Most people who worked in Web3 marketing spent the last two years trying to forget about it.
I did the opposite. I kept a document called "what actually worked" — a running list of the things that moved markets in one of the hardest marketing environments I have ever operated in.
Reading it back now, almost everything on that list applies directly to AI.
Web3 audiences were unusually difficult to market to, for reasons that felt specific to crypto at the time but turned out to be early signals of where all technically sophisticated markets were heading.
They were fast. Misinformation spread in hours. Weak claims got stress-tested publicly and immediately. If you said something that didn't hold up, someone in the community would find it and share the correction before your post had finished indexing.
They were skeptical by default. Years of rug pulls, failed promises, and projects that overpromised and underdelivered had created an audience that assumed bad faith until proven otherwise. Trust had to be earned, not assumed.
They were technically literate. You couldn't get away with hand-waving the how. People wanted to understand the mechanism, not just the outcome. Vague claims about "the future of finance" landed with a thud. Specific claims about how the protocol actually worked landed much better.
Each of those characteristics describes the AI buyer market in 2025 almost exactly.
Enterprise buyers who have already sat through fifteen AI demos this quarter. CTOs who have been burned by tools that worked in the pilot and fell apart at scale. Technical founders who know enough to spot the gaps in your pitch.
The marketing approaches that failed in Web3 — broad vision statements, vague capability claims, demo-first positioning — are failing in AI for identical reasons.
The things that worked in Web3 translate almost directly.
Specificity over generality. The protocols that survived were the ones that could explain exactly what they did and exactly who it was for. Not "we're building the future of finance" — "we let small developers deploy composable lending contracts without managing liquidity themselves."
Community before product. The projects that built genuine communities of people who understood and believed in the technology before the product launched had a massive distribution advantage. In AI, the equivalent is building an audience of people who care about the problem before you announce the solution.
Earned trust compounds. In Web3, the founders and builders who showed up consistently — who were honest about failures, who engaged with criticism, who demonstrated competence over time — built asymmetric trust. One post from a trusted voice moved more than a hundred posts from an unknown one.
I spent years learning how to market in an environment where the audience was sophisticated, skeptical, and fast. That turned out to be preparation, not just experience.
The AI market is harder than most founders think. It is also more knowable than it appears — if you've seen this pattern before.