Web3 vs AI Product Design: What's Actually Different?
We design for both, and founders often ask whether that makes sense. It does — because underneath, Web3 and AI products have the same design problem. They ask users to trust something they can't see. Where they differ is where that trust breaks.
The shared problem: invisible machinery
In a Web3 product, the hard part happens on a chain the user can't see. In an AI product, it happens inside a model the user can't see. Either way, the user is asked to act — sign, send, accept, rely on an answer — without being able to inspect what's going on. Good design for both comes down to the same job: make the invisible legible enough to trust.
Where they differ
| Web3 products | AI products | |
|---|---|---|
| Where trust breaks | The moment of commitment — signing, paying gas, moving assets | The moment of reliance — acting on an answer that might be wrong |
| Biggest friction | Onboarding: wallets, seed phrases, networks, gas | Expectation-setting: what it can do, what it can't, how sure it is |
| Failure users fear | Losing money, irreversibly | Being misled, confidently |
| Key states to design | Pending, confirmed, failed, reverted transactions | Thinking, partial, uncertain, wrong answers and recovery |
| What builds trust | Clear amounts, plain-English signatures, audits, on-chain proof | Showing sources and reasoning, easy correction, honest limits |
Web3: design the commitment
Web3 users are most anxious right before something irreversible. That's where design effort pays off: say exactly what will happen, in words, before the wallet pops up; show the real amount and the real cost; and design every state after the click — including failure. Better still, remove the scary parts entirely where you can. For Slingshot we used account abstraction so 5,000+ Web2 gamers could take part on-chain with no seed phrase and no gas. For Aventus, card payments, custodial wallets and KYC let mainstream buyers use an NFT marketplace that stayed fully on-chain.
AI: design the reliance
AI users are most at risk right after they get an answer, because it looks finished whether or not it's right. Design here is about calibrated trust: showing what the AI used to reach an answer, making it obvious how to correct or retry, and being upfront about what it can't do. The worst AI interfaces are the most confident ones.
There's a second AI problem that's more about the product than the model: a lot of AI products are now built by AI. Vibe-coded apps tend to drift — each generated screen forgets the one before it, so onboarding, navigation and states fall apart by screen four. We wrote more about that in fixing an AI-generated app for real users.
Where they overlap — and why it's one discipline
- Both need honest states. Pending transactions and "thinking" responses are the same design problem: keep the user oriented while something slow and invisible happens.
- Both are judged on first impressions. Users and investors have seen scammy crypto apps and flaky AI demos. Polish is how you show you're not one of them.
- Both increasingly come together. AI agents that hold wallets, on-chain AI marketplaces, AI tooling for Web3 communities — the products being funded now often need both skill sets in the same flow.
Could a new user explain, in one sentence, what just happened and what happens next? If they can after signing a transaction and after reading an AI answer, the product earns trust. If they can't, it's spending it.