2025 feels like we are back in the DeFi era of 2020 again, but the difference this time is that it's DeFAI. That is DeFi and AI in one. Analysts on social media like X argue that AI agents and chat style interfaces will make DeFi usable for normal humans. However, under all this hype, there is a real shift. AI models are moving on-chain or are being tightly coupled to chains. This lets smart contracts predict, adapt and self govern instead of just following hard coded rules. But the big question is; what changes when contracts and DAOs start thinking on their own? And how are regulators going to react?
Let us dig in and explore this topic, we may just be able to learn something!
Why AI blockchain hybrids are exploding now
Initially, DeFAI was treated as part of DeFi but now it is being treated as its own category in the crypto space. DeFAI refers to AI agents that rebalance protocols, route trades or farm yields on top of DeFi protocols. TradingView and Cointelegraph highlight it as a new trend with hundreds of millions in market cap and dedicated tokens. These tokens include HeyAnon, Mode Network and Orbit.
Trends on social platforms show that DeFAI was popularized in early 2025. Posts by people like Daniele Sestagalli framed AI agents as the user-friendly layer on top of the complex DeFi rails. Some people believe that DeFAI is the new DeFi.
The availability of cheap L2s with an abundant AI infrastructure including GPUs, vector data bases and large language models make the introduction of DeFAI economically viable. It becomes economically viable to run inference trading, risk scoring and governance assisted tied directly to on chain actions. Investors are therefore betting that the next wave of on-chain growth will be run by AI agencies instead of humans clicking buttons in a wallet UI.
Predictive smart contracts
Normal smart contracts are like vending machines in which you can enter commands and they can give you an output. On the other hand, predictive smart contracts are like vending machines with a small AI brain that can look at data and change behaviour.
Smart contracts cannot see the real world like humans so they use oracles. Oracles like Chainlink help them to stream prices, weather, on chain stats and AI model outputs. Those feeds on oracles often include AI generated signals including price forecasts, risk scores and fraud flags. Several offchain AI Machine Learning models run off chain which is cheaper, they then compute predictions and push only the results on chain. The smart contracts can then use the computed results to change terms in real time. For example they can adjust lending rates or rebalance liquidity pools.
Concrete use cases of predictive smart contracts
AI driven vaults on Ethereum or Arbitrum dynamically move liquidity between protocols and chase better risk adjusted yield instead of following one fixed strategy. On Polygon and other chains, AI models score risk covering things like weather, behaviours and historical claims and then smart contracts payout or adjust premiums automatically. This system provides dynamic insurance. In supply chain and RWAs, predictive smart contracts are also very valid. AI can predict shipping delays and price changes in tokenized treasury bills/ real estate. Smart contracts can then tweak deadlines, collateral rations or coupons on-chain.
Predictive smart contracts turn static code into live policies that can watch the world and update themselves within preset limits.
Decentralized Machine Learning (ML) on Ethereum and friends
The real AI native step isn't just calling centralized APIs, it's in training and running models under blockchain rules so that no single part will say that they own the brain.
AI and machine learning can help ensure that it's easier to detect on-chain DeFi attacks. A 2025 AFT paper proposes a fully on-chain learning framework. This means that there would be heavy AI training running on L2, and updates pushed back to Ethereum L1. In addition smart contracts will do gas bounded inference before approving a. DeFi transaction. AI can also introduce a Proof of Improvement mechanism in which a model update is only accepted on-chain if it provably improves accuracy on a public test set. This means that bad updates will be slashed financially. Models are also optimized to fit inside Ethereum’s block gas limit and yet match off-chain accuracy. Ethereum has a decentralized security operations centre where the model itself is governed by consensus.
Bittensor on the other hand runs a decentralized market place where ML models compete to answer queries and get paid in TAO based on usefulness. This is a kind of proof of intelligence mechanism. In 2025, DeFAI write ups highlight Bittensor as the backbone of AI driven DeFi. Models specialise in trading, risk or analysis. Then their signals feed into DeFi strategies via EVM compatibility and dTAO delegation.
Instead of one company owning the trading algorithm, the network owns and trains it. And Ethereum becomes both the ledger and the AI coordination layer.
AI in DAOs and protocol control
Educational and industry pieces now treat DAO governance as a primary use case for AI. AI agents can summarise proposals, simulate effects on treasury and token price, and predict voting outcomes to help token holders make decisions. Some DAOs experiment with having AI delegates. Instead of humans reading every proposal, you delegate votes to an AI agent with a public track record and guard rails. For example you can place a guard rail that does not allow the agent to vote for proposals that raise protocol risk to certain levels.
Research and investor commentary in 2025 openly speculates about AI centred DAOs. Here AI systems propose, rank and help execute governance decisions. At the end the only role of humans in this system is that of an overseer not day to day voters. However, this system is not all roses, what if an AI swarm quietly steers votes to pass a malicious parameter change. Therefore humans still need to be careful on what power they give to AI agents and to what extent.
The thing is that DAOs are moving from crowd voting to AI assisted decision systems. This brings both a UX win as well as brand new governance risks!
Effects of regulation when AI runs the money
MiCA in the EU which went fully in force at the end of 2024 created a harmonised regime for crypto asset issuers and service providers which included custodians and exchanges. As DeFAI protocols start managing user funds and issuing tokens, more of them are going to fall into the MiCA regulated CASP group. This means licensing and capital requirements must be met.
The GENIUS Act U.S. became active in July 2025. This is the first federal stablecoin law which requires payment stablecoins to be backed 1 as to 1 with real cash or low risk assets. As a result, if these stablecoins are compliant, they will be carved out as securities or commodities. Any AI run DeFi or agent wallet stack that leans heavily on dollar stablecoins is now indirectly sitting inside this legal framework.
AI safety laws like Carlifornia’s SB-53 of September 2025 require big AI developers to publish safety frameworks for frontier models and disclose catastrophic risk testing. If protocols outsource critical decisions to frontier scale models, then regulators can publicly argue for audit trails and documented risk controls even in decentralized systems.
Now, for both securities and AI regulators, there are open questions on who is responsible or liable when AI governed protocols misbehave. And now that there are 3 different groups of regulations, it's no longer possible for DeFAI builders to say that they are just protocols. We should therefore expect guidance on whether AI agents running strategies count as investment advisers or automated trading systems. And this would pull some DeFAI projects into existing financial reg buckets.
Final thoughts and conclusion
DeFAI is a thing and this system can be very helpful in the usage of DeFi products by the general public. It is however important to know that data is everything. If an AI agent is trained or fed from an oracle with garbage data, then it will make garbage predictions which results in smart contracts that auto liquidate or misplace risks. In addition, if there is noone who can interpret why an AI recommended a proposal then the DAOs may drift into governance theatre. Now, let's also take note of the fact that running big models is expensive and without careful incentives like Bittensor’s Proof of Intelligence systems, control can recentralize around a few model operators.
AI blockchain hybrids aren't magic, they just move a lot of human decisions into code and models. The opportunity is programmable, transparent intelligence and the risk is that of building opaque, unstoppable systems we don't fully control.
References
- OnchainStandard – “What Are AI-Powered Smart Contracts And How Do They Work In 2025” (2025)
https://onchainstandard.com/guides-education/ai-powered-smart-contracts-2025/ (onchainstandard.com) - Alhaidari et al. – “On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation” (AFT 2025)
https://arxiv.org/abs/2510.16024 (arxiv.org) - Bitrue – “AI-DeFi Integration 2025: TAO’s Rebound and the New Era of DeFAI” (Oct 18, 2025).
https://www.bitrue.com/blog/ai-defi-integration-2025-tao-rebound-defai (bitrue.com) - European Union – Markets in Crypto-Assets (MiCA) Regulation Overview (updated Sept 15, 2025)
https://en.wikipedia.org/wiki/Markets_in_Crypto-Assets (en.wikipedia.org) - U.S. GENIUS Act (Stablecoin Law, 2025)
https://en.wikipedia.org/wiki/GENIUS_Act (en.wikipedia.org)
