Flow AI
Last updated
Last updated
Flow AI is building a new primitive for AI-driven liquidity management (AILM) with our HAL 9000 algorithm. It is designed to maximize APYs and optimize our concentrated liquidity strategies on Uniswap v3.
Our goal is to establish a benchmark for efficiency and profitability in liquidity provisioning and empower our users, offering them access to the most rewarding liquidity-providing opportunities available.
Automated market makers (AMMs) like Uniswap have made liquidity provision more accessible, yet maximizing yields remains a challenge for users. The need for constant monitoring of liquidity pools and manual processes for adjusting positions and claiming fees and rewards adds complexity. This situation deters users, especially new entrants to DeFi, due to the steep learning curve. Providing liquidity for long-tail assets is particularly challenging due to their lower volumes and high price volatility, often resulting in these assets being overlooked and exposing providers to risks like impermanent loss.
In the current landscape of liquidity management, many DeFi protocols have placed a disproportionate focus on TVL rather than on optimizing APYs. This approach often results in a trade-off for users, where the potential for maximizing earnings is compromised.
The primary issue stems from the notion that a larger TVL is inherently better, signifying a more successful or reliable protocol. However, this focus on TVL growth overlooks a crucial aspect: the efficiency of capital utilization. In scenarios where TVL is excessively large, it can lead to diminished returns for individual liquidity providers. Essentially, as the size of the pool increases, each participant's share of the trading fees — which are contingent on trade volume — becomes diluted.
This dilution effect means that despite contributing to a large pool, the actual yield for a liquidity provider may not be optimized. The situation is akin to having a larger pie but receiving a smaller slice. In contrast, a more balanced approach that emphasizes APY optimization would seek to align the size of the liquidity pool more closely with actual trade volumes, ensuring that each participant's capital is used more efficiently and profitably.
Therefore, the current emphasis on expanding TVL without adequate consideration for APYs leads to a suboptimal scenario for liquidity providers who are looking to maximize their returns. This underscores the need for a strategic shift towards APY-focused liquidity management, which not only values the size of the pool but also prioritizes the efficiency and profitability of each liquidity provider.
At Flow AI, we're pioneering a new standard: prioritizing APY maximization, not just conventional TVL expansion.
Flow AI redefines liquidity provisioning with our AILM solutions:
CALM (Concentrated Active Liquidity Management): An on-chain vault that allows liquidity providers to manage their positions across various risk pools effortlessly. It eliminates the need for constant market monitoring and manual rebalancing, making liquidity provision more accessible.
HAL 9000: Our AI-driven engine, leveraging adaptive learning, ensures optimal 24/7 liquidity management. It enhances capital efficiency and maximizes APYs while minimizing costs and risks by adapting to market conditions in real time.
In summary, CALM & HAL 9000 as a package streamlines LP participation and empowers passive management in both low and high-risk pools.
First AI Liquidity Management - A new primitive
24/7 Real-Time Optimization - AILM solution combining CALM & HAL 9000
Peak Capital Efficiency - Refer Present Liquidity Management Landscape
Community-Centric Rewards - Access for PLX stakers, enhanced earnings in PLX
Enhanced Cost Efficiency - Batch positions, no unnecessary position shift
Emphasis on Long Tail Assets - Blue ocean strategies, overlooked assets.
Built for Simplicity - Ease of use, hassle-free management
Attractive to All Liquidity Providers - Targeted focus on high-volume pairs, higher R:R
#KeepCALMCarryOnPrinting #HAL9000
