AI x Crypto is a hot narrative right now.

But what does “AI x Crypto” even mean? Is it all hype, or do these projects have substance?

To answer this, we've written a 90-page report.

Key ideas are in this 🧵👇

(1/12) If you want to go straight to the report: linktr.ee/aicryptoprimer

Over the last few months, the Danu team has done extensive research, and we've got 17 papers/articles in the pipeline.

We will be sharing those with the community starting next week!

Here is some info on the papers I am most excited about 🧵👇 1) NFT Market making

MM for NFTs differs from crypto as there is no layered order book structure and a long expected duration between transactions. We present a quantitative approach to compute MM prices where we model NFT price dynamics and view it through embedded optionality.

Introducing Granular: the next generation NFT AMM.
It’s on-chain, gas-efficient and preserves the NFT value (doesn’t demote assets in the pool to the floor price).

Granular was one of the winners at @Macro_Hacks by @1kxnetwork @nftfi

A brief description 👇🧵 Let’s start with AMMs for fungible tokens. In a @Uniswap or @SushiSwap pool, you have two fungible tokens (e.g. ETH and DAI). Each token has the same value, I can buy or sell fractions of these tokens, and the price is decided by a bonding curve: x*y = k