AI in Web3: Projects Using AI to Improve User Experience

"AI in web3" is a broad label, and it helps to separate its meanings before looking at individual projects. Most products fall into one of four groups.

• AI services paid for with crypto. A chat, image or video tool where access, credits or staking run through a token or wallet instead of a card payment.
• Decentralized AI infrastructure. Networks where independent operators supply GPUs or models, and developers pay to run inference through an API.
• Onchain agents. Software that reads onchain data and takes actions for a user, such as moving funds between lending pools according to rules.
• AI as an interface layer. Assistants that explain transactions, summarize proposals, search documentation or answer support questions.

These groups solve different problems. A private chat tool is about who sees your prompts. An inference network is about cost and access for developers. A yield agent is about saving time on repetitive financial decisions. An explainer assistant is about making complex interfaces readable.

The label is also attached to plenty of tokens whose connection to AI is thin. A useful habit is to ask four questions of any project: what does the model actually do, where does it run, who pays for it, and what does the token do that a normal payment could not? Projects that answer these plainly in their documentation tend to be easier to evaluate. Projects that answer with slogans usually are not.

The rest of this guide uses three examples that sit in different groups, Venice, ZyFAI and Chutes, and then covers the wider patterns, the risks and a checklist.

Three dApps from our directory show how different the use of AI can be. The table summarizes what each one does, based on how the projects describe themselves.

Project	Group	What the AI does	Who it is for
Venice	AI service	Private-focused chat, image and video generation, plus API access; VVV token on Base	Creators and developers
ZyFAI	Onchain agent	Automates allocation across vetted lending pools on Base, Arbitrum, Ethereum, Sonic and Plasma	DeFi users who want yield without manual rebalancing
Chutes	Infrastructure	Serverless AI inference on Bittensor with an OpenAI-compatible API and pay-per-token pricing	Developers building AI features

The user-experience gains differ in each case. Venice reduces the friction of using AI tools while keeping privacy a priority. ZyFAI reduces the time and attention that yield management normally takes. Chutes reduces the setup work for developers, who can call models through an interface that works like a familiar API and pay for what they use.

None of these is a guarantee of quality or returns. Supported models, networks, features and token details change, so confirm the current state on each project's official site and documentation before relying on it. The sections below go through each project and then the shared patterns.

Venice is an AI platform built around privacy. It offers chat, image generation, video generation and API access, and it has a token, VVV, on Base. Its audience is mainly creators who want generative tools and developers who want to build on top of them.

Where the experience improves

• Privacy posture. Mainstream AI chat services generally tie conversations to an account and a company's data policies. Venice positions itself as a privacy-focused alternative, which appeals to people who do not want their prompts treated as a product input. How that works in practice is set out in its own documentation and policy, so read those rather than assuming.
• One place for several media types. Text, images and video in one product cut the need to juggle subscriptions.
• API access. Developers can integrate the models into their own apps instead of building a pipeline from scratch.
• Crypto-native access. A token on Base ties usage and participation to a wallet, which suits users who prefer onchain accounts.

What to check

• What the platform means by private: what is stored, where, and for how long.
• How access works: free tiers, paid plans, staking or token-based credits, since these terms can change.
• Which models are available and their usage limits.
• What the VVV token does, and whether you need to hold it at all to use the product.

As with any AI chat, do not paste seed phrases, private keys or passwords into prompts. A privacy-focused platform lowers some risks but cannot protect secrets you hand over voluntarily.

ZyFAI is a self-custodial DeFi yield agent. In plain terms, it automates the allocation of your funds across lending pools it has vetted. It works across Base, Arbitrum, Ethereum, Sonic and Plasma.

The problem it addresses

Lending yields differ between pools and networks and shift over time. Capturing the best option by hand means monitoring rates, judging risk, bridging or moving funds, paying gas and repeating the process. Many users either leave money in one place or stop paying attention. An agent handles the monitoring and the repositioning according to its rules.

What self-custodial means here

Self-custodial design means the user's funds are not handed to a company to hold. The agent acts within permissions the user grants. That is different from depositing into an account run by a service. It still does not remove risk. You are exposed to the underlying lending protocols, the contracts the agent uses and the quality of its decisions.

What to check before using it

• Which pools are in the vetted set and how the vetting is described.
• What permissions you grant, and how to revoke them.
• How fees are charged, and when.
• How the agent decides to move funds, and what happens if a pool is paused or has a problem.
• Withdrawal steps and any delays.

Yield figures change constantly. Use the app's live dashboard for rates and never treat a past number as a promise. Start with a small amount while you learn how it behaves.

Chutes is a decentralized, serverless AI inference platform that runs on Bittensor. Inference means running a trained model to produce an output, such as answering a prompt or generating an image. Chutes offers this through an OpenAI-compatible API with pay-per-token pricing, aimed at developers.

Why that matters for user experience

The end user rarely sees Chutes directly. The benefit shows up in apps built by developers who use it.

• Familiar interface. An OpenAI-compatible API means many existing tools and libraries can point to a different endpoint with limited code changes.
• Serverless model. Developers do not need to rent and manage GPU servers. They call the service and pay for usage.
• Pay-per-token pricing. Costs scale with use, which suits small projects and experiments.
• Decentralized supply. Because it runs on Bittensor, compute and models come from a network of participants instead of one company. Bittensor organizes this through subnets, each focused on a type of task.

What to check

• Which models are offered and their context limits.
• Current pricing per token, which can change.
• Reliability and latency for your workload, ideally through a small test before you commit.
• How data is handled when your requests run on third-party hardware, particularly if your application processes sensitive information.

Decentralized does not automatically mean cheaper or faster. It can offer more choice and fewer single points of control, but you should compare it against alternatives using your own use case.

Beyond these three projects, AI improves crypto experiences in a handful of repeatable ways.

• Plain-language transaction explanations. Wallets and explorers can summarize what a transaction or contract call will do, which helps users avoid signing something they do not understand.
• Intent-based actions. Users state a goal, such as moving funds to a network or finding a yield, and software works out the steps. Agents like ZyFAI follow this logic within narrow rules.
• Support and documentation search. Assistants answer questions from a project's docs, cutting the time spent hunting through guides and forums.
• Governance summaries. Long DAO proposals and discussion threads can be condensed so voters can follow them.
• Risk and fraud screening. Models can flag suspicious addresses, phishing patterns or unusual contract behavior. These flags are a signal, not a verdict.
• Personalization. Dashboards and alerts can adapt to what a user holds or follows.
• Developer tooling. AI helps write and review code, including smart contracts, although generated code still needs expert review and audits.

A pattern runs through these examples. AI works best on reading, summarizing, matching and routine optimization. It is weakest where mistakes are irreversible and the stakes are high, which describes many onchain actions. For that reason, the better products keep a human confirmation step for important actions and limit what an agent is allowed to do.

AI and crypto each carry risk, and combining them adds some new ones.

Model errors. Language models can produce confident, wrong answers. An assistant that misreads a contract or invents a function can mislead a user. Treat explanations as a starting point and verify anything involving money.

Agent permissions. An agent that can move funds is only as safe as its limits. Broad approvals, unclear rules or bugs can lead to losses. Prefer agents that operate within narrow, revocable permissions.

Smart contract and protocol risk. An AI layer does not change the risk of the lending pools or contracts underneath. If a pool is exploited, a smart agent cannot undo that.

Prompt injection. Agents that read web pages, messages or onchain data can be manipulated by malicious text designed to steer their behavior. This is a known weakness of AI systems that take actions.

Privacy leakage. Prompts may be logged, processed on third-party hardware or used in ways you did not expect. Policies differ widely between services.

Fake AI projects. Scammers use the AI theme for rug pulls, fake agents and impersonation. Giveaways, cloned sites and fake support accounts are common.

Token and market risk. A project's token can lose value regardless of how good the product is. Using a service and speculating on its token are separate decisions.

Regulatory uncertainty. Rules on AI services and crypto vary by country and are still changing. Check what applies where you live.

Use this checklist before connecting a wallet or paying for any AI-related web3 service.

1. Identify the real function. Can you describe in one sentence what the AI does and why it needs a blockchain? If not, be cautious.
2. Read the documentation. Look for specifics on architecture, data handling, fees and limits, not just a vision statement.
3. Check the permissions. For any wallet connection, review what you are approving. Avoid unlimited approvals where a limited one will do, and know how to revoke access.
4. Look at the security record. Find out whether contracts have been audited and by whom. An audit reduces risk but does not remove it.
5. Understand the token. Work out whether it is required for use, offers discounts, grants governance rights or does nothing practical.
6. Verify the official channels. Reach the project only through links in its verified accounts. Ignore direct messages offering help or rewards.
7. Test with a small amount. Deposit, use and withdraw a small sum first to see how the process works.
8. Keep secrets out of prompts. Never enter seed phrases, private keys or passwords into an AI chat or an agent.
9. Use a separate wallet. A dedicated wallet with limited funds limits damage if something goes wrong.
10. Check legality locally. Make sure the service is allowed where you live.

Because AI and crypto both move quickly, some details in this guide will date. The structure of the space is more stable than the specifics.

What tends to stay constant

• Private or open AI tools will keep competing with large centralized providers on trust, price and control.
• Decentralized networks will keep trying to offer compute and models without a single operator, and will be judged on reliability and cost.
• Agents will keep taking over routine financial chores, with the debate centered on permissions and accountability.
• Interface assistants will become standard in wallets and apps, with the question being how far users should trust them.

What you should check live

• Token prices, supply and market data: use a reputable market data site.
• Supported chains, pools, models and pricing: use the project's official documentation and app.
• Security incidents: check the project's announcements and well-known security trackers.
• Network activity on Bittensor subnets: use the network's own explorers and dashboards.
• Legal status in your country: consult official regulator guidance.

Nothing here is financial advice. Yields, token values and AI service quality can change at any time, and you can lose money. Pick tools whose function you understand, keep control of your keys, and expand use gradually.