Gauntlet x Sushi Incentive Optimization [Renewal]


A proposal to renew Gauntlet’s engagement with Sushi on dynamic incentive optimization to drive efficiency and growth.


Over the past year, Gauntlet worked with the Sushi team to optimize incentive allocation across pools based on trading volume elasticity. We deployed an optimization engine that ingested on-chain and off-chain data, modeled elasticity, and generated recommendations.

Sushi implemented 612 recommendations across multiple pools. As a result of these recommendations along with an overall reduction in incentive spending and market dynamics, Sushi’s ROI increased from 10% ($0.10 earned for every dollar spent) to 133% ($1.33 earned for every dollar spent) and projected annual revenue increased by $18.3M. Click here for further reading.

Gauntlet has begun scoping the next phase of work with the Sushi team tailored to the team’s goals and to serve them more holistically:

Focused emissions are important to improve the profitability of the DAO. We will continue to work to provide an ecosystem that is funding innovation & growth while also profitable to our stakeholders. - pocketsquare

  • [New] Optimizing Sushi’s overall incentive budget in order to maximize profitability while Sushi ships new products
  • [New] Expanding optimization efforts across additional chains - Gauntlet’s work to date has been targeted to the Ethereum mainnet
  • Continuing to optimize incentive allocation across pools as this continues to be Sushi’s largest expense


In the following sections, we will outline the case and goals for incentive optimization. The target metric is “Protocol Utility”:

Where revenue is defined as fees captured by protocol stakeholders for rewards eligible pools optimized by Gauntlet (equivalent to 5 bps * volume for typical pools) and where incentives is defined as the USD value of liquidity rewards given to LPs (varies pool-by-pool).


  • Ingest additional Sushi DEX data (new chains and pools) into the platform, maintain existing data
  • Develop and customize incentive optimization elasticity models
  • Continuously refit, update assumptions, and analyze model outputs
  • Provide weekly incentive recommendations to the Sushi team

As an example, to better understand trading dynamics and volume elasticities within Sushi, we use a combination of simulation, address tagging, and price analysis to classify arbitrage trading to separate retail volume from volume originating from bots:

Here is another view showing slippage incurred per swap for a few major Sushi pools. Positive slippage implies that the trader received more money out than they put in, which is likely arbitrage:


  • Incentive and Total Budget Optimization Updates
    • Coverage of Sushi’s native pools (current and future, multi-chain)
    • Supported Parameters: incentive budget, allocation points per pool
    • Ad-hoc analyses to understand tradeoffs between growth and profitability
  • Communications
    • Gauntlet will share methodology details with the Sushi team along with weekly incentive recommendations
    • Gauntlet will perform a quarterly retrospective on the market and results to be shared in the forum
  • Out of Scope
    • Protocol development work, (e.g. solidity changes that improve risk/reward)
    • Formalized mechanism design outside of the supported parameters
    • Risk management for Sushi’s lending protocol


As part of this engagement, Gauntlet will build a dashboard (example below) to provide key insights into incentive optimization, with the key metric being Protocol Utility.


Gauntlet charges a service fee that seeks to be commensurate with the value we add to protocols and provides a strong signal of our alignment with the protocol itself.

In order to increase our alignment with Sushi’s goal to maximize profitability, renewal pricing for the upcoming 12-month term will be structured as follows:

  • Flat service fee of $500k - denominated in $SUSHI at 30d VWAP, Sablier stream at outset of the engagement, with a 12-month linear vesting period, reduced by 50% from previous year’s engagement
  • Variable quarterly performance fee at 3.6% of Protocol Utility - denominated in $USDC or $SUSHI at previous day close, with no vesting period

Gauntlet will request payment at the end of every quarter for the 1-year term. Any change to the key metric requires a performance fees assessment and change, which will be put up for discussion and vote.

Quarterly Protocol Revenue: $5.5M
Quarterly Protocol Incentives: $3.8M
Quarterly Protocol Utility: $1.7M
Quarterly Gauntlet Performance Fee (3.6%): $61.2k

About Gauntlet

Gauntlet is a simulation platform for market risk management and incentive optimization. Our prior work includes optimization work for Compound, Aave, ApeSwap, Benqi, Acala, MakerDAO, and Synthetix.


  • Yes, renew Gauntlet’s expanded incentive optimization engagement for 12 months
  • No, do not renew

0 voters


I appreciate the shout out but im assuming you wanted to link to SUSHI 2.0 which is below. You linked to my updated proposal which has not finished going thru governance yet so just want to make sure there isnt any confusion for readers as 2.0 is currently the “official” doc. Ill read thru the rest of your proposal but just wanted to flag this


Couple questions before i can cast an informed vote:

  1. What was the previous contract fee structure?
  2. When is the dashboard supposed to get deployed?
  3. What is the justification for 3.6% protocol utility fee? Are you measuring some pre-gauntlet vs post gauntlet delta? If so what is it?
  4. How many gauntlet team members are assigned and available to SushiSwap during this agreement

what differentiates these 2?

How much of the fee is dedicated to this task?

@JiroOno do we have some metrics to internally verify the impact of gauntlet? Would prob be helpful to get some insight from the team on the next community call as to the value proposition



Great questions.

  1. $1M fixed - denominated in $USDC, with no vesting period
  2. Dashboards will be provided within the first 2 weeks of the engagement. They will be private to the core team to prevent leaking alpha about upcoming rewards changes.
  3. In order to better align our optimization work with Sushi’s profitability goals, we lowered our fixed fee (from $1M to $500k) and added a quarterly variable performance fee based on the target metric we are optimizing (Protocol Utility, essentially revenue less incentives for pools we optimize).
  4. The team includes a dedicated program manager and product manager, supported by two teams (Incentive Optimization and Platform) comprised of 10 data scientists and engineers.

“What differentiates these 2?” - We are currently optimizing incentive allocation across pools within a fixed total budget. As part of the expanded engagement, we will also optimize the total budget (how much to spend) on a weekly basis.

“How much of the fee is dedicated to this task?” - This is baked into the variable performance fee as this is based on revenue for pools we optimize.


Seems like a better all around way of paying a fee. Aligns incentives for Sushi & Gauntlet with maximizing revenue stream which is good. How did you arrrive at 3.6%? Seems specific. And do you have an estimate of total protocol utility for what you optimize for the trailing 12 months to give an idea of what we could be looking at

3.6% is based on our internal projections for “Protocol Utility” for the upcoming 12 months that puts the total fee (fixed plus variable) at around $1M annually (matches the previous contract fee structure, but with more incentive alignment).

Protocol Utility for the trailing 12 months is -$98.6M. A better metric is Protocol Utility for the past 90 days, which was roughly $400k — this would come out to a ~$14k performance fee for the quarter.

1 Like

98.6M for trailing 12 months but just 400k for trailing 90 days (1.6M annualized)? So a 98% drop?

It’s negative 98.6M for the trailing 12 months

Got it ty … need to get my eyes checked apparently. was reading it on my phone and thought it was a ~ not a -

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Sushi may one day want to set-up their own data pipeline for analytics/reporting. Is the data in the Gauntlet data store made available to Sushi?

Hey @Andrew - this is someting Flipside Crypto does!

We were awarded a $1,000,000 grant to curate and maintain free and accessible data for Sushi.

We use this grant to pay users to create powerful analytics for the DAO. You can check it out here.

So as interesting as this proposal is we really need some feedback from the team as to what they think and what internal KPIs there are to justify the cost.

Hard to cast an informed vote without team feedback