Thank you all for the thoughtful feedback and questions. We see three primary areas that require a deeper dive: (1) whether liquidity incentives truly yield sustained, ROI-positive growth; (2) the methodology used for setting KPIs, TVL, and volume targets; and (3) how pools are selected and managed. Below is a deeper look at the approach to each.
Sustainable Impact of Liquidity Incentives
We agree mercenary capital and LPs warrant legitimate concern. Our dynamic optimization approach is specifically designed to counter this. Here’s how:
- Historical Benchmarks & Live Feedback Loops:
Gauntlet’s strategy is backed by prior campaigns on other networks (ref’d in previous comments), where we closely tracked retention after incentives ended. We run constant performance checks (e.g., daily volume, net LP flows, pool market share) and recalibrate incentives every two weeks, aiming to capture not just raw order flow but sticky liquidity that remains even after emissions taper. - Integration With Broader Growth Programs:
We view incentives as one “supply-side” lever in tandem with “demand-side” initiatives—like developer grants, new hook deployments on V4, and early DeFi protocols on Unichain. When incentives are complemented by active builder engagement, the resulting network effects outlast the incentive period. - Evidence of Retention:
In previous Gauntlet-run campaigns, we have documented higher capital efficiency and longer-term TVL retention compared to many self-directed or “set and forget” programs. For instance, 70% of targeted pools in an Arbitrum incentive program showed net positive post-incentive liquidity and volume growth—even after direct rewards ended. As noted in previous comments, some of the programs referenced as coming up short in sustainable impact were not optimized by Gauntlet and did not follow the same high touch approach. We’re confident that the combination of dynamic allocations and concurrent demand generation fosters a higher-than-typical retention rate.
Methodology & KPIs (TVL and Volume Models)
Our modeling and optimization pipelines revolve around the explicit targets set by the Uniswap Foundation for both v4 and Unichain:
- V4: Migrate the equivalent of ~50% of V3’s volume within 3 months, ramping to 75% by 6 months.
- Unichain: Achieve a top-5 chain revenue ranking by year’s end, underpinned by robust TVL and volume.
- ROI-Based Projections:
To set a benchmark for success, we calculate how much incremental TVL and volume an additional $1 of incentives can attract, referencing historical multipliers from other chain-level incentive programs. Using inputs from prior Arbitrum and Uniswap campaigns with a highly conservative discount factor, the rationale includes a starting assumption of $1 in incentives that will lead to $35–$50 of TVL. Although not a guarantee for future performance, this historical range informs our starting baseline. - Continuous Recalibration:
These multipliers are not static. We refit them in real-time based on actual traction, so if a pool or chain sees stronger organic inflow than projected, the system updates to allocate resources more effectively. This ensures that incentives are aligned with user demand, and keeps the DAO from wasting UNI on incentivizing low-traction pools. We also plan to publish dashboards (within ~1 month of launch) so the community can track accordingly and decide whether adjustments are warranted. - Example Data Point:
In the Arbitrum LTIPP Program Retro (Nov 2024), we saw:- $689 of TVL added per $1 of incentives during the incentive period
- A post-incentive retention of $214–$243 in TVL per $1 in incentives
While many programs can see short-lived spikes, ours focuses on measurable and verifiable longer-term TVL retention.
Pool Selection Criteria & Ongoing Management
We believe intelligent pool selection is pivotal to sustainable growth, which is why we emphasize high-volume, core pairs rather than spreading incentives too thinly:
-
Core Token Pairs First:
We prioritize “blue-chip” assets (WETH, WBTC, USDC, USDT), stablecoins, and certain LST/LRT pairs that have shown durable volume in previous deployments. Concentrating early incentives on these core assets supports stable fee generation and fosters user stickiness. While our data collection and quantitative frameworks will remain objective, asset selection will also be dependent on Uniswap Foundation’s strategic guidance and application momentum. -
Developers & Hooks (v4)
For v4 specifically, we aim to enhance developer traction by incentivizing hook-enabled pools that attract new integrations and novel DeFi use cases. We will incorporate UF feedback and community signals to boost the “right” pools, not just those chasing the highest short-term APR. -
Filtering & Monitoring:
We actively filter out underweight pools with high wash-trading risks or minimal long-term potential. Our dynamic approach means that incentives can be scaled back or redirected if a pool starts losing liquidity or performing poorly. -
Transparent Tracking & Reporting:
Our system collects core metrics—like net new LPs, daily active liquidity, volume trends, etc.—on a public dashboard. This allows governance to review the ROI on specific pools and refine future distributions accordingly.
Conclusion & Next Steps
Seeing the passion for Uniswap’s long-term success around these liquidity incentives is great.
We welcome further questions or clarifications on any of the above points, either in the forum or via direct messages. Our team remains committed to transparent, data-driven analytics and stands ready to refine the incentive parameters as real-time market data rolls in.