Community-driven AI product discovery
Dave Rogenmoser, CEO and co-founder of Jasper, on the generative AI opportunity
Jasper’s community mattered because it was not just a growth channel, it was the product discovery engine in a market where features were easy to copy. The company grew by teaching marketers how to use AI for specific jobs like ads, blog posts, and social copy, then watching what those users wanted next. That let Jasper shape prompts, templates, workflows, and eventually broader product direction around real usage instead of internal guesswork.
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Jasper’s early users were marketers from the founders’ existing audience, and the community scaled training far beyond what the company could do itself. In practice, that meant power users were showing others how to get useful outputs, which created both faster onboarding and a steady stream of product feedback.
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This mattered more in AI writing than in normal SaaS because the underlying models were widely available. Jasper and Copy.ai were both built on top of GPT-3, so the edge came from packaging, workflow design, and fast iteration on what users actually found helpful, not from owning a unique model.
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The strongest comparable is Copy.ai, which built a similar feedback loop from a large user base, tracking what people saved, copied, and rewrote to improve models and experiments. Across both companies, user behavior became training data for better outputs and a guide for where the product should expand next.
Going forward, the winners in AI applications will keep turning user communities into workflow data, model tuning, and faster product decisions. That is how a simple writing tool becomes a deeper system of record for how marketing teams create content, and later how enterprise teams automate larger parts of go to market work.