Designing AI experiences in Figma is misleading because it only captures the ideal "golden path." Prototyping in code with live AI models is essential to understand and design for latency, errors, unexpected responses, and the true user "feel" of interacting with an unpredictable system.

AI coding tools let solo developers 'vibe code' impressive prototypes quickly, creating a false belief that they are production-ready. These projects often lack the robust architecture needed to scale, requiring expensive rewrites by 'God level' developers to fix the resulting spaghetti code.

The "vibe coding" trend, where non-technical staff use AI to rapidly build prototypes, is a legitimate accelerator for innovation. However, it's not yet a substitute for professional engineers when building scalable, mission-critical systems that are ready for deployment.

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.

Many companies market AI products based on compelling demos that are not yet viable at scale. This 'marketing overhang' creates a dangerous gap between customer expectations and the product's actual capabilities, risking trust and reputation. True AI products must be proven in production first.

The ease of building polished-looking applications with AI ("vibe coding") has become a problem for early-stage investors. It's now trivial to create a demo that looks impressive, making it difficult to discern which founding teams have built a real, defensible product versus a superficial facade.

Unlike traditional software, the core of an AI product is its dynamic, often unpredictable output. Static wireframes, even with placeholder text, are mere 'gargoyle rain spouts'—decoration that fails to represent the actual system. You can't validate an AI idea without building and testing the real, content-generating thing.

Resist the temptation to treat AI-generated prototype code as production-ready. Its purpose is discovery—validating ideas and user experiences. The code is not built to be scalable, maintainable, or robust. Let your engineering team translate the validated prototype into production-level code.

AI prototyping tools have broken the traditional link between visual fidelity and process maturity. Designers can now create highly realistic, functional prototypes on day one. This makes it challenging to signal to stakeholders that a concept is still early and exploratory, leading to feedback on pixels instead of strategy.