With AI making it easy to build impressive-looking prototypes, the risk of skipping validated learning is higher than ever. Eric Ries states the core purpose of an MVP—the learning—cannot be outsourced to AI and remains the bottleneck to innovation.

With AI, teams can create crude prototypes immediately after a customer call. This "build to learn" phase cheaply validates ideas. Only after confirming market need should teams shift to "build to earn," investing in scalable development. This strategy mitigates the risk of building unwanted products at high speed.

The primary AI bottleneck isn't idea generation, but validation. Feed customer research data (call transcripts, survey data) into an AI to create 'synthetic customers' that can give initial feedback on prototypes, quickly filtering out bad ideas before engaging real users.

The goal isn't to build one perfect prototype quickly. The real strategic advantage of AI tools is the ability to generate three or four distinct variations of a feature in a short time. This allows teams to explore a wider solution space and make better decisions after hands-on testing.

Rather than replacing customer interaction, AI facilitates a more continuous and iterative feedback loop. It allows for the rapid creation of virtual prototypes that can be shared with customers multiple times throughout development, ensuring the product stays on track.

Early demos shouldn't be used to ask, "Did we build the right thing?" Instead, present them to customers to test your core assumptions and ask, "Did we understand your problem correctly?" This reframes feedback, focusing on the root cause before investing heavily in a specific solution.

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.

Voice of the customer research is often insufficient. Adopt iterative innovation by quickly creating and demoing cheap prototypes—even computer simulations or animated concepts—to get constant, early feedback. This validates ideas in real-time.

Building a product too quickly with AI, without incremental user feedback, is like growing a tree indoors without wind. It appears fully formed but lacks the structural integrity and deep intuition gained from being exposed to real-world forces and user friction at each stage of growth.