Moses with the Tablets — by Dan Lynn
By Dan Lynn
When we built our first internet company, it was 1999 and the web was still new to most enterprises. Companies knew it mattered but had no idea how to use it to drive brand awareness, brand engagement, or sales. We'd figured a lot of that out, so when I sat across from a prospective client, they'd look at me like I was Moses coming down with the tablets — like I had all the answers.
I didn't, of course. Nobody did at that stage. But we'd done the work to be genuinely ahead, and that was enough. It happened again years later when we launched RelevantNoise, one of the first AI-powered social-media-mining companies. Same look across the table.
I'll be honest — it's one of the best feelings in business. Not because of ego, but because it means you've built something real, early enough to matter, and people trust you to guide them through it.
And here's what excites me now: that opportunity is wide open again with AI. Do the work, get genuinely ahead, and you can feel like Moses with the tablets.
Questions this Key Learning answers
How do I get ahead of customers without being too early?
The author’s experience suggests doing work customers know they need but have not yet learned to do. Our analysis adds that foresight must align with infrastructure, demand, and customer habits. The strongest position is slightly ahead of buyers: knowledgeable enough to reduce uncertainty, but close enough that customers can successfully adopt the product now.
How can I tell if the market is ready for my idea?
Our analysis distinguishes genuine readiness from admiration, curiosity, or press attention. The clearest test is actual adoption. Before launching, the relevant questions are whether the necessary infrastructure, customer habits, and proof already exist—and whether buyers can make a manageable change rather than being asked to leap too far into the future.
How do I become a trusted expert in a new technology?
Dan Lynn did not have every answer when the web was new, but he and his team had done enough work to be genuinely ahead. That gave clients confidence that he could guide them through uncertainty. His lesson for AI is similar: do the work, build something real early enough to matter, and earn trust through practical knowledge.