# 🌊Scaling Liquidity

## Preview

Our data-driven approach has uncovered a critical truth: the core challenge for solvers isn't fancy algorithms; it's *liquidity*. Since this is primarily an on-chain space and data is readily available, we can obtain figures that could confirm or refute our assumptions. We paid particular attention to Solvers, examining their functionality, risks, performance, scalability, and more.

## Base Assumption

A key task was pinpointing possible roadblocks to clear a path for better outcomes. To achieve this, we initially needed a deep understanding of the problems the solvers were dealing with. For instance, let's consider a solver working on optimizing a token swap on a decentralized exchange like Uniswap. The solver must navigate various challenges, such as finding the best liquidity pools, minimizing slippage, and optimizing gas costs. While the solver's experience can help address some of these issues, it often only covers a small portion of the problem. Sticking to the point, solvers encounter diverse challenges beyond just token swaps. These include optimizing cross-chain transactions, managing complex DeFi positions, or handling NFT trades. Each scenario presents its own set of hurdles that solvers must overcome.

## Insights

### Definition of Solver Algorithms

In this context, "the algorithm" refers to the collective suite of routing and graphing algorithms utilized to solve complex network problems. These sophisticated computational procedures are designed to efficiently navigate and optimize paths within graphs or networks, facilitating accurate and expedient solutions to various routing challenges.

### The Myths Surrounding the Solver Challenges

Without a doubt, a common misconception exists about the primary, most significant challenge a solver encounters. Or, to put it another way, what does it truly take to succeed as a solver?

### The Algorithm Impact is Minimal

Gauging the solver's performance is straightforward in a CoW swap. The surplus signifies the winning bid, with the remaining bid improvement falling between that one and 0%.

Surplus: "The price improvement on a user's limit price" – CoW Protocol

Although there are exceptional instances where the surplus can skyrocket to 10%, the mean surplus hovers around a modest 0.5%.

Why do we emphasize this fact? Because from the perspective of the user, it's clear-cut that there's no discernible difference between 0.4%, 0.5%, or 0.6% optimization. Conversely, teams invest immense effort and substantial resources into crafting an algorithm that yields similar outcomes.

The algorithm has transformed, now appearing more as a bottleneck than a challenge to delegate for superior results. This realization steered our focus toward reevaluating solver success metrics, emphasizing liquidity as a cornerstone for effective and competitive intent resolution.

### Key to Success: Liquidity

After we've clarified the algorithm's scope, it has become increasingly crucial to define the challenge that needs to be addressed precisely. The straightforward approach was to seek a correlation between the effort or challenge faced by a solver, represented by a numerical parameter, and the solver's success. One parameter stood out as particularly significant: access to liquidity.

Numerous indicators point to this, but let's focus on a standout example: On October 27, a new solver named Wintermute joined UniswapX. Despite an initial four-day period of modest successes (with a daily success rate ranging from 0% to 4.6%), it has emerged as the most dominant solver since that time. Achieving a daily success rate between 39% and 88.6% in the past two months, Wintermute is by far the most successful solver on the UniswapX platform. Another notable aspect of Wintermute's strategy is that it leverages its own liquidity in 100% of cases (link).

In conclusion, **Liquidity** has the most significant impact.

## Conclusions

For any platform that involves multiple participants and aims to attract as many participants as possible (like solvers), enhancing its appeal can be achieved by saving time and effort on common tasks. Given the results, the algorithm is a prime candidate for the title of an effort that can be reduced.

### Scaling what is important

In conclusion, having participants focus solely on liquidity seems more practical. When you compare the task of becoming a solver with providing liquidity, you'll realize that scaling has become significantly easier.

### Changing the Format

Providing a solution by the solvers sets some limitations on the format. It must encourage the solvers to provide the best solution and compensate them. It must also have a way of defining the winning solution in a deterministic, predefined way. As for now, the outcome of this, for many reasons, is that solvers motivated by MEV are against the user's interest.

Removing the algorithmic priority bias opens the format for many other alternatives that hopefully will be more positive, simpler, and more efficient for the user.

A solution provided by a third party does impose certain constraints on the format. It needs to encourage them to get the best solutions and adequately reward them. Moreover, it should define the winning solution in a predictable, predefined manner. An outcome, for example, is that solvers are motivated by MEV, which is not in the user's best interest.

## Action Items: Envisioning the Future

Transitioning away from the MEV as motivation.

Multi-parameter optimization beyond cost, i.e., gas efficiency, slippage tolerance, etc.

To focus on the liquidity scale, not solely on the algorithm scale.

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