Crisp.ai's founder advocates for selling a product before it's built. His team secured over $100,000 from 30 customers using only a Figma sketch. This approach provides the strongest form of market validation, proving customer demand and significantly strengthening a startup's position when fundraising with VCs.
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Artist's co-founder warns that the biggest mistake founders make is building technology too early. Her team validated their text-based learning concept by manually texting early users, confirming the core hypothesis and user engagement before committing significant engineering resources.
Instead of guarding prototypes, build a library of high-fidelity, interactive demos and give sales and customer success teams free reign to show them to customers. This democratizes the feedback process, accelerates validation, and eliminates the engineering burden of creating one-off sales demos.
To test an idea like flavored creatine for women, use an AI image generator to create mockups. Post these images on Facebook Marketplace, a low-friction platform, to gauge interest via views, clicks, and messages before investing in product development. This provides quick, cheap data.
Product teams often fear showing prototypes because strong customer demand creates pressure. This mindset is flawed. Having customers eager to buy an unbuilt feature is a high-quality signal that validates your roadmap and is the best problem a product manager can have.
While the goal is to build a platform (second-order thinking), initial single-purpose app ideas (first-order) are critical. They serve as your "golden evaluation set"—a collection of core use cases that validate your platform is solving real user problems and is truly useful.
In AI, low prototyping costs and customer uncertainty make the traditional research-first PM model obsolete. The new approach is to build a prototype quickly, show it to customers to discover possibilities, and then iterate based on their reactions, effectively building the solution before the problem is fully defined.
A pre-product CRO conducts thousands of market conversations to validate demand and guide the product roadmap. This de-risks development by ensuring you build a product that customers will actually buy, a task more suited to a sales expert than a founder.
Historically, resource-intensive prototyping (requiring designers and tools like Figma) was reserved for major features. AI tools reduce prototype creation time to minutes, allowing PMs to de-risk even minor features with user testing and solution discovery, improving the entire product's success rate.
To truly validate their idea, Moonshot AI's founders deliberately sought negative feedback. This approach of "trying to get the no's" ensures honest market signals, helping them avoid the trap of false positive validation from contacts who are just being polite.
