How I Use AI Tools as a Web3 Developer
It’s been about a year since I started integrating AI tools seriously into my development workflow. I resisted for longer than I should have — partly scepticism, partly the classic developer ego thing of “I should be able to figure this out myself”. Eventually I caved, and I have thoughts.
What Actually Helps
Boilerplate and scaffolding. Asking an AI to generate a basic ERC-721 contract or a standard API handler is genuinely useful. Not because I can’t write it, but because starting from a skeleton and modifying it is faster than starting from a blank file every time. The generated code is usually sensible enough to be a good starting point.
Explaining unfamiliar code. I work with a fair bit of code I didn’t write — open source contracts, legacy services, other people’s ABIs. Pasting a function and asking “what does this do and what should I watch out for?” saves a lot of time jumping between documentation tabs.
Writing tests. I dislike writing tests for obvious-behaviour functions. AI is surprisingly good at generating unit test cases, including edge cases I might have glossed over. I still review everything but the coverage improves noticeably.
What Doesn’t Work Well
Anything bleeding-edge or niche. If you’re working with a protocol that launched six months ago, or a new L2 with limited public documentation, the AI will confidently give you outdated or hallucinated information. This is dangerous in Web3 where a wrong address or incorrect contract ABI loses real money. I always cross-reference with official docs for anything chain-specific.
Auditing smart contracts. I have seen developers paste their Solidity into an AI and treat the response as a security review. Please don’t do this. AI can catch obvious patterns but it misses subtle re-entrancy issues, business logic bugs, and protocol-specific edge cases. Use proper auditing tools and if it’s going to hold real value — get an actual audit.
Architecture decisions. Great for tactics, not great for strategy. Asking “how do I implement this function” works. Asking “how should I structure this entire system” tends to produce generic advice that doesn’t account for your specific constraints.
My Current Stack
I mostly use Claude for code explanations and Go/Solidity generation, and GitHub Copilot for autocomplete while I’m typing. They serve different purposes and work well together. I turn Copilot off sometimes when I’m in “deep thinking” mode because the autocomplete starts to feel like it’s steering my thoughts rather than following them.
The Bigger Picture
I think the honest answer is: AI tools make the boring parts of development faster, which frees up time for the parts that actually require thinking. For Web3 specifically, the “require thinking” parts are non-trivial — the security surface is large and the cost of mistakes is high. So my approach is to use AI for speed on the low-risk stuff, and stay cautious on anything that touches user funds or sensitive logic.
It’s a tool. A pretty useful one. But it’s not a replacement for understanding what you’re building.