Forward the Original Title: DeFai = DeFi + Ai

What happens when we combine old DeFi with the new shinyAI?

What mutant / innovation can we create?

Today, we will explore the early landscape of DeFai.

I hope you find it useful!

( *I’ll be publishing a 20 pages deep dive on Medium soon. Today’s thread is just a fast food post for you to have a glimpse of landscape)

Why DeFai?

AI have a long history with blockchain, from the early days of decentralized model training on Bittensor subnets, to decentralized GPU / computational resources marketplaces like Akash and io.net, to the current wave of AI x memecoins and frameworks on Solana.

Each phase has shown that crypto, to some degree, can complement AI with resource aggregation, enabling sovereign AI and consumer use cases.

According to CoinGecko, the mcap of DeFai stands at ~$1B (13 Jan). By the time of writing, Griffian dominates the market with a 45% share, while $ANON holds 22%.

The sector began experiencing rapid growth after Dec 25, when frameworks and platforms like Virtual and ai16z gained momentum following the return of “US money” after the Christmas break.

AND

This is merely the beginning. DeFai’s potential reaches far beyond its current state.

While its integration remains in the proof-of-concept phase, we shouldn’t underestimate its capacity to transform DeFi into a more intelligent, user-friendly, and efficient financial ecosystem through AI capabilities.

Before exploring DeFai’s landscape, it’s important to understand the fundamental mechanics of how agents operate within DeFi and blockchain environments.

How agents work in DeFi?

AI agents are programs that execute tasks on behalf of users following specific workflows. At their core, these agents are powered by LLM that generate responses based on their training data.

In blockchain, agents can interact with smart contracts and accounts to handle complex tasks without constant human intervention.

For example:

~ Simplify defi UX by executing multi-step bridging and farming in one click

~ Optimizing yield farming strategies for better returns

~ Autonomously executing trade (buying/selling) based on third party or it’s own market analysis etc.

Taking reference from @threesigmaxyz research, model usually follow 6 specific workflow:

• Data collections

• Model inferences

• Decision making

• Hosting and operations

• Interoperability

• Wallet

Once you “collect” the 6 stones of AI element, you can now build your own autonomous agent on da blockchain, where different agents can each play a role in the defi ecosystem to improve efficiency and trading experience on-chain.

Entering the World of DeFai v2.

In general, I have categories DeDFI x Ai into 4 main categories.

• Abstraction / UX friendly AI

• Yield optimisation or portfolio management

• DeFai infrastructure or platform

• Market analysis or prediction

Abstraction AI or AI chatgpt

In this sector, an effective AI solution should be able to:

- Automatically executes multi-step trading and staking operations, this requires no prior industry knowledge from users.
- Performs real-time research and delivers all necessary info and data that users need to make informed trading decisions.
- Fetches data from various platforms to identify market opportunities and provides comprehensive analysis for users.

Without further ado, let’s study some of the popular player in the landscape:

Griffain

@griffaindotcom is the first, and most performant abstraction Ai on solana right now that can execute swap , wallet management, NFT minting, and token sniping and many more.

To be specific, here are the functions that griffain offer:

• Execute trade with natural language

• Launch token with pumpfun, mint NFT and you can select address to airdrop.

• Multi-agent coordinations

• Agents can tweet on behalf of users

• Sniping new launch memecoins on pumpfun based on certain keywords or condition

• Staking, automations and execute defi strategy

• Scheduling tasks, users can input memory into agents to build tailor agents.

• Fetch data from platforms for market analysis, such as identifying the top holder of a token.

Wallet:

Upon account creation, the system generates a wallet with privy where users can delegate the account to agents to execute transactions and manage the portfolio autonomously. In which, keys are split by Shamir’s secret sharing so that neither griffain nor privy can hold custody of the wallet.

Anon:

@HeyAnonai is built by @danielesesta , known for creating the defi protocol wonderland and MIM. Anon is made to simplify his defi interactions for both the newcomers and veterans.

Some of the function included:

• Cross chain bridging enabled by LayerZero

• Getting real time price & data feeds via Pyth

• Offering time-gas-price base automation & triggers

• Providing real time market Insights such as sentiment check, social profile analysis etc.

• Borrowing and supplying with partnered protocol such as Aave, Sparks, Sky & Wagmi

• Execute trade with natural language (International language including Chinese)

Other than that, Daniele published 2 majors updates about Anon recently:

Automated Framework:

Gemma research focus agents

This makes Anon one of the most anticipated abstraction tools across the landscape.

Slate (No Token Yet):

Backed by BigBrain Holdings,@slate_ceo positions itself as an “Alpha AI” that autonomously trades based on on-chain signals. Currently, slate is the only abstraction AI capable of automating and executing trades on @hyperliquidX

One thing that worths to notice is their fee structure:

Overall, there are 2 fee categories:

General actions: Slate does not charge a fee for regular transfer / withdrawal but charges a 0.35% fee for swap, bridge, claim, borrow, lend, repay, stake, unstake, long, short, lock, unlock etc.

Conditional actions: If you set a condition order (E.g., limited order), slate charges 0.25% gas conditions or 1.00% for all other conditions.

And the landscape have many more players coming, naming them:

- @AIWayfinder

- @orbitcryptoai

- @dolion_ai

- @askthehive_ai

- @HeyElsaAI

- @Spectral_Labs

- @Infinite_Labs

- @ProjectPlutus_

- @bankrbot

and more….

Here is a table comparing some of the Abstraction AI comparison:

Autonomous Yield optimisation and Portfolio Management:

Unlike traditional yield strategies, protocols in this sector use AI to analyze onchain data for trend analysis, then provide insights that help the teams develop better yield optimization and portfolio allocation strategies.

T3AI:

@trustInWeb3 is a lending protocol that supports under-collateralized loans by using AI as an intermediary and risk engine.

The protocol’s AI agent continuously monitors loan health in real-time and is capable of ensuring the loan to remain repayable through T3AI’s risk metric framework.

An Interesting use case of AI in Defi tbh.

Kudai

@Kudai_IO is an experimental, GMX ecosystem-focused agent launched by GMX Blueberry Club using the EmpyrealSDK toolkit. The token is currently trading on Base.

Below is Kudai’s roadmap:

Kudai’s concept is to channel all trading fees earned from $KUDAI into funding agents for autonomous trading operations, then distribute profits back to token holders.

In the upcoming Phase (2/4), Kudai will be able to interpret natural languages commend on Twitter to:

• Buy and stake $GMX to generate new revenue streams
• Invest in GMX GM pools to enhance earnings further
• Sweep GBC NFTs at floor prices to expand its portfolio

Sturdy Finance V2:

@SturdyFinance is a lending and yield farming aggregator that leverages AI models (trained by Bittensor SN10 subnet miners) to optimize yields by moving funds between different whitelisted silo pools.

Specifically, Sturdy runs on a 2 tier architecture consisting of silo pools and aggregator layer.

1. Silo pools are isolated single asset pools where users can only lend one asset or borrow with one collateral.

2. Aggregator layers are built on top of Yearn V3, where users’ assets are disturbed to whitelisted silo pools according to the utilisation rate and yield. Bittensor subnet provides the aggregator the best allocations strategy. When users lend to the aggregator, they maintain exposure only to their selected collateral types, eliminating any risk from other lending pools or collateral assets.

More players in the yield / prof management landscape include:

- @derivexyz

• @Thales_ai

- @Mozaic_Fi

- @boltrade_ai

- @vainguard_ai

- @Ensofi_xyz

- @0xARMAgeddon

- @glamsystems

and more…..

Market Analysis Agent:

Aixbt:

@aixbt_agent is a market sentiment tracking agent that aggregates and analyzes data from over 400 KOLs on Twitter. Using its proprietary engine, aixbt can identifies real-time trends and publishes insights around the clock.

Among all AI agents in the space, AixBT commands a significant 14.76% mindshare, making it one of the most influential agents in the ecosystem.

His capabilities is beyond just delivering alpha - he is interactive, capable of replying to user questions, and even token launches through twitter using specialized toolkits.For example, the $CHAOS token was created through collaboration between AixBT and another interactive bot called Simi using the @EmpyrealSDK toolkit.

Other market analysis agents include:

- @tri_sigma

- @ASYM41b07

- @kwantxbt

- @gemach_io

Defai Infrastructure and Platform:

This is a big sector to cover.

Web3 AI agents would not be possible without a decentralized infrastructure. These projects provide not only model training and inference but also data, verification methods, and a coordination layer for AI agents to develop on.

Regardless of Web2 or Web3 AI, model, compute, and data are the 3 ultimate bedrocks for driving LLM and AI agent excellence.

In the medium, we have dived into the details of:

• Model Creation

• Data & Compute Provider

• Verification

• How do TEE work?

Since there’s so much ground to cover, I have written all the details / concept in the medium , please stay tuned for it.

Below is a good Defai infra ecosystem map created by @pinkbrains_io but

Some of the key players in this landscape include:

TEE:

- @PhalaNetwork

- @MarlinProtocol

- @AutomataNetwork

Framework:

- @arcdotfun

- @ai16zdao

Platform / All in one:

- @virtuals_io

- @aisweatshop

- @Almanak__

- @autonolas

- @Cod3xOrg

- @crestalnetwork

- @CreatorBid

- @openservai

- @WaveformBackup

- @getaxal

- @EmpyrealSDK

General Infra:

- @加入FXN

- @TheoriqAI

- @hyperbolic_labs

- @BagelOpenAI

- @Hive_Intel

Toolkit:

- @sendaifun

- @lexiconinfra

The Next Step for DeFai.

I always believe the market will evolve in 3 stages: first demanding efficiency, then decentralization, and finally privacy.

There will be 4 phases of defaiI.

Phase 1 of DeFi AI will focus on efficiency, with tools that improve user experience for complex DeFi tasks without requiring deep protocol knowledge. Examples include:

• AI that understands user prompts even with imperfect formatting
• Fast swap execution within minimal block times
• Real-time market research helping users make profitable decisions based on their goals

If innovation is realised, they can save time and energy while lowering barriers to on-chain trading, potentially creating a “Phantom” moment in the coming months.

In Phase 2, agents will trade autonomously with minimal human intervention. Trading agents that can execute strategies based on third-party insights or other agents’ data will create a new DeFi paradigm. Professional or sophisticated defi users can fine-tune their models and build agents to generate optimal yields for themselves or their clients which requires less manual monitorisation.

In Phase 3, users will begin to focus on wallet management concerns and AI verification, as users demand transparency. Solutions like TEE and ZKP will ensure AI systems are tamper-proof, protected from third-party interference, and verifiable.

Finally, once these phases are complete, a no-code DeFi AI engineering toolkit or AI-as-a-service protocol could create an agent-based economy where fine-tuned models are traded using crypto.

While this vision is ambitious and exciting, several bottlenecks remain unsolved:

• Most current tools are simply ChatGPT wrappers, with no clear benchmarks to identify legitimate projects
• On-chain data fragmentation pushes AI models toward centralization rather than decentralization, and it’s unclear how onchain agents will address this issue these days.

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