Prototypes vs Products

Marty Cagan argues that a new wave of generative AI prototyping tools has been a good development for product discovery, but it has created confusion among product managers who fail to distinguish a prototype from a shippable product. Prototypes are built to learn, while products are built to earn, and the gap between them is large for customer-facing enterprise software. Real products carry dozens to thousands of use cases, complex business rules, and run-time demands like reliability, observability, scale, security, and compliance. Cagan notes that prototyping tools and professional code-generation tools serve different purposes, and that whether AI can close the gap from prototype to enterprise product remains an open question. For product creators, the practical point is to respect the difference and use the right tool for each activity.

01Key takeaways

• Treat prototypes as learning artifacts, not as near-final products, even when they look polished.
• Recognize that a working demo covers only a few use cases, while a real product needs far broader coverage.
• Plan for run-time requirements such as reliability, observability, security, compliance, and disaster recovery from the start.
• Be skeptical of vendor claims that a prototyping tool can produce enterprise-grade products.
• Use prototyping tools for discovery and professional build tools for delivery, matching each tool to its purpose.
• Collaborate closely with engineers so that the difference between discovery and delivery is understood by the whole team.

02Key sections

Prototyping is a discovery tool

New gen AI prototyping tools let product creators participate directly in shaping products, which Cagan sees as a very good trend for discovery. The risk is that the polish of a prototype misleads people about how much work remains.

Building to learn versus building to earn

Discovery aims to learn quickly, while delivery must produce a product customers can run their business on. Product managers without an engineering background are most likely to underestimate this difference.

Why products are far more complex

Simple prototypes usually cover a few use cases, while real products span many use cases and complex business logic. Enterprise solutions can involve thousands of use cases and intricate constraints.

Run-time demands of commercial products

Commercial products must be reliable, observable, performant at scale, multilingual, integrated, secure, compliant, and recoverable from disasters. Internal tools usually face lighter demands.

Tools and the open question

Prototyping tools and professional code-generation tools are used differently for different problems. Whether AI can go from prototype to enterprise product is uncertain, and Cagan notes that spoken language limits the specification process.

03From the post

“Note: This is part of the product creator series of articles, based on the overview article, The Era of the Product Creator. This series is intended for anyone that wants to create a successful product, whether or not the person has had professional training or experience in product management, product design, or engineering. In a... The post Prototypes vs Products…”

04Frameworks mentioned

Summary and takeaways written by PM Atlas; quotes are short excerpts. © the original author.