Announcing OpenGradient x UAGP
Today, the OpenGradient team is excited to announce that we will be working with UAGP (Uniswap-Arbitrum Grant Program) as research grant recipients to conduct research on applied machine learning in AMM fee optimization. See the Twitter official announcement here.
The research will be focused on developing models to intelligently compute dynamic fees for the Uniswap V4 AMM to reduce net impermanent loss for liquidity providers.
The model inference, proof generation, and validation that secures the inference will all be powered by OpenGradient, an EVM blockchain network that is a composable execution layer for on-chain AI inference. The network features access to scalable and secure model inference, allowing developers to seamlessly leverage AI models in composable smart contracts to create powerful decentralized applications and use-cases.
You may remember AMM dynamic fees as one of the most impactful use-cases of on-chain inference in our previous article Applications of AI/ML on the Blockchain, the team cannot be more excited to be tackling one of these use-cases head on in this research initiative.
The Uniswap AMM
AMM pools on decentralized exchanges have skyrocketed in popularity over the past couple years, with AMMs like Uniswap crossing $10 billion in total value locked (TVL) at its peak with billions of dollars in trading volume every day. They’ve also continuously iterated on the design of their protocol, with the recent announcement of the imminent release of their V4 design.
As much as we love decentralized market making, unfortunately most liquidity providers in AMMs not only cannot make money, but actually end up with net losses as a direct result of impermanent loss which is loss of liquidity position value as a result of diverging price ratio between the two assets in the AMM pool.
In the above, one can see that impermanent loss can often outweigh fees made from an AMM pool. If market-making is not profitable in Web3, how are centralized market-makers like Citadel Securities consistently generating >$1 billion in revenue for 15 straight quarters?
The answer lies in the fee mechanism of market-making: centralized market-makers have sophisticated models of varying the spreads they quote depending on the market while AMMs are stuck charging the same fee agnostic of the market environment. This means when the market is extremely volatile arbitrageurs trade cheaply against the LPs which incurs impermanent loss, and when the market is very calm traders may opt to trade on centralized venues with cheaper fees which reduces revenue for LPs.
Enter: OpenGradient
Like aforementioned, OpenGradient is a blockchain network that can support native AI/ML inference directly computed, secured, and verified on-chain; all designed in a way that is so seamless that leveraging AI/ML is as easy as a simple function call.
We’re designing inference to suit all sorts of use-cases depending on demands for speed, cost, verifiability, and security. In additional to vanilla inference, other flavors include secure inference through protocols like opML (optimistic machine learning) and zkML (zero-knowledge machine learning), inference designed for privacy in trusted execution environments (TEE)…etc. The verifiable and secure nature of the inferences will unlock high-stakes use-cases of AI and ML like DeFi, which we are ecstatic to explore.
The research the OpenGradient team is conducting is centered around creating a dynamic fee computation model that can vary the fees quoted in AMM trading depending on the market condition, this can both reduce net impermanent loss for LPs during volatile markets and bring lower fees to retail traders during non-volatile markets.
As can be seen in the explanation above Uniswap V4 will become an increasingly sustainable DeFi protocol in the adversarial world of on-chain trading with OpenGradient’s dynamic fees.
- Liquidity Providers win because they will suffer from less net impermanent loss in the long run.
- Regular traders win because when users trade normally most of the time in non-volatile markets they can actually get lower fees.
- Opportunistic arbitrageurs lose because they make less net profit (due to higher fees) when they arb against a pool.
If you’ve been following the OpenGradient publication on Medium, you would already have context through a previous experiment we’ve done with ML-driven fees. Check out the write-up here where we ran some AMM simulations with a proof-of-concept regression model quoting dynamic fees: Protecting AMM Liquidity with On-chain ML Models.
In addition to simply just AMM dynamic fee calculation, OpenGradient’s infrastructure is widely applicable to all sorts of different use-cases in Web3. By taking advantage of interchain queries , we aim to bring trustless inference to different blockchain ecosystems to empower their dApps. Smart contracts on EVM blockchains like Arbitrum can make a simple interchain query and be able to enable all sorts of intelligent dApps and smart features.
Lastly, we’re excited about the fact that OpenGradient’s infrastructure and integration with Uniswap opens doors to new verticals of research in DeFi. Iterating on models to help improve the initial fee calculation model, or building out sophisticated models to create new use-cases on Uniswap like:
- On-chain systematic trading strategies
- AI-driven smart execution
- Models to predict trade toxicity for varying trading fees depending on trader profile
- Using HMM market regime models to vary fees in different market environments
- Incorporate AI-generated credit scores to vary fees
Conclusion
The OpenGradient team is so excited to be not just developing the network, but also researching core AI/ML use-cases in Web3.0 DeFi. For the Web3.0 space to move forward, it’s important we equip the ecosystem with the tools necessary that will make foundational protocols like AMMs (and more!) increasingly intelligent, smooth, and mature. We plan to be front and center in the movement to empower the crypto ecosystem with more compute that will allow for the development of smarter features.
If you liked this, stay tuned to our socials! We are building infrastructure that will enable more use-cases than just intelligent AMMs. I hope you’re just as excited as we are to see trustless, scalable, and native AI/ML inference introduced to Web3.
Twitter: https://x.com/OpenGradient
Discord: https://discord.gg/y8RD2VEa
