sufficiently secure”. However, under the present curve, issuance will rise substantially as more stake comes in, eventually yielding diminishing returns (or worse) for Ethereum. If Ethereum continues to offer higher yields than needed, it “compels its users to incur higher costs, degrading user utility in aggregate.” In short, excessive incentives become perverse subsidies. This post systematically examines the benefits of moderating issuance and compares various methods for achieving this goal.

• Proposed Curve (“Option A”): The candidate reward curve divides the current formula by (1 + D/k). Here, D is the total ETH staked, and k is a constant that sets the inflection point. Under this curve, issuance (and yield) still grows with more stake, but after a threshold, it grows much more slowly – eventually plateauing. Notably, the peak issuance rate occurs at D = k, beyond which additional staking actually reduces per-validator rewards, halving issuance at D = 2k, and so on. An initial k = 2^26, i.e., 67 million ETH, is suggested, which would place the peak around 55% of today’s supply, with a future adjustment to k = 2^25, i.e., 33.6 million (about 25% of the supply). These choices reflect a graduated approach: start by capping at a relatively high stake level, then potentially tighten the cap later once effects are observed.

• Benefits & Trade-offs: The tempered curve is designed to maintain reliable consensus incentives and security while eliminating only the excess. The following criterias are examined:

  • Discouragement attacks & cartel risk: The network must avoid scenarios where low rewards could tempt validators to drop out or collude. The proposal maintains a non-zero baseline yield even at high D, ensuring there’s always incentive to stay online and honest. Critical attack thresholds (like a 1/3 cartel) would still require far more stake to go offline than plausible if k is well-chosen.

  • Stake Decentralisation: By limiting the total stake, the curve indirectly limits the growth of any single staking provider, as the entire pie stops expanding. This helps keep the staking set diverse, especially if k is adjusted downward over time.

  • Solo Staking Conditions: The post emphasises maintaining viable conditions for solo validators. With tempered issuance, equilibrium yields for stakers might actually be higher than under the status quo endgame. For example, if Ethereum ended up with 60M staked under the current policy, yields could drop to ~2%; however, if a tempered policy caps the stake at ~30M, yields might hover around 4% for those participants – benefiting individuals who stake, while still reducing total issuance. All ETH holders, stakers and non-stakers alike, gain from the reduced dilution and improved decentralization.

• Comparison to Alternatives: Anders contrasts this approach with others, such as an even stricter capped issuance curve that sets a hard maximum or a dynamic targeting system. He finds the tempered curve strikes a good balance: it’s simple, autonomous, and doesn’t require complex governance or oracles, yet it achieves most of the gains by naturally finding an equilibrium stake level. In broad strokes, the author suggested tempered issuance policy could itself serve as the endgame solution, obviating the need for active stake targeting. While other’s noted it as a great short-term policy but still favor an eventual explicit targeting mechanism.

“Issuance Issues” Series (Mike Neuder) – Mar–Jun 2024

Ethereum researcher Mike Neuder wrote a three-part series, Issuance Issues, distilling the debate in an accessible way. These pieces provide intuition and address misconceptions around Ethereum’s issuance strategy:

Initial Issue (Part 1, Mar 2024)

Mike opens by noting that the reality of staking in 2024 has changed since the Beacon Chain launched – thanks to developments such as liquid staking, MEV, and Shapella. The future is uncertain, but the decisions made now will shape Ethereum’s security and decentralisation for years to come. With the Electra fork, he urges making a conscious, intentional decision on issuance . Even doing nothing is an active choice; given Ethereum’s march toward an ossified protocol.

The article walks through why more is not always better for stake. It echoes the negative externalities, including the dilution of ETH holders beyond the necessary security, LST dominance, and solo staker struggles, among others. Beyond a certain point, the marginal utility of more stake becomes negative. The piece also addresses why EIP-1559’s burn mechanism doesn’t cancel out issuance concerns (this is fully explored in Part 3). In short, net supply might be constant or deflationary, yet issuance is still redistributing ownership from non-stakers to stakers. Even if ETH’s total supply isn’t increasing, over-issuance can still harm decentralisation and fairness.

Mike outlines possible paths: keep status quo, adopt a one-time curve adjustment (like Anders’ proposal), or aim for a dynamic targeting policy. He compares staking equilibria under different issuance rates (“apples-to-apples”), illustrating how a lower issuance rate could result in fewer total validators but each getting a higher fraction of rewards – thereby achieving a similar or even higher real yield for stakers in equilibrium. A special focus is on the solo staker’s perspective: what level of rewards is needed to justify solo staking given costs and risks? And how do large staking pools change that calculus? The takeaway is that a moderate issuance reduction can still keep solo staking economically rational, especially if it prevents an overly large validator set where only big providers thrive.

Subsequent Soliloquy (Part 2, May 2024)

The second article responds to feedback from the initial post. One critique was that the case for changing issuance relied on speculation about the future. Mike acknowledges the uncertainty (e.g., we cannot know exactly how ETFs, restaking, or macro factors will influence staking). However, he argues that Ethereum’s success will hinge on decisions made under uncertainty, and that this policy should be proactive not reactive.

A key focus here is the distinction between the nominal issuance rate and the real yield for stakers. The author walks through “supply curve” scenarios to show that paradoxically, lowering issuance now could result in higher staking yields in the long run. How? If issuance is lower, fewer people will stake (all else equal), which means the reward pie is shared among fewer validators. Those validators could earn a similar or greater percentage yield than in a world where everyone is staking for a larger pie: in equilibrium, the marginal staker’s “reservation yield” is met. A reduced issuance policy means that equilibrium occurs at a smaller D with a slightly higher yield, rather than a huge D with a very low yield. Thus, stakers aren’t necessarily hurt by a policy change – they might give up some nominal rewards now in exchange for healthier long-term conditions (less competition, more decentralisation, and likely higher percentage returns once the system equilibrates).

Tertiary Treatise (Part 3, Jun 2024)

The third installment tackles a common confusion in “The Great Issuance Debate”: “How can issuance be too high if ETH’s net supply is barely inflationary or even deflationary (thanks to EIP-1559 burns)?. Some community members pointed to Ethereum’s shrinking supply in 2023–24 as evidence that issuance is fine. Mike’s rebuttal: burning fees is largely orthogonal to the issuance policy. Whether the total ETH supply goes up or down isn’t the central issue – what matters is the redistribution effect of issuance.

Ownership Redistribution: The author systematically shows that issuance always shifts relative ownership from non-stakers to stakers, regardless of burns. Imagine two groups: stakers and HODLers. When new ETH is issued to stakers, their share of the total pie increases at the expense of HODLers. Now, burning fees (which come roughly pro-rata from all users via gas) reduces the pie for everyone, but doesn’t change the fact that stakers received newly minted ETH. In fact, a higher burn rate can increase the gap in relative holdings: if the net supply is deflationary, a non-staker’s balance might drop in percentage terms while stakers both avoid that drop and gain new ETH. Thus, a big burn makes it even more important to stake if one wants to maintain their share of the network.

The treatise concludes that net inflation ≠ economic dilution. Even with 0% net supply growth, issuance can be too high if it forces people into staking to avoid losing ground. The burn is a mechanism for value accrual and fee market efficiency, but it does not justify an otherwise suboptimal issuance level. Ethereum should decide issuance based on security and incentive alignment, treating burns as a separate consideration. Once this is understood, it becomes clear that we should evaluate issuance in its own right – and if it’s causing undesirable effects (such as excessive stake centralisation), it should be adjusted irrespective of the burn.

The external effects of staking rate with DeFi

In the work, Competitive equilibria between staking and on-chain lending, Tarun Chitra explores lending markets in Proof of Stake (PoS) systems and starts with a fundamental reality: crypto tokens in these networks play two roles at once. They serve as both the security deposit that protects the network (when staked) and the money that people trade (when circulating). This creates a basic tension - the same token can't be in both places at once. This dual-purpose nature doesn't exist in Bitcoin's Proof of Work, where security comes from external resources (electricity and mining hardware) rather than the token itself. Ethereum currently has a significant portion (estimated around 20-25% as of early 2025) of all staked ETH simultaneously restaked in EigenLayer, illustrating this latent leverage.

The model assumes that people who stake tokens (validators) will behave rationally and allocate their funds to where they earn the best return. If lending suddenly offers better rewards than staking, validators may withdraw their tokens from staking to lend them instead. If many do this at once, it could seriously weaken network security. This mirrors what happens in traditional finance with mortgage-backed securities (MBS), where homeowners' long-term debts are transformed into liquid investments, creating risk if everyone tries to cash out simultaneously. This analogy was explored in a subsequent blog by him, titled “What PoS and DeFi can learn from mortgage-backed securities.”

To understand how validators make decisions, Markowitz portfolio theory is employed - in simple terms, this just means people try to balance risk and reward when investing. Think of it this way: if you have $1,000, you don't usually put it all in one extremely risky investment or all in a very safe one. Most people spread it around based on their level of comfort with risk. In the paper’s model, validators constantly weigh the rewards of staking (network inflation rewards) against lending (interest rates), considering the risks of each. Some validators are more risk-averse than others, which prevents everyone from making the same move at once.

The paper looks at two main scenarios: a simpler one where tokens are either staked or lent, and a more complex one that includes borrowing. In the simpler model, the lending interest rate depends on the percentage of available tokens that are already being borrowed, much like banks charge higher interest when loan demand is high. This mimics how mortgage rates work in traditional finance, though crypto lending has the huge advantage of complete transparency and automated execution through smart contracts.

One of the model's key discoveries is that there's a tipping point where the system can flip from mostly-staked to mostly-lent. Think of it like a crowded theater where a few people leaving is fine, but once enough people head for the exit, everyone rushes out at once. This same behavior happened in mortgage markets during the 2008 crisis, when seemingly stable investments suddenly collapsed as investors all tried to sell at the same time.

The math indicates that deflationary cryptocurrencies (those with a decreasing supply over time, such as Bitcoin) are particularly vulnerable to this security drain. Without sufficient rewards to keep validators staking, lending becomes the more attractive option. Meanwhile, inflationary systems (where supply increases over time) tend to maintain better security because they can keep staking rewards competitive with lending rates. This pattern mirrors the boom-bust cycles in mortgage markets, where periods of easy credit followed by tightening create instability.

The model helps protocol designers find the "sweet spot" where validators can borrow against their stake without undermining network security. This requires careful balancing of borrowing limits, interest rates, and liquidation thresholds, similar to how traditional securitisation markets structure mortgage-backed securities. The big advantage in crypto is that these rules can be enforced by code rather than depending on human judgment and institutional oversight that failed so spectacularly in the mortgage crisis.

Thinking loudly about Issuance

Philosophy of “Moneyness”

The fundamental question at the core of blockchain economic design is not simply technical but philosophical: what truly constitutes "moneyness" in digital assets? Unlike fiat currencies, which derive their legitimacy from government backing, legal tender status, and centuries of institutional usage, blockchain assets must establish their monetary properties through protocol design alone. The introduction of dynamic staking yields—as proposed in SIMD-228, and the eventual target of ethereum issuance (a dynamic staking curve)—creates a tension between market responsiveness and predictability. While floating rates may optimize capital efficiency, they fundamentally alter the asset's perceived stability characteristics. This added complexity in monetary policy potentially undermines the simplistic narrative that users need to understand an asset as "money," which traditionally benefits from straightforward, predictable properties rather than algorithmic complexity. Although traditional money is indeed a system managed through a dynamic monetary policy, it is imperfect.

Engineering for Antifragility

Consider that lowering issuance doesn’t equate to weakening security – if done correctly, it trims excess “buffer” stake while keeping enough validators online. Excessive stake beyond the necessary threshold doesn’t materially increase security, but it does increase the risk of centralisation. But we must ask how low is too low for issuance, highlighting the importance of not undershooting and causing a security shortfall.

While targeting stake is a good idea, The emergence of sophisticated financial engineering in blockchain—particularly liquid staking derivatives (LSDs) and cross-protocol restaking—introduces systemic risks that protocol monetary policies often fail to fully account for. When stake can be simultaneously counted across multiple applications and synthetically looped through derivatives, the actual risk exposure becomes exponentially more complex than a simple measure of "percentage staked" might suggest. These instruments create hidden leverage, potentially triggering devastating cascade failures during market stress. The interconnectedness of these systems means that a protocol's monetary policy cannot be designed in isolation; it must consider how capital flows through an increasingly integrated financial ecosystem where the same collateral is effectively counted multiple times. Consider the following: if, in a system targeting a 25% stake, 10% is restaked, can we consider this 10% portion with the same “purity” and risk parameters as we do for the un-restaked assets? In the event of a slashing of these restaked assets, isn’t the network suddenly at risk? Restaking has made systems more fragile to edge cases and cascades that are difficult to design against.

There is also a rational argument that stake targeting might actually not result into the intended effect. In a world where asset values remain static/appreciate, holders might buy tokens, stake for a minimal yield and seek more restaking opportunities for additional “safe” yield, thus increasing the quantity of restaked yield in the entire system.

There is an argument that complex systems require continuous feedback mechanisms to achieve antifragility—a principle observed across evolution, market economics, and scientific progress. Fixed inflation schedules represent a form of central planning that lacks the responsive adaptation characteristic of resilient systems. The rigid, time-based inflation curve currently used by Solana (and initially borrowed from Cosmos) exemplifies this rigidity—an arbitrary human decision rather than a market-determined equilibrium. Market-based approaches potentially introduce greater adaptability, allowing the system to self-adjust to changing conditions and unexpected shocks. However, we must question whether the specific feedback loop in SIMD-228 (staking percentage) provides sufficient information about the system's true security and health, especially given the complexities introduced by derivative instruments.

The interaction between staking yields and lending rates represents another critical dimension often underexplored in monetary policy discussions. When on-chain lending markets offer yields competitive with or superior to staking returns, capital naturally flows toward higher returns, potentially undermining network security. This phenomenon, where DeFi protocols effectively "cannibalise" security by outbidding the consensus mechanism for capital, creates complex game theory scenarios that static models struggle to capture. A dynamic monetary policy may prevent this competition in high-stake scenarios by reducing inflation (and thus staking returns), but could potentially create dangerous oscillatory effects if lending rates and staking yields repeatedly prompt large capital shifts between the two. Second-order effects and feedback loops present perhaps the greatest challenge in designing resilient token economics. Network stability depends not just on direct parameter adjustments but also on how participants behave in response to these changes.

Ultimately, blockchain monetary policy represents an unprecedented experiment in designing crypto-economic systems without centralized control. While traditional finance relies on central banks, lenders of last resort, and regulatory frameworks to manage crises, decentralised systems must attempt to encode all edge cases and design for adversarial scenarios in their protocol design. They must anticipate worst-case scenarios and ensure their systems can withstand these, eliminating the need for hard forks or network restarts; liveness is a crucial and desirable property in blockchain systems. This requires a balanced approach that incorporates market feedback while maintaining sufficient stability and predictability for everyday users. The tension between creating capital efficiency through dynamic mechanisms versus establishing trusted monetary properties through predictable issuance remains fundamentally unresolved. As these systems mature, they may need to develop more sophisticated response mechanisms that account for derivative instruments, cross-protocol interdependencies, and behavioural economics rather than relying solely on simple metrics like staking percentages to guide monetary policy. Future work must measure effective stake, discounting assets rehypothecated in restaking or DeFi, to maintain honest security budgets until we do, ‘issuance tweaks’ risk becoming whack-a-mole against invisible leverage rather than an actual monetary policy.

We’ve come a long way since the first time I wrote this list in Jan 2022. It has been inspiring to see the space proliferate and build extremely interesting infrastructure for the future of crypto, but we have a long way to go.

I believe that having an open list of problems is a good reminder of progress for a field, nascent or old, research problems or practical applications. These also provide a benchmark to look towards and glance upon while taking stock of the progress the field has made over the years.

While I wrote about open problems just in Defi last time, this time, I attempted to be a little ambitious and cover open problems in crypto. These aren’t just research problems; some will ultimately be protocol-level solutions, and some will even be applications.

Table of Contents

1. Decentralized Finance

2. Mechanism Design

3. MEV

4. Zero Knowledge

5. Other problems in Crypto

Decentralized Finance [Defi]

• Automated risk scoring of lending borrowing pools

  • Possible Solution: Risk assessment without historical data is really hard because it could be so that a pool of users with a good credit history will always pay the loan back. “Credit score” in tradfi.

  • How can we accurately model the risk of default for a pool of borrowers without relying on traditional credit data? → Answering this might lead to a framework for lending pool risk scores.

• A dynamic Lending market parameter model

  • Bad Loans have been increasing. Gauntlet/risk monitoring teams are doing well, but DAOs are moving slowly (Curve example).

  • How do you solve for low liquidity coins? The model could be a function of liquidity and adjust rates dynamically for a given pool.

  • Though there are downfalls to this model, in “Attacks on Dynamic Defi Interest rate curves,” Chitra et al. show that dynamic lending models have more MEV.

• Managing Risk for lenders and distributing risk/ Undercollateralized Loans

  • While active monitoring is excellent. How do we distribute this risk and make it efficient?

  • Is there a world where we could have undercollateralized loans on-chain?

    • Are off-chain contracts the only way? (Wildcat protocol)

• Private Lending

  • Lending protocol transparency of pools has led to the hunting of liquidation levels, which have become Schelling points for traders.

  • How do you design a privacy mechanism that reveals minimal information?

• Designing Cross-chain Defi

  • As lending markets become chain-centric, liquidity across assets is becoming fractionalized.

  • As liquidity generally gets more fractionalized, how do you source liquidity seamlessly at size and settle it?

  • Look at Cross-chain Margining Systems from The Defi Prime Broker for the DEX version.

  • Defining the safety of these protocols in terms of balance between synthetic assets (cross-chain) and Native assets (on-chain)

• What does the future of Spot Dexes look like?

  • Can CLOBs be designed to accommodate tail assets?

    • Is there a unification of AMM/CLOBs beyond CLAMMs possible?

    • CLOBs historically haven’t worked for tail assets (Etherdelta was bad)

• LP Profitability Problem: What is the optimal strategy for passive and active liquidity providers?

  • How can you formulate and model the problem? → Possibility that it doesn’t exist

    • Does Uniswap-X RFQ mean this is not possible onchain? Onchain LPs in uniswap X have become LPs of last resort and get toxic flow possibly.

    • How do you protect LPs better?

• How does DEX design solve for LVR (Loss-Versus-rebalancing)

  • Dynamic fees - How do we best set fees dynamically based on volatility and other signals to optimize returns?

  • Orderflow Discrimination - If you know how to discriminate between uninformed and toxic flow, you could possibly charge them different fees, etc

Open Research Problems - Mechanism Design

• Restaking Equilibria

  • Competitive Equilibria Between staking and on-chain lending discusses how lending and staking equilibria can exist.

  • What does this look like in a restaking world where the return from staking isn’t exactly uniform but definitively higher than simple staking?

  • What does restaking mean for the security of the base layer? Is restaking an anemic phenomenon to the security of a base layer?

• The privacy-information tradeoff for DEXs (privacy-efficiency frontier) - No Free Lunch theorem

  • In Differential privacy in CFMMs, authors show that partial privacy in CFMMs is possible and that there is a tradeoff between price (execution) and privacy. Private Dex Architectures have been rising recently. How do they address this?

• The privacy-information tradeoff for MEV (privacy-efficiency frontier) - No Free Lunch theorem

  • The privacy-efficiency tradeoff in sharing MEV information. Privacy is needed to decentralize the MEV supply chain, but more privacy usually means less efficiency.

• Mechanism Design (Private & Verifiable)

  • Adding ZK to existing mechanisms like CFMMs and auctions does not automatically guarantee strong privacy.

  • Verifiable and auditable mechanisms for applications like auctions, order flow, and matching markets

  • Adopting Mechanisms for a ZK World

• Designing multi-resource Fee markets

  • Resources: Dynamic Pricing for Non-fungible Resources and Transaction fee mechanism design

  • Blockchain resources are being focused on at different granularities (blob market introduced to handle ephemeral data). Does it still make sense to meter all resources at the same level?

  • Finding the right level of pricing granularity between opcodes and full applications to optimize productive efficiency and therefore, develop robust local fee market designs that can practically segment demand and allocate execution efficiently.

  • Multidimensional EIP 1559 Model from Vitalik

Open Problems in MEV

• Minimizing latency advantages in MEV/ Geographic Decentralization

  • Talks by Phil Daian and Robert Miller

  • Low latency provides advantages in optimizing and extracting MEV. This could incentivize geographic centralization if MEV parties co-locate to minimize latency.

  • The challenge is designing MEV systems that are not sensitive to latency and allow geographic distribution of nodes.

  • Ideas include allowing deferred transaction specification at block-building time rather than sending individual transactions.

  • Also, look at “Exploration of MEV Latencies”.

• Credible Private Auctions on-chain

  • In an everything-is-an-auction world, how do we hold auctioneers accountable and trustable?

    • Chitra et al. show a possible world. When will we get this onchain?

    • What are other guarantees that we need for auctions to be practical?

• Design of Order Flow Auctions

  • OFAs follow a common framework with four components: originators/orders, auction/info sharing, bidders/bids, and winning bid/inclusion.

  • Key design decisions exist around order types, information sharing, bidder permissions, bid selection, and execution guarantees.

  • More at “The Orderflow Auction Design Space”

• MEV Distribution Applications

  • MEV is not evil or has any nature associated with it, just an emergent property of an economic system (Can be thought of as inefficiencies being captured)

  • If MEV is captured by the applications and redistributed in the case of AMMs, in essence, LPs would get value or users in the case of sandwiching.

• MEV Mitigation

  • Recent work has shown private RPCs don’t prevent users from experiencing slippage (a popular belief among private RPC users). Are commitments necessary for MEV mitigation?

  • How do you design UX to improve this at the user end?

• A framework for Sequencing rules given payoff/Application?

  • Chitra et al.’s Theory of MEV II proves that MEV handling should be application-specific/ Sequencing criteria for payoffs (properties proven in the paper)

  • Better lower bounds for specific applications/payoffs when sequencing is defined (already shown for CFMMs, O(log n) when abundant liquidity)

• Generalizing PBS

  • How to generalize PBS (proposer-builder separation) to support more flexibility like partial blocks, different block specifications, inclusion lists, etc.

  • Also relevant in the L2 world

    • A key problem is whether to enshrine MEV auctions in the L2 protocol, burn MEV to incentivize proof production, or leave it to proposers and builders. More research is needed on the economics.

    • Other open problems include determining optimal mechanisms for proposer decentralization, managing high compute needs for L2 block production, prover incentivization, and enabling permissionless participation.

• Censorship Resistance Mechanisms

  • Today, five out of the six largest block builders comply with the OFAC sanctions.

  • How do you design mechanisms for avoiding this?

  • To learn more, listen to this talk here.

Some Problems to Upgrade Zero Knowledge

• Impossibility results for Zero-Knowledge Crossovers like ML and Defi

  • Are some applications that are not possible or bounded for some and prevent practical usage?

• Separability results for Zero-knowledge Crossovers

  • Are there any results that possibly allow you to say with ZK, you get better efficiency properties for payoffs, and without ZK, they don’t?

• The Imagenet of ZK - Towards a ZK benchmark

  • Imagenet competition ultimately brought about the revolution in AI in the form of deep neural networks. Alex Krizhevsky wrote custom kernels for training Alexnet, and thus GPUs started getting adopted for Deep learning.

  • Is there a ZK equivalent benchmark to make ZKs more performant?

    • Opening this up as a yearly benchmark incentivizes research groups to work on this.

  • The imagenet of ZK could possibly do the same for ZK (maybe need a yearly competition)

• Torch/Tensorflow for ZK circuits

  • Zero Knowledge feels like it’s in the Cuda era

  • Computational graph models for circuits are similar to how neural networks are written.

  • There is no standard framework for ZK applications

• A Fast.ai for Zero Knowledge

  • Some credit to Deep learning’s growth was the ability to top-down learn and this course design subsequently.

  • What do toy applications for learning ZK look like?

• Zero Knowledge Identity/ ZK-KYC

  • Real-world application with outsized impact

  • It does not have to be strictly KYC-centric

  • Issues around making it government-compliant

• ZK Deep Learning/ZKML

  • Verifiable computation inference?

    • Explored for some models in the Cost of Intelligence: Proving Machine Learning Inference with Zero Knowledge by Modulus Labs.

    • Does this need the existence of ZK provers for Models specifically?

  • Is Zero knowledge Deep learning possible?

  • Some other ZK applications possible - Data/Training Provenance

    • Tools for Verifying Neural Models Training Data Any such addition to a system would help determine compliance issues/or verify that the model was actually trained on the data.

    • Also explored in “Experimenting with Zero-Knowledge Proofs of Training.”

Other Problems in Crypto

• Towards a Definition of Intents

  • Intents have become popular over the last year.

    • A formal definition of intent is still missing and could lead to the design of protocols around them and research.

• Private intent solving

  • What minimum knowledge is needed for an intent/requirement to be filled?

• Compiler/Program Synthesis - User-defined compositions and flexibility → I give you a payoff, and you “compile” it by stringing together other primitives.

  • One way to think of a compiler is you are interested in a certain payoff from the assets you have, so you have a compiler that auto-selects and tries to model the desired payoff from the instruments/primitives available on-chain, “auto yield stacker.”

  • Intents but more abstract - over protocols

  • Lego blocks today are only building Lego blocks of composability for protocol builders.

  • As protocols become sophisticated and yields become siloed, what are ways to allow for user composability of protocols/portfolio building?

  • If the atomicity of protocol stacking is lost due to appchains, how do you solve this?

• A Universal Intent DSL to onchain transaction pipeline

  • As we grow towards better UX models, the idea of users signing transactions for taking money in the wallet to stake money/transact/trade is a high effort.

  • Design rules have morphed from early 2000s web2 3-click-rule to even more effortless UX designs today.

  • When you go to a bank, you tell your relationship manager that you want to stake your money in Fixed deposit/tell your broker to buy you stocks, etc. These things might not apply to sophisticated users. Most users would benefit from intent/”objective” based design.

  • Converting intent to meaningful onchain steps unlocks the next level of meaningful UX.

• Better Wallets

  • As we move to an infinite chain world with multiple chains for apps and multiple app chains for a better experience, how do wallets consolidate this experience while maintaining safety and security?

    • For example, you’d need to keep dust on all chains

  • Wallets need to configure RPCs which are best for users

  • Wallets can be intent solvers/broadly everything apps

• Social Recovery Wallets/Towards Better Wallet Security

  • The user experience of maintaining wallets sucks, and hardware wallets aren’t for everyone.

  • Designing Wallets with social recovery or even models that abstract the maintenance of private keys might pave the way to adoption.

• Coprocessor/Verifiable Off-chain computation architectures

  • Succinct zero-knowledge proofs allow reasoning about secret data owned by one party. Fully homomorphic encryption and MPC allow joint reasoning on secret data. A combination could allow joint reasoning without interaction.

  • The ideal model is a trusted execution environment (TEE) that can run arbitrary programs and keep secrets, but TEEs face challenges with communication and state.

  • What applications are ZK-coprocessors not possible for (Computation heavy possibly) → Designing FHE/MPC-based solutions with blockchains for these applications?

  • Security guarantees of TEEs vs. FHE vs. ZK and the applications that these unlock.

• Coprocessor/Verifiable Off-chain Computation Architectures - Some Applications

  • On-chain Security Monitoring

  • ML/RL controllers for stablecoins

  • Off-chain margining systems

• Gas derivatives

  • Derivatives could allow Ethereum stakeholders like validators, developers, and users to manage risk and volatility in gas prices better. They could pay fixed rates and hedge exposure to spot price fluctuations.

  • Historical analogies exist in markets like oil and VIX futures, where derivatives volumes far exceed the underlying spot market. This shows the potential for major growth in gas/blockspace derivatives.

  • Explored in “Opportunities and Considerations of Ethereum’s Blockspace Future.”

• Designing Encrypted Mempools/Alt Mempools

  • Initial Exploration is done in “Cryptographic Approaches to mempool privacy.”

• Handling State Growth with State Rent

  • How do you price state growth? This is a common problem in highly performant blockchains like Solana and, ultimately, an issue for ethereum as well.

• Decentralized Sequencers

  • How to make the decentralized sequencer implementation faster while preserving security guarantees?

  • There is a tradeoff between speed and trust assumptions while guaranteeing censorship resistance and liveness.

  • Block production on L2s has higher compute needs due to larger proofs, more transactions, and proof generation. This increases centralization risks.

• Shared Sequencers

  • Shared sequencing is gaining popularity for rollups, where a separate sequencing layer orders transactions before app-specific rollup chains execute them.

  • How do we design economic mechanisms for revenue sharing between rollups that accurately capture their marginal contributions to MEV transparently?

  • Developing fee mechanisms for the sequencer that don't require it to execute transactions or maintain excessive state?

  • Goals include low latency, resisting front-running, avoiding centralization, and independence of unrelated transactions.

  • Look at this talk for more.

• Oracle Systems, which gives access to more varieties of data

  • Comments from SoK: Oracles from the Ground Truth to Market Manipulation

    • Two conditions seem necessary for securing oracle systems: the token's market capitalization stays material, and the token is evenly distributed.

    • Oracle systems with on-chain modules are expensive to run on public blockchains like Ethereum, which prices out certain use cases that consume a lot of Oracle data but do not generate a proportional amount of revenue (e.g., Weather data).

• The tokenomics/governance problem

  • A boilerplate for tokenomics doesn’t exist.

  • Are there better models possible? Buyback and burn are done to skirt regulations.

  • Will we classes of shares in tokens like in tradfi? Class A shares, priority shares, etc., for better governance? Could this be the decaying power of priority over time? so the team has control to set forth a vision and then decentralize it?

• The governance framework problem

  • There have been a myriad of token takeovers of DAOs with no recourse and loss of funds every few months, and the most recent one is Tornado cash

  • Is there a good distribution or holding model or a delegation model that avoids voter apathy (most votes are decided by whale votes today)

  • Dual Governance models are being explored - OP labs and Lido.

• Governance beyond coin-voting

• • Is the Governance framework problem stuck as a plutocracy?

  • What other models reward or incentivize governance participation from those who care about the protocol's future?

• Better Fiat on-ramps/off-ramps: Are centralized exchanges the single point of fiat on-ramps to blockchains?

• Crypto x AI

  • Is Decentralized Computing the only idea?

  • POC for model control via decentralized protocols

    • Helpful for AI safety

  • Trustless AI inference

  • Decentralized Data networks for High-quality data with provenance

  • AI model marketplace

• Reputation Systems

  • A good decentralized reputation system can replace the need for identity.

This list was inspired by Riva Tez’s following tweet :D

I’m not quite sure where the market is going, it looks pretty fatigued. When I came back to trading/investing in crypto in summer 2020, I was under the assumption that this cycle would probably end around us topping at 5 trillion dollars. This article is perhaps just a recollection and retrospection with some learnings of what I saw since then with some Dota analogies (bear with me through those).

Note: Someone told me that this work reminds them of Cobie ser’s, Trading the Metagame, So you can probably skip this if you have read that, This is probably just an extensive exploration of that idea dating back to defi summer 2020.

I’m a DOTA 2 player (Defense of the Ancients), and it’s viciously addictive having spent 3000 hours playing the game and even then still being engaged by it, I often wonder what makes me go back to the game? Most modern RPGs are played around for 100 hours to complete the main storyline while 200-300 hours for covering everything. DOTA 2 is an MMORPG (pronounced morph) and something that keeps getting changed due to patches, the heroes are balanced/the map is changed/the items are updated, and that keeps it very interesting.

There is this dialogue in the movie, The Prestige by Alfred Borden,

And while that is true about magic tricks, that is true about games as well, “figured out” games simply aren’t interesting, and every patch in dota brings with it new things to figure and new interactions to explore and new things to learn. There’s probably something about stochastic games, partially observable games which keep humans engaged (since we are pattern recognition machines constantly trying to figure stuff out).

So what happens when a patch is figured out, what does it look like in a game, Some heroes are found to be more broken than others they are simply better given of the in-game economy and the items available (in their current form in this current state of the patch). And this is when a meta develops.

The meta is almost always created by the players. They try several builds and items and some strategies eventually tend to work much better than the other ones. Even with relatively “balanced” patches, some things become more popular than others, even though there might be other broken things (due to influential people, pro players in the context of a game). Developers then nerf certain things that are too broken and another cycle of finding the next meta commences, also done so that the game doesn’t become too stale. That probably is enough of a digression from the point of this article. But the existence of a big action space, by that I mean, there are too many things to try out always means that even though there might be some things popular in a meta, given that the popular stuff gets nerfed, there might still be things in the system that can be exploited or found to have an edge over others.

But what does the meta of a video game have to do with crypto? Abstract models translate to other places in the wild too. Crypto being the hyper-financialized paradigm of tokens and projects that it is, is very much like the partially observable, stochastic games that are played in dota, league of legends or other MMORPGs. The meta is the tokens that are currently going wild, the projects that have the maximum attention devoted to them because financial regimes seem to be driven by intrinsic attention economies. The thing about attention economies is nothing can stay in its place forever and the meta dissolves (in this case this certain class of projects die) and then either it’s on to another class of projects or everything is just silent for a while. Why is knowing any of this important to you? Knowing about the existence of the metagame perhaps helps you discover them or recognize the one you are in or discovering some common trends in the different metas could help you know if the shitcoin that you currently have has something unique in it to drive its own meta. After all, you play to win.

While when the run in summer 2020 began, crypto was but a 250 billion dollars worth asset class and I was initially even sceptical of it ever reaching the trillion-dollar mark (because that for me was the hallmark of a mature asset class) the highest it ever went in the 2017 run was 850B before collapsing to 150 billion at its lowest. It has 10xed in 2 years achieving an all-time high of around 2.5 trillion dollars and currently sits at 1.7 trillion. But I think regardless of that what is quite true (and might stay true unless we go sub trillion as a market), An asset class worth a few trillion dollars is liquid enough to fund its own microcycles and anyone who understands this is positioning themselves for some gains (if not a lot).

Token Metas

I will first cycle through token projects, and then later talk a little bit about NFT cycles (there haven’t been many). Also, some of the chronology might be misplaced or just simply wrong but I’ve tried to recapture the major ones that I recall.

Nothing taught me about information asymmetry, which could be something as simple as knowing to use a product or putting different things, as much as defi summer 2020. The early projects were yam, yearn, harvest and other projects which at this point were called defi even though all they had was TVL based token emission slowly getting diluted through pool 2s and had insane APYs which hovered from 6 digits to 10 digits. Anything less than 5 wasn’t even considered. Most people were still trying to figure out the process of depositing tokens → getting LP → depositing LP share → claim rewards, which wasn’t clear early on or were too scared because the APY looked too good to be true. I think a dominant signature of different metas in crypto and I will mention this latter too, is that you can identify the ending stages i.e the meta attaining critical mass by the number of rampant forks that it spawns. Kimchi, Based Finance and other names I can’t even recall were forks attaining 9 figs worth TVL less than a day into the launch and losing it just as quick.

Simultaneously around the end of the yam cycle which ended with a smart contract bug (which would’ve or did, I don’t recall, led/lead to loss of funds), developed the rebase token project meta. You could recognize this by the number of people talking about how the “elastic supply” of tokens led to better tokenomics. Everyone suddenly knew what elastic supply meant and what number $AMPL or some random rebase token would have to be at to rebase positively or negatively. Something that is very true, particularly about financial games is that (only?) being early helps and that the later you are to the game, the more likely you are getting dumped on, so if you recognize that you are late to this meta you are probably better off avoiding it, because you probably don’t know what stage you are in and could probably end up losing a lot if you are late.

In the shitcoin projects that I saw something that became popular for a while after this was deflationary tokens which rebased, every transfer had a penalty but holding the token availed you rebases and unlike the earlier rebase tokens these were strictly positive rebases. This did not last for very long since it led to quick inflation of the supply and the bigger participants profited very quickly and moved to book their profits.

Soon after the rebase coins, late 2020 saw the rise of algorithmic stable coins. This was the introduction of game theory (somewhat so) to tokenomic design and has been tried in a variety of ways since, but early on was about how long the stability of the ecosystem could be kept propped up through bond sales and the emission of the stable coins to bring the value of the coin closer to 1$. Empty Set Dollar (ESD), Digital Set Dollar(DSD) are just a few names that come to my mind when thinking of these, both of them trade at a cent or so today, so while it was an interesting experiment per se, it’s clear that they didn’t survive.

Q1 2021 was a period during which almost every token was going up only, and you didn’t have to exactly be right about the meta to make money in this period. Tokens were up 10-20xes in 30-45 days and the saying “you don’t have to be right in a bull market” comes to my mind quickly. Around early q2, dog coins and other meme-able name coins (like cummies, safe moon etc) started getting more popular and while you could call this a meta or an overheating market (since you would see no value in these tokens), its true that this part was one of the most degen parts of the cycle.

While late Q2- early Q3 2022, were somewhat muted something that came after this was the L1 cycle. Where all L1s were suddenly pumping, you could say that this was a confluence of several things and while it would be untrue to say that these alternative L1s didn’t work before then, it’s coincidental that everything came together for this to happen. A popular tweet by the fox would probably sum up Q3 2022,

Something curious about L1 cycles was that they were first token pumps and then ecosystem pumps, where the value of the token went up and then the projects on-chain went up (somewhat consecutively instead of in sync), and these in-ecosystem pumps had their own micro cycles which lasted from days to weeks. This also went onto other L1s not just the ones listed above, but the fact that those projects now inhabit the top 20 in the space speaks about their success more than others.

Late Q3, saw the rise of Olympusdao. A POL service that was launched in March 2021, which took some time to actually get stable, was countercyclical for the entire dip in Q2-Q3 because of the coin being solely backed by USD reserves. The thing that really made Olympus popular was the narrative that surrounded it and (3,3) which will probably be remembered as one of the hallmark memes/narratives to come out of this cycle. Something that quickly became obvious alongside the rise of Ohm was that in a multi-chain world, the meta would also become multi-chain and forks wouldn’t be restricted to the same chain. The rise of Time, Rome, Snowbank, Spartacus, Invictus are proof of this and something to keep in mind for the future, something that was fascinating to me about multi-chain forks was the fact that it did not fractionalize liquidity from the main project. Will seamless multichain interop change this? Only time will tell.

For a moment or two in late 2021, early 2022, projects with ve-conomics especially those that involved themselves in the curve wars started pumping. It did not last long enough for me to credit it and call it a “meta”. But something that has definitely been true for me over the last two years has been the following

If crypto is a game, tokenomics is the meta - @cobie

Most crypto micro-cycles have been driven by the tokenomics of the project, which get exploited for maximal gains and quickly fall down as well. Do projects in these metas contribute something to the long term health of the ecosystem? As I write this article, ohm forks (which were the last meta) are down 90-95% since their market top but it’s undeniable that they pioneered the liquidity as a service idea and continue to through Olympus pro. Also, the game-theoretic algorithmic stablecoins were an important experiment which probably couldn’t have been done were it not for the existence of crypto, and finally, something true about all these microcycles is that narratives and communities in projects matter.

Most innovation in defi seems to revolve around making the tokenomics more appealing to the holders, and while that may be an interesting problem to solve. It won’t increase the surface area of participation that we all aim or hope to see.

NFT Metas

If NFTs are a game, style is the meta.

NFTs are relatively a much more nascent market compared to token projects but may at least provide some solace to the holder because they can perhaps say that they were in it for the art (if the art is good). I will keep this part short.

While crypto punks were extremely popular they never quite garnered heavy attention until late 2021. One of the first NFT micro-cycles was developed elsewhere in the project called Hashmasks. Hashmasks started the bonding curve meta, not quite bonding curve as much as selling blocks of the supply in increasing order until the supply was over (which was tried for at least 4 months by a variety of projects). Something that was quickly evident with NFT projects was that community engagement matters. Hashmasks were the heartthrob of crypto Twitter, while everyone figured out hidden meanings in the artwork, a variety of servers spawned which catered to different trends within the artwork. Hashmasks also was one of the first projects to have more volume in sales than crypto punks in a given day. Hashmasks today does less than double digits volume in a day (if any at all).

NFTs were quick to die in the event of the May 2021 meltdown.

Since then, in late q2, NFTs saw the PFP meta which was led by cryptopunks and then extended to byac, doodles, cool cats and various other PFP projects.

Something that was very popular and in retrospection definitely just a meta within the NFT space was the generative art NFT series. Q3 2021 saw the rise of a variety of sub art projects from Artblocks. Fidenzas, Singularities, Subscapes, Archetypes were just a few names that I can recall from the 15 or projects that were existent then which commanded high valuation up to as much as a few thousand eth for a Fidenza sale. Generative art was hailed because of a variety of things, and you can only question the veracity of those claims now that they seem to have had a similar fate to that of hashmasks. Why did generative art fail? I don’t think it was as much as generative art that failed, as much as it was the reason of the interest in these projects. The projects are not any less aesthetic today than they were at the top. A primary reason of interest for Artblocks was the collections had about 1024 supply and artificial scarcity (low supply is good) remains one of the best ways of pumping the value of an NFT supply. Artblocks kept releasing about 3 new art series every week since q3 2021 and you could say that ironically supply fungibility was why the gen-art meta came to an end.

Something that started crowning its head at the end of the gen-art cycle were the Loot projects which in its most idealistic sense could be thought of as seeds for games in the future/ or art in the case of the n-project. Loot was hailed for a variety of reasons and perhaps development for the game still happens today ( i am unaware, although i did see a project launch visualization for loot recently). Loot also saw the rise of the NFT-token meta where every loot holder was airdropped tokens (AGLD) which would be used in the game economy. This idea has since then been adopted to a variety of projects, and every NFT project plans to launch a token for its supply or is at least actively considering it. Something to wonder about when it comes to this is if it’s a good idea to 1] fractionalize liquidity from the NFT volume to fund a liquid token? 2] Are liquid tokens necessary for something NFTs? And while these questions might have been ill-formed I hope you get the idea.

As of the time of this writing, we recently just saw the anime-pfp microcycle, which didn’t last as long as I expected it to. but NFTs have definitely seemed to have had their own economy insulated from that of the wider crypto market perhaps due to their mainstream appeal.

I might have missed mentioning a variety of projects or cycles in my article but I hope i have mentioned enough for you to understand the broad strokes of this cyclical market that we thrive in. One thing that I definitely missed from the top of my head is the trading card NFT cycles and classifying projects like Aurory, magic and others to the metas that I have mentioned.

A small crossover that was seen and has since then developed and will probably keep getting more common until it blurs all the lines is the Play to earn ecosystem. The market resurrection in q2 2021, was led by Axie Infinity, a play to earn NFT game where you had to buy the Axies to play the game and were rewarded. A lot of projects have since then tried to pioneer Play to earn and have been successful to various measures, but to date, no game seems to have had the same success that you would find from a game from a traditional gaming studio. There was also a micro P2E cycle which was sparked by $jewel, a token for the economy of the P2E game, DefiKingdoms. Something that was fascinating about DFK was the fact that it had bought all aspects of crypto into the game, was an intersection of various concepts and was perhaps one of the only projects which led the underlying L1 to be used more than it previously was.

I wrote this article to keep reminding myself of the following

That recognizing that the market funds micro-cycles allows you to keep playing the game even when the wider market might be consolidating but also recognizing meanwhile that fatigued markets do take breaks and these microcycles only get rarer in these markets (this is when you start planning to take a break).

It’s frustratingly simple to convince yourself that you could have seen all the signs of the meta developing had you done something differently because it is in retrospection. You can keep reminding yourself of the signs that you saw in the previous microcycles and keep applying them to find new ones, but something that has to be kept in mind is that Metas don’t always develop quite the same ways, some achieve critical mass very quickly (like algorithmic stable coins) while others have to survive and the narratives around it have to develop for them to achieve it. Knowing what will happen with the shitcoin you are invested in is almost next to impossible, and perhaps that is why I end with a note to myself, “don’t get married to your bags.”

Something that is no longer true as it once was is the ability to keep in touch with everything in the market today. I could name almost 90% of the hot projects in early defi summer, actually, almost all of them save a few. Today I barely know a few real projects forget hot ones. It’s hard to keep track of a space that has about 13k projects (consider even the first 1500 for brevity). But as Jeff Bezos once said, “Your margin is my opportunity”, something that is true for someone new entering the space is that “your lack of attention in my opportunity” (quite doesn’t roll off the tongue as well). The game gets harder when there are more participants because newer cycles might not even get the time to fully develop before they are dominated or found out by the market. Information asymmetry keeps growing and the meta gets hidden from public spaces eventually becoming non-existent to the public.

Its been an interesting mental exercise to consolidate these ideas and I hope they’ve been of some interest to you as well.

While there is no doubt that yield stacking is impressive and that we can create extensive levels of leverage by stacking things on top of one another. The stick of measure will be solving problems that either extend the ethos of decentralization to every real-world product or rectify the errors that current systems possess or fill the gaps to make the system seamlessly functional, all of this leads to more adoption or reduces the friction/inefficiencies in the ecosystem.

Following is an incomprehensive list of defi problems, I will take stock of these problems in 12 months.

• Automated risk scoring of lending borrowing pools -> Increasingly important problem

  • Possible Solution: Risk assessment without historic data is really hard because it could be so that a pool of users with a good credit history will always pay the loan back. “Credit score” in tradfi.

    • One alternative way of looking at the problem would be, looking at a function for calculating the probability of default given the pool of assets you have.

    • Thinking of risk in the extreme terms of everyone defaulting although useful is an edge case, every time someone defaults (since these are collateralized pools) there'll be a liquidation. So this means that there is an existence of a liquidation price, so one way to view this problem (even without considering the users at all) given that I borrow X amount at Y price, What is the chance that price hits my liquidation price Z (in this case the user will have defaulted and will get liquidated).

    • How do you calculate/model the price action?

      • One naive and simple metric would be a weighted average(weight by asset in a pool of n assets) of historical/implied volatility (because in a highly volatile asset it’s likely that the price reaches your liquidation price) and you can do it on a basis of a monthly epoch (now this is a very sanitized way of thinking about pool risk because an asset can improve its order book/action price becoming less volatile)

      • The added advantage of doing a weighted average across a function (IV/historical volatility) is you can just add another variable modelled through ML or some other way and get a more refined answer

    • The assumption of modelling the problem this way is that a borrower might borrow from a pool with less risky/volatile assets.

• Managing Risk for lenders and distributing risk/ Undercollateralized Loans

  • Defi Lending is not in a place where it can be used by traditional or real word entities because of over-collateralization

  • Most real-world loans are undercollateralized

  • This under collateralization ensures that the lender establishes the borrower’s ability of repayment

  • Tradfi is plagued by NPAs but still ultimately fall back to some sort of credit score establishment [Spectral finance solving this, but still an open problem].

  • But still, most credit score methods would rely on onchain history for credit establishment, we are moving towards privacy-centric defi is this approach extendable to that idea? [Homomorphic encryption could provide a solution]

• Uniswap v3 is one of the most used AMMs, with over a few billion dollars in it, What is the optimal strategy for lending for both passive and active liquidity providers? How can you formulate and model the problem?

  • Most vault managers have failed and only charm finance appears to have some stronghold over this having consistently beaten full range v2 performance

• The tokenomics problem [Millenium problem of defi]

  • A survey of different tokenomics designs and what they have accomplished for their respective projects

  • is veToken the optimal token design for most protocols?

  • What are some good boilerplate tokenomics that a project could look into?

  • Solving the Liquidity dilemma for most protocols, is POL the final answer for this?

• What does a privacy-preserving CFMM look like, and what privacy guarantees can it make? From the paper A note on privacy-preserving constant function market makers

  • Answered to an extent in Differential Privacy in Constant Function Market makers

• Private Lending

  • Lending practices today involve an almost on-chain registry, how do you make this more private

    • Imagine knowing what every user borrowed and paid in the real world

• Derivatives

  • Today’s on-chain options are not a success yet [The metric here would be compared to volume compared to centralized counterparts]

  • Primitive is making some headway with this

• Compiler → I give you a payoff and you “compile” it by stringing together other primitives

  • One way to think of a compiler is, you are interested in a certain payoff from the assets you have so you have a compiler that auto-selects and tries to model the desired payoff from the instruments/primitives available on-chain, “auto yield stacker”

• Gas derivatives

• Cross Margining Systems, from The Defi Prime Broker.

• Soulbound Governance & Proof of excellence, Inspired by Vitalik’s Blog Soulbound, Hard Problems in Cryptocurrency: Five years later

  • Governance today especially in defi is misaligned with the ideas that push forward long term development and choices, Is there a way to recognize and give non-transferrable say in matters concerning the protocol.

  • Recognizing a way of who should be soul bounded also solves some airdrop dilemmas which haven’t had much progress since 2019

• Talking about Economic problems from the above blog post, we’ve come a long way in some cases since 2019. Stable value assets have seen a lot of exploration and development, looking forward. Stablecoin health dilemma [Centralized ↔ Usage ↔ Mechanism]

  • Is it dependent on incentivization for maintaining peg, how healthy is this?

• Decentralized Public Goods Incentivization

  • While there are experiments with retroactive public goods incentivization and GitCoin also helps in this area, there is a lot of exploration in this space left

• Reputation Systems

  • Why is this a defi problem? A good decentralized reputation system can do away with a lot of requirements that would need a private defi ecosystem to thrive

• Oracle Systems which give access to more varieties of data

  • Comments from SoK: Oracles from the Ground Truth to Market Manipulation

    • Two conditions seem necessary for securing oracle systems: the market capitalization of the token stays material and the token is evenly distributed.

    • Oracle systems with on-chain modules are expensive to run on public blockchains like Ethereum, which prices out certain use-cases that consume a lot of oracle data but do not generate a proportional amount of revenue (e.g., Weather data).

  • There have been multiple hacks/losses in the Defi space due to oracle manipulation which suggests that we aren’t there yet.

• Better Fiat on-ramps/off-ramps, are Centralized exchanges the single point of fiat on ramps to blockchains? [Source]

Updated last on the 29th of Jan 2021.

Inspired by Riva Tez’s following tweet.

This article is a “the cup is half-full” look at Olympus, instead of the other side of the coin that seems to be popular. I only think of ohm as far as the decentralized reserve currency experiment is concerned any pivot from this idea moots the arguments that I’ve made in this essay.

There seems to be little love lost between most of CT and the downfall of Olympus. But if there’s one characterization that I don’t entirely agree with that has dominated the common parlance of Olympus talk is calling it a Ponzi, and I think it’s quite sad that the project itself tried to popularize that narrative on some level, you reap the seeds you sow.

What is a Ponzi, though? A quick search on google yields the following.

A Ponzi scheme is an investment fraud that pays existing investors with funds collected from new investors. Ponzi schemes are named after Charles Ponzi. ... Ponzi used funds from new investors to pay fake “returns” to earlier investors. Ponzi scheme organizers often promise high returns with little or no risk.

By comparison, at least I’m unaware of Olympus ever promising high returns at little or no risk. For as long as I was active in the server till September (before its monumental run perhaps), the conversation in this case usually ran around what would happen in the case of a bank run and how most investors wouldn’t have lost a lot when considering the RFV of the token. Perhaps it was the APY marketing campaign that came closest to ohm ever being an absolute Ponzi.

But what went wrong? I think the Olympus October run and the November top almost echoed the levels of craze that were seen in the GameStop run which were seen in stock markets earlier this year. But I think the comparison mostly stops there. Speculative price runs are a breed of virus that eventually bring down any asset that they take hold of Doge, Shiba, Floki, time, hex and while you might think that I’m just adding “Ponzis” in this list, I’m sure you are aware of the bags that you have owned over the past year. It’s all reflexivity.

Ohm was actually countercyclical in the first half of its existence until September. You could chalk this up to a variety of reasons, but it could have been majorly attributed to the fact that ohm was actually a stables hedge, with APY embedded, since the treasury was all stables, and after they started diversifying the treasury into non-stables (eth) it took a more cyclical turn even though it was a fraction of the treasury. I think the thing that led most to ohms insane run and its subsequent downfall was its (9,9) feature. Anyone who has ever done any fundamental analysis in stocks or has looked at a financial balance sheet would tell you that Olympus was almost trading at 20-30 times its book value and *expanding supply* at this rate, which means quick dilution. I think you cannot allow lending-borrowing practices on the underlying expanding token (sOHM) at a 100% collateralization rate, something that would’ve saved ohm, perhaps also would've tapered its upward run, would’ve been a collateralization rate which was a function of its RFV. This would’ve removed the excessive amount of leverage that dictated and still dictates every downturn the token has seen. Another reason for the downfall was the immense concentration of the distribution in whales that were early to as much as one whale holding 85k ohm (about 120 million dollars at the top, which was sold for about 30 million dollars recently).

But…but… only early people really made money on ohm, it was only people who participated in the IDO? Even by the time I had left, only about 12 presale participants were still left. At this point I’m mostly convinced that the only way to really make money on assets, unless you are a trader, is to be early to it and while people in this space do make insane amounts of wealth through airdrops sometimes that is also perhaps precisely why we call crypto a generational opportunity or find some asset which has been in the scene for a long time and is undervalued and you wait for a people to see its value and even then in strictly monetary terms you are “early to it”.

But now that the token trades near its RFV and almost all leverage has been removed out of its system and the ohm holding concentration looks much more distributed than ever before, I personally am one of the biggest proponents and would like to see ohm succeed, perhaps not so as much for profit (I don’t own any bags) but because it would be the first successful “currency” experiment that was successful in defi.

Why do we need a decentralized reserve currency at all? Or what would it mean to achieve that target? From here on, I talk about the possibilities and you as the reader might have to allow me to wonder about a decentralized financial ecosystem.

A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution - Satoshi Nakomoto, Bitcoin Whitepaper

That is the first line of the bitcoin whitepaper abstract. Bitcoin in its original form and its earlier narratives wanted to replace “money” and be used for our daily transactions and other uses that you commonly use money for. Most of its opponents always have drawn attention to bitcoins volatility to highlight its inability to be a currency in any form (something even ohm opponents draw attention to). But something that is an undeniable usage of bitcoin however small the volume is that bitcoin is definitely used for cross border payments because of its convenience, censorship resistance and usability. What would bitcoin have to accomplish to become an actual currency? Bitcoin would have to be held by multiple treasuries across nation-states across the world and allow it to become a legal tender perhaps if it really kicked off you would even at some point see bitcoin-backed currency (this is really outlandish and I don’t think so either). But the other reason bitcoin’s “currency” dreams never took flight was because of its network being bottlenecked on the scaling level and while you might talk about bitcoin L2s, none of them has seen the sheer level of adoption that merits it any mention here and bitcoin’s network security is under question given the trajectory that it is on so its dreams of becoming a currency are currently under question, The narrative that has caught on for bitcoin is the “store of value” narrative.

How is any of this relevant to ohm? It’s the idea of the need for a currency. The entire crypto ecosystem now hinges upon stablecoins and while stablecoins are good they are still a distorted representation of the currency-pegs we use, what is the current currency of the crypto ecosystem assuming we were living in a crypto world with no fiat anchor? Bitcoin. Because Bitcoin is used in a variety of places and every major exchange and CEX usually adds a bitcoin pair for the token immediately after the fiat pair. The gap that ohm is trying to fill is that of having a peg that is neither bitcoin nor a stablecoin, and in the ideal sense of the crypto ecosystem, so that it becomes the base token for a variety of assets to be measured in. While I’m aware that the risk-free value of $ohm is measured in x dollars, it’s exactly the idea of moving away ohm being backed by 1$ like every other algostable has tried to each ohm being backed by x RFV amount that the idea of being a reserve currency is about.

When did we all forsake ourselves? That we aren’t enthusiastic about the prospects of something like this being a possibility is something I often wonder about. Something that’s always been fascinating to me about decentralized finance was the sheer genius of the entire ecosystem. Within the years that it has grown and since the summer of defi when it went mainstream, we have now almost rediscovered the entire financial ecosystem almost replicating everything that is available and beyond while keeping our building blocks composable and in some cases even going beyond and discovering new primitives, which we might see adopted in mainstream finance. When you consider this, a decentralized reserve currency that replicates the gold/silver standard that we previously used to have is but one experiment that we should be excited about.

Have you ever wondered that you actually (3,3) with the currency that you use every day? now you would ask me what is the RFV of your token i.e your currency and while until the early 20th century we did have an RFV namely the gold standard today we don’t, our (3,3) lies entirely in trust and backing by the governments of the respective nations we live in, our currencies are backed by guns.

Ohm was in its expansion phase and I still think that it is in its expansion phase but something that is undeniable about the success of the project is the number of hands it has put itself in. Putting yourself in the hands of 80k holders (this number is probably only counting wallets, assume 40k unique holders for convenience) in <1 year of the launch of the project is quite the view to look at.

The (3,3) phase was more about the expansion of supply to me rather than participants engaging together, but that was still an important aspect of the system so as to not let the price of the token go below the RFV and this has been observed in a variety of ohm forks. But why was the expansion phase important? There are a few ways you bootstrap a treasury, you either do a sale of tokens or you do an expansion → slow growth of the treasury. And while the earlier might sound convincing, I recall a stablecoin project which had raised almost a billion dollars in the summer of 2021 and failed to maintain a dollar peg for the first few months and while it has since turned a fresh new page, it just proves that raising your treasury through a sale isn’t a panacea/universal solution. What excited me about the sheer growth of the ohm treasury even though you might call bonds analogous to TWAP sale of tokens, is the number of people that have bonded over time consistently with the system.

Something that has undeniably seen an uptick due to Olympus, is inter protocol diplomacy due to its Olympus-pro offering. And while a diverse treasury due to such token accumulation has its issues, something you can’t help but wonder in the case of taking that idea to its extreme is, let’s say crypto does grow 5x from here and a lot of protocols have been using OlympusPro to bootstrap their liquidity and OlympusDAO due to this holds their token in non-trivial amounts. There is a vested interest across all the protocols to help ohm maintain its peg lest the collapse affects their own treasuries or let ohm dilute these tokens for maintaining its RFV, and while this sounds like guerilla warfare, this vested interest in helping a currency maintain its status is how the US dollar reigns supreme as well. This is somewhat different than the curve-wars bribe meta that is popular for maintaining the pegs of a variety of stablecoins.

This article is not meant to be financial advice nor is it a call for you to load up on your bags because I don’t think the success of ohm would quite be measured in the multi-billion mcap that it has achieved or will have achieved. The final metrics for Olympusdao is its usage and the day even one user starts talking about a pair in “token-ohm” parity instead of “token-fiat/btc” parity I think ohm has achieved something worthy, and something that will be a case study in several universities across the world where economics is studied.

Thank you for reading this article, any feedback is appreciated.

Decentralized oracles are smart contracts that rely on users voting on particular prediction market outcomes. A final outcome is chosen via a social choice function, similar to how majority or weighted majority voting is used to decide outcomes in elections.

Decentralized oracles are becoming increasingly important and sought after sources of information because no one agent/entity/organization, i.e. a centralized source of information, can be trusted for its information completely. While this is not something wholly solved by the de-oracles, their design usually involves a “challenge” phase or/and provide users with rewards for reporting info correctly.

A fundamental problem when it comes to price markets is the “oracle problem”, the ability to have correct prices for an asset, or more generally, in the case of blockchains, the problem of providing external data (i.e. outside of the system) to a blockchain. One concern whenever using an AMM is that the price of an asset should accurately reflect that on a centralized exchange (assuming this to be the global price). AMMs are automated market makers and have no notion of this global price and aren’t adjusted accordingly; this price adjustment is usually left to arbitrage bots or traders who use these products. Why is the validity of this price being close to the global price important, though? Suppose an AMM generally has a negligible error or constantly reflects the same price as the global external prices. In that case, such an AMM’s price feed can be used as ground truth for various purposes and is said to be a “good price oracle”.

In this post, I will go over how CFMMs (a class of AMMs) can be used as price oracles and what interesting properties they have, while covering the following paper.

· Angeris, Chitra et al. (2020): Improved Price Oracles: Constant Function Market Makers

The paper is a generalization of the authors work, An analysis of Uniswap markets. In this paper the ideas are generalized to CFMMs with the usage of convex analysis.

You can find a smaller summary of this paper, in this thread.

Been on a mission to read more research in the AMM space. My favourite project, Primitive, is launching soon and is based on Replicating market makers[RMM]. There was a lot of precursing research to RMMs, and I’ve been lately making my way through the research. I can’t say that I have grasped every nitty-gritty of the paper or will be able to do justice to every aspect of the paper, but I’ll do my best to provide nuance or access to more resources. [Note: Convex optimization is hard]

Automated market makers were made famous by Robin Hanson’s” Logarithmic Market Scoring Rules for Modular Combinatorial Information Aggregation”, basically the LMSR market makers. These are functions that are extremely popular in prediction markets. (Refer to Gnosis’ blog or this one for understanding how LMSR’s actually work).

AMMs have gained immense popularity in the defi space as a primitive and have become essential building blocks. While the LMSR and its counterparts provided one kind of automated market makers, the cryptocurrency community has developed a different class called constant function market makers (CFMMs). Uniswap’s V2s constant product AMM quickly comes to mind.

Since the CFMM paper was written in march 2020, DEX volume has increased from roughly 10 million per day to a few 100 million, reaching even a 2 billion dollars of trade volume every day [As of 15th Jan 2022]. This has led to Uniswap (the most popular dex) being used as a price source.

A natural question to ask here is, Is Uniswap a good price oracle for every type of asset pair possible? i.e. is Uniswap’s AMM design optimal for all assets? The answer for this is no. Stablecoins arent usually best handled by Uniswap and have been addressed by better mechanisms through curve. Although recently since the introduction of Uniswap v3, it has come to light that may be in the current state of the AMM landscape no one AMM is superior to the other when it comes to stables and that we might be hitting the upper limits of asset-based AMM design, as seen follows. In the absence of superiority of product the usage falls back to fees, and Uniswap v3 has been seeing steady growth of its stable coin market share since it introduced the 1bp fee tier.

The paper’s contributions are as follows:

• A complete and general framework for analyzing CFMMs

• Provided sufficient conditions

  • Agents are incentivized to have the CFMM report the correct prices of an asset (the implication of this being that AMMs can act as sound decentralized oracles given the conditions are satisfied)

  • A malicious agent has no way of draining the assets of a CFMM by only trading with the given CFMM.

• A simple derivation for the total asset value in a CFMM as a function of external market prices

While a mathematical treatment of the entire paper here would do justice to it, I will not go into the depths but instead talk about the definitions in short and what they help accomplish/prove in the paper, so we can also take away the main results. I think the math in the paper is beautifully written and straightforward.

The definitions of the paper help you make a mental framework around market makers and a neat problem formulation to work with if you are trying for optimal trades or are interested in the CFMM design space.

Constant Function Market Makers

Trading Function

Now, these definitions break down an AMMs functionality into two parts. The CFMM definition itself defines the domain to the range R and secondly, the trading function, which is a representation of a simple trade. Today we only deal with both the input and output trades as vectors of size 2, wherein you have the exchange of (token 1, token 2) → (token 1, token 2). We also deal with larger vectors when it comes to exchanges that have routers wherein you don’t have a direct pool from token 1 → token 2 and choose to hop between n pools to get the trade done, in this case, the size of the vector of both the input trade and output trade will be larger. In the future, we might even have multi-coin tuples where you trade a set of tokens at once with some other set of tokens which saves you the number of distinct transactions that you might have to do.

The trading set is further defined to have the ability to compare two trading functions, and it offers a way to work with the trading functions elegantly. An interesting consequence of this definition is that constructing equivalent CFMMs with trading functions with properties of interest becomes easier.

Convexity, Path Deficiency and Path Independence

It is assumed that trading sets are closed convex sets. What does this allow? The field of convex optimization has been thoroughly developed over the past century. A convex definition allows agents to find an appropriate solution that maximizes their payoff to an optimization problem over the trading set. There is no CFMM used in practice whose trading set is non-convex (which is important for generalizing this result).

The Reachable reserve set is the set of reserves that can be “reached” from the current CFMM reserves by performing a trade. “Reached” in this context means the value of the underlying reserves.

Practical CFMMs don’t satisfy path independence, but what does path independence mean? While I earlier thought path independence (from what I understood) was if you arrived on the same values of for the reserves of token 1 and token 2 no matter what path i.e route of assets you take. If you think of the variety of assets that are present in a CFMM, then going from any one asset to some asset in the system will have a variety of paths i.e token 1 → token 2 directly or breaking this down into doing multiple small transactions from token 1 → token 2. Now this is something that is not practically possible because of fees, multiple transactions accumulate the fees so you don’t end up with same amount of token 2 as you would if you would directly do it.

A slightly more general property to talk about when thinking about path independence though is path deficiency, path deficiency instead of talking about the path independence instead makes guarantees about the underlying reserves and this is a property satisfied by all known CFMMs. No trade in a CFMM can make the reserves larger than the current one, i.e they cant make assets out of nowhere.

Optimal Arbitrage and Marginal Price

The optimal arbitrage trade is given as follows,

Here Λ, is the output tuple, specifying how much of each coin in the ith entry of the tuple after the trade, similarly Δ is the input tuple, specifying how much of each in the ith entry of the set before the trade. An optimal arbitrage would try to maximize the difference, as seen in (8). Thinking of an example here you could consider (token1, token2) as your tuple and in an arbitrage, you would want to start with (x1, x2) and end up with (y1, y2) where the sum of these differences i.e (asset price of token 1 ) * y1 - x1 + (asset price of token 2) * y2 - y1 is highest.

While generally, it could so be possible that solving (8)/(9) does not give the best possible strategy due to market inefficiencies or otherwise or an arbitrageur could break this trade into smaller trades and these could lead to higher benefit. Something that the paper proves is that, for path deficient CFMMs there is no strategy for which an arbitrageur can get a higher payoff than solving (9) and that solving (9) gives the highest payoff possible.

There are some optimality conditions that need to hold for (9) to be true, which lead to a simple definition for reporting the price (would suggest people interested in this condition to check the paper pg 15). The definition of this asset implies that whenever the price of the CFMM is mismatched to that of a reference market, an agent is always incentivized to make “free money” by only trading between these two markets which would also bring the two prices to parity.

While the rest of the paper is beyond the scope of this discussion, there are some results worth pointing out:

• The next section elicits the problem to be solved for find the total value of the reserves in a CFMM, ultimately posing a single variable convex optimization problem as follows

• Which when solved for the case of uniswap with zeros fees and a special case of two assets leads to the solution

I wonder how this applies to the Uniswap v3 model which isn’t exactly a CFMM due to its formulation of concentrated liquidity (the ticks fractionalize a continuous curve into discrete parts), but each tick can be seen as a CFMM. I’m excited to write about Replicating Market Makers next, these are CFMMs whose portfolio value function matches the payoff of a desired product, in simple words owning liquity in these would be similar to the payoff you would get if you bought the desired product. Primitive’s RMMs are designed to match the payoff of a covered call option. I’ll also be looking into some papers which formalized MEV recently and some others which talk about the importance of liveness in blockchains.

Got a long way to go, Thank you for following along.