Pieter Levels ran the Vibe Jam of 2026 — 945 games submitted, 242,000 players, roughly 12 million views on X. It was the largest vibe-coding competition to date. And if you looked across those 945 submissions, a clear pattern emerged: most of them had the same code footprint.
Not because the builders weren’t skilled. Because they all started from the same place — AI-generated scaffolding, default templates, the same npm packages, the same folder structures, the same patterns for handling state, routing, and storage. The games looked different on the surface. Underneath, they were nearly identical.
That is not a problem for a game jam. For production software, it matters more than most builders initially realize.
timeline title From AI Scaffolding to Production Footprint Stage 1 : AI generates starter scaffold Stage 2 : Core features built on default structure Stage 3 : Scaling exposes template constraints Stage 4 : Architectural decisions get retrofitted Stage 5 : Distinctive footprint established or abandoned
What a Code Footprint Is
A code footprint is the underlying pattern of how a codebase is organized — the data model, the dependency graph, the integration architecture, the conventions for how different parts of the system communicate. It is not the features. It is the structure the features live inside.
When you vibe code from a default template, you inherit that template’s footprint. The assumptions baked into the template become the assumptions of your application. Most of the time, for simple projects, this doesn’t matter. The moment your project grows beyond the template’s design assumptions, you hit friction: the data model doesn’t fit, the routing structure doesn’t scale, the dependency graph creates security surface area you didn’t choose and didn’t notice.
The code you wrote is yours. The footprint you’re running it on might not reflect your actual requirements.
What the Template Problem Looks Like at Scale
The clearest version of this problem shows up when a project earns traction. A team vibe codes a working MVP with Cursor or Bolt, ships it, gets users, and then finds that extending the product requires working against the scaffolding structure rather than building on it. The original template was designed for the demo, not for what the demo became.
This pattern came up clearly at CloudVirga, where I worked as a solutions architect on a mortgage origination platform. The team had a predefined front-end framework with standardized components for every UI element — impressive reusability in principle. But every feature that fell slightly outside those predefined component boundaries required workarounds that accumulated over time. The framework’s footprint determined what was easy and what was hard, and it wasn’t always aligned with what the business needed to prioritize.
That observation led directly to the CleenUI framework I built subsequently — designed from the ground up around the 15 most common areas of real business software, rather than around generic component patterns. The point wasn’t more features. It was a footprint that matched how production business software actually needs to behave: consistent security and user management, structured content management, clean data access patterns.
Where Starter Templates Fall Short
AI-generated starter templates are designed to get you to a working demo as quickly as possible. That is a correct optimization for the beginning of a project. The costs emerge later:
Data model assumptions. The default schema the AI suggests is generic. When your business logic has specific access control requirements, multi-tenant data separation needs, or reporting queries that demand particular index structures, retrofitting those requirements into a generic schema is significantly harder than designing for them initially.
Dependency surface area. Templates include packages that are popular and general. Not all of them belong in your specific application. Each dependency is a maintenance obligation and a potential security exposure. Most vibe-coded projects inherit dependencies they don’t use and don’t audit.
Integration architecture. The way the template connects to external services — APIs, databases, authentication providers — is optimized for simplicity. Production applications often need audit trails, retry logic, failure handling, and rate-limit management that the template doesn’t address. Retrofitting reliability into an integration that wasn’t designed for it is rarely straightforward.
Building a Distinctive Footprint
The teams that separate themselves at the production stage are the ones that use AI to accelerate development without surrendering architectural judgment to the default template. The process isn’t complicated:
Before building, define what your application needs to do at scale. What are the access control requirements? What does the data model need to support for reporting and analytics down the line? What are the integration dependencies that need to be reliable, not just functional? Then use AI to implement against that definition.
The difference is 30 minutes of upfront thinking versus weeks of structural refactoring later. A distinctive code footprint also matters for companies that expect to raise funding, pursue acquisition, or work with technical due diligence teams. An acquirer looking at two similarly-featured products will find it easier to value the one whose codebase they can audit, extend, and operate without the original developers. Generic scaffolding that nobody fully owns is a diligence problem. A designed, documented architecture is a negotiating asset.
What 945 Submissions Reveal
The Vibe Jam results showed something encouraging about what can be built quickly with AI. They also showed that fast production and good production architecture are different things. Most of the games were built for the demo, and that was exactly right for the context.
Production software isn’t built for a demo. The footprint decisions made at the start determine what the product becomes — and in most cases, those decisions are being made by the AI’s default template rather than by the builders who are responsible for the outcome.
Changing that default is not technically difficult. It just requires the 30-minute conversation that most teams skip because they want to start building.
