Moving from Web3 to AI: making the transition legible on a resume
Web3 experience translates into AI roles better than the market's current discount suggests — but only if the résumé does the translation instead of asking the reader to. Data work, incentive design, shipping under volatility, and operating real systems with real money on the line all carry directly. What doesn't carry is the vocabulary: a résumé that says "DeFi protocol" where it could say "payments infrastructure with adversarial users" is making the screener do a conversion they won't do. And the subject-years rule cuts both ways here: your analytics years are real analytics years — but AI-specific requirements (LLM APIs, evals, retrieval) start at whatever you've actually built, and honesty about that line is what makes the rest credible.
What carries, written so a screener sees it
- Data and analytics: exchange or on-chain analytics is high-volume, adversarial, real-time data work — name the volumes, the latency, the decisions your dashboards fed, and drop the token names into a parenthesis.
- Product under volatility: shipping through market cycles is genuine 0-to-1 evidence; write the user problem and the shipped mechanism, not the narrative arc.
- Systems with money on the line: incident response, risk controls, and monitoring translate directly to AI-safety-adjacent and infrastructure roles.
- Translate the nouns everywhere: 'wallet activation funnel' → 'user activation funnel'; 'validator operations' → 'distributed infrastructure operations'. Keep one Web3 line for honesty; translate the rest.
Where the line is
AI-specific requirements are new subjects, and pretending otherwise fails fast: "experience with evals" means you have built one, even a small one. The efficient move is making the line real, then moving it — a working project that uses an LLM API with retrieval and a basic eval, shipped where someone can use it, clears more AI-role gates than any course certificate, and it can be built in weeks, not years. Then the résumé states plainly: production data/product experience from Web3, hands-on AI work from these named projects. That structure — deep transferable base plus honest recent specifics — is exactly what "transitioning" screens are trying to find.
Making the bridge concrete
Run your translated résumé against real AI-role JDs and see which requirements actually gate you — subject by subject, not vibes. That diagnosis usually reshuffles the priority of what to build next. OfferCoach parses requirements into quantity and subject and refuses to blur the line between your transferable years and your AI-specific ones, which for career switchers is the honest map: it shows which roles you already clear and which need one more shipped project first. This page exists because its author walked the same path — the coaching is the method that worked, written down.