In my recent post on build vs buy in the age of vibe-coding, I argued that the classic binary is breaking down. Thanks to generative AI tools, teams now face a third option: AI-build. Instead of waiting for engineering capacity or relying entirely on vendors, product managers can prototype, test, and even wire together solutions themselves using natural language.
Marty Cagan just published a piece on build vs buy in the age of AI. He frames the same shift as user programming, rather than vibe-coding. The intent remains the same, though. Across the product world, practitioners are noticing the same tectonic shift. AI is turning what once required a software team into something accessible to anyone with intent and a clear prompt.
The rise of AI-build
Cagan makes the case that this new “user programming” is not just about speed of delivery. It is about changing the decision framework. For SaaS vendors, that means the competition is no longer just about feature breadth. As I argued in my earlier piece, SaaS products are under pressure to deliver leverage for vibe-coders, making their services easier to extend and customize through AI rather than waiting on roadmaps.
This is why AI-build deserves to stand alongside build and buy. It is a legitimate option for teams, not just a stopgap. When the problem is clear and the scope is well-bounded, vibe-coding can deliver faster insights and working prototypes than either traditional path.
The business rules challenge
Where Cagan is particularly insightful is in highlighting business rules as a natural limit to vibe-coding:
“The hard part is not the code, the hard part is the business rules. The real complexity in most systems is not in the algorithms but in the countless rules that govern pricing, entitlements, workflows, and compliance. This is why user programming has its limits.”
A product may have hundreds of subtle rules about pricing, entitlements, compliance, or workflows that aren’t written down in one place. Encoding and maintaining those rules is where complexity lives. A quick AI-generated script might deliver a prototype, but if it fails to respect business rules, it won’t survive in production.
This is a critical point—and one worth keeping an open mind about. My experience is that vibe-coding is powerful at the edges: testing ideas, automating simple flows, stitching together services. But it is less effective when deep institutional logic is required. At least for now, business rules remain the province of careful design, documentation, and human oversight.
Still, I wonder if this limit will shift. Already, research into AI-assisted orchestration and validation points to ways that large language models can help discover, codify, and even enforce business rules. It may be premature to assume AI will always be confined to the surface layer.
The product discovery is still the lynchpin
Cagan emphasizes that “the hard part is discovering the right solution to build, not coding it once you know what it is.”
AI will generate functioning code almost effortlessly, but unless the product team has already done the work of discovery, those outputs risk being clever detours rather than meaningful solutions. Delivery has been radically accelerated, but discovery remains the bottleneck.
Closing thought
Build vs buy is no longer binary. The third option, AI-build, is here. But the debate about business rules raises an important caution: vibe-coding may give us the freedom to build quickly, yet it also reminds us that the hardest part of software is not writing code but capturing the messy logic of the real world. Whether AI can help us bridge that gap is still an open question—and one worth exploring with both optimism and skepticism.