Why Your AI-Built MVP Will Need to Be Rebuilt And How to Avoid That Fate

Chris Loy, a former CTO, recently articulated why this happens. He describes how AI coding tools deliver code at remarkable speed but without the comprehension that human developers build as they work. "Most of the time will be spent on post hoc understanding of what code the AI has written," he writes. The marketing claims of 10x faster development collide with reality, where measured productivity gains are closer to 10%.

This gap between promise and reality is not a failure of the tools. It is a fundamental misunderstanding of what the tools are good at and what they are not. Understanding this distinction is essential for any founder building with AI assistance today.

What Is the Difference Between Vibe Coding and Sustainable Development?

Loy introduces a useful distinction between two approaches to AI-assisted development. "Vibe coding" means moving fast, letting AI generate code with minimal oversight, prioritizing delivery speed over comprehension. "AI-driven engineering" means employing best practices, ensuring humans understand what the AI produces, moving deliberately to keep development sustainable.

For simple projects and throwaway prototypes, vibe coding works beautifully. The AI generates code, the code runs, the product ships. The simplicity means there is no accumulated complexity to manage.

But as projects grow, something changes. Features start interacting in unexpected ways. Changes in one area break functionality in another. The AI, which has no persistent memory of the codebase and can only hold limited context at once, starts producing code that conflicts with existing patterns.

When Electrosmart needed to move from spreadsheet chaos to automated arbitrage, the architectural decisions we made early determined whether the system could scale. AI tools could generate pricing algorithms quickly, but they couldn't architect systems that would handle growing transaction volumes or safely add new pricing strategies later. Senior Product Owner Paweł Dudek reflected: "Digital Bunch didn't just build us software; they changed how we think about our business."

This is the vibe coding wall. You hit it not when AI tools fail, but when accumulated complexity exceeds what can be managed without genuine understanding. And by the time you hit it, you have often accumulated so much technical debt that the path forward is unclear.

How do you know if you have hit or are approaching the vibe coding wall? After watching dozens of projects struggle with this, we've identified characteristic symptoms.

Why Do Timelines Become Unpredictable?

The first symptom is unpredictable timelines. Early in development, you could estimate how long features would take and be roughly correct. Now estimates are consistently wrong, usually by large margins. Simple-sounding changes turn into multi-day efforts. The AI generates solutions that break other things, requiring cascading fixes.

What Does Fear of the Codebase Mean?

The second symptom is fear of the codebase. You or your developers have started avoiding certain parts of the code. There are files nobody wants to touch because changes there tend to cause problems elsewhere. The architecture has become a minefield where each step might trigger an explosion.

When we took over Cortex from a previous development attempt, this fear was palpable. The construction drawing management system worked for basic cases but nobody could safely add features. The architectural foundation needed rebuilding before the platform could handle the complexity of real construction workflows.

Why Do AI Tools Become Less Helpful Over Time?

The third symptom is AI tool degradation. The AI assistants that once seemed magical have become less helpful. They generate code that does not fit existing patterns. Their suggestions introduce inconsistencies. You spend more time fixing AI output than you save using it. This happens because the codebase has grown beyond what the AI can hold in context, so it is effectively working blind.

What Causes Debugging Paralysis?

The fourth symptom is debugging paralysis. When bugs appear, nobody can quickly identify the cause. Debugging sessions stretch into hours or days. The code does things for reasons nobody remembers deciding. Comments are sparse or misleading. The path from symptom to cause has become too tangled to follow.

How Do Workarounds Compound Problems?

The fifth symptom is mounting workarounds. Rather than fixing underlying issues, the team has started routing around them. Special cases proliferate. Configuration becomes complex. Each workaround makes the next problem harder to solve properly.

If several of these symptoms sound familiar, you have likely hit the wall. The question becomes: what now?

To understand the path forward, it helps to understand why AI tools produce this outcome so reliably.

Loy frames it precisely: AI coding agents are "lightning fast junior engineers" with two crucial differences from human juniors. First, they work far faster, unconstrained by thinking or typing speed. Second, they have no capacity to learn or accumulate understanding of your specific codebase and domain.

How Does Human Understanding Differ from AI Context?

A human junior developer, working on your codebase over months, builds mental models of how things fit together. They learn conventions, understand the history of decisions, develop intuition about where problems lurk. This understanding makes them increasingly effective over time.

An AI tool has no such accumulation. Each session starts fresh. The AI might have access to your code through context windows, but it does not understand your code the way a human who has worked with it does. It cannot hold the full system in mind. It does not know why decisions were made, only what the current state is.

When C&R Software needed to modernize 40 years of legacy systems, the challenge was not generating new code. It was understanding 40 years of business logic, domain knowledge, and architectural decisions embedded in that legacy. CEO Ed Wallen noted: "Digital Bunch understood that our challenge wasn't the technology. It was explaining it." AI tools can't build that understanding through context alone.

This means AI tools optimize for local correctness rather than global coherence. Given a task, they produce code that accomplishes that task. But they do not consider how that code fits with broader architecture. Over time, these local optimizations accumulate into global incoherence.

When you have hit the wall, you face a decision. Do you try to rehabilitate the existing codebase, or do you rebuild from scratch? This is not an easy choice, and the right answer depends on circumstances.

When Does Rehabilitation Make Sense?

Rehabilitation makes sense when the core architecture is sound but the implementation has become messy. If the fundamental structure of your application makes sense and the problems are primarily about code quality, consistency, and accumulated shortcuts, you can potentially clean things up without starting over.

This typically involves establishing clear patterns, refactoring incrementally through our DevOps and infrastructure approach, adding tests to catch regressions, and gradually bringing coherence to the chaos.

When Is Rebuilding the Better Path?

Rebuilding makes sense when the architecture itself is the problem. If your data models do not reflect your actual domain, if your core abstractions are wrong, if fundamental assumptions baked into the code no longer match your product direction, rehabilitation will be fighting uphill indefinitely.

A few signals suggest rebuilding is better. If you find yourself saying "we cannot do X because of how Y works" frequently, where X is something your product needs and Y is a core architectural decision, that suggests architectural problems. If the cost of every new feature is dominated by working around existing structure rather than building the feature itself, that suggests the structure is actively harmful.

When Premier Construction Software needed platform transformation, we evaluated rehabilitation versus rebuild. The decision to rebuild parts while preserving domain logic meant we could deliver in three months what had taken years originally. CEO Karoline Lapko reflected: "We're very happy with the outcomes. Digital Bunch is creative, responsive, engaged and passionate about what they do."

What Value Does the Old Code Still Provide?

One common mistake is assuming rebuilding means losing everything. A codebase that does not work well as a production system may still be valuable as a specification. It shows what the product does, how users interact with it, what edge cases exist. A rebuild informed by a working system is much faster than building from scratch, because most hard questions about product requirements have already been answered.

Whether you rehabilitate or rebuild, the path forward requires something that vibe coding lacked: deliberate architecture and sustained comprehension.

Loy argues that working effectively with AI tools requires treating them as tech leads treat talented but unpredictable junior engineers. You do not let them run unsupervised. You establish practices, standards, and processes that convert their raw speed into sustainable output.

What Architectural Practices Prevent Future Problems?

For a codebase already in trouble, or to prevent trouble in new projects, several practices are essential.

First, establish architectural clarity before writing more code. Document the intended structure of the system. Define boundaries between components. Specify patterns that should be used. This documentation serves as reference both for human developers and for AI tools, which can use it as context.

Our digital strategy process starts here because architectural decisions have 10x to 100x leverage over implementation speed.

Second, add tests to create a safety net. Tests serve multiple purposes: they verify correctness, they document expected behavior, and they catch regressions when changes are made. For a troubled codebase, adding tests to critical paths is often the first priority, because it enables subsequent changes to be made with confidence.

How Should You Use AI Tools More Effectively?

Third, use AI tools more carefully. This does not mean abandoning them. It means providing better context, reviewing output more thoroughly, ensuring generated code fits with established patterns. AI tools remain valuable accelerators, but they need human oversight to avoid recreating problems.

When Valley Insurance Associates needed their custom CRM, AI could have generated generic insurance software quickly. But the value came from careful architecture that understood their specific compliance requirements and filing workflows. CEO Gina Doyle's reflection, "Best investment we've made," came from sustainable systems, not fast code generation.

For founders who have not yet hit the wall, or who are starting new projects, the lesson is clear: vibe coding is fine for prototypes but dangerous for products.

What Purpose Does Each Serve?

The distinction matters because the purpose of a prototype is different from the purpose of a product. A prototype exists to learn: to test assumptions, to get user feedback, to explore possibilities. Speed matters more than sustainability because the prototype will be thrown away once it has served its learning purpose.

A product exists to serve users over time. It will need new features, bug fixes, performance improvements, security updates. It will need to evolve as you learn more about users and market. Sustainability matters because you will live with the codebase for years.

The trap is that AI tools make it easy to blur this distinction. The prototype works so well that you keep building on it. The velocity feels too good to sacrifice. By the time you realize you should have started over with proper foundations, you have users and revenue and cannot afford the downtime.

When Opus Platform launched their hiring platform, the architectural foundation had to support growth from day one. CEO Naif Alwehaiby noted: "Digital Bunch accomplished in three months what we had been struggling with for years." That came from treating it as a product, not a prototype, from the start.

What Practices Make the Difference?

The solution is to be honest with yourself about which phase you are in. If you are prototyping, vibe code freely and plan to throw it away. If you are building a product, invest in architecture, documentation, and testing from the start. The initial slowdown pays dividends as complexity grows.

Loy suggests specific practices: specification and documentation before coding, modular design to control context scope, test-driven development to guide implementation, coding standards enforced through context engineering. These practices feel slower than vibe coding because they are slower. They are also the difference between a product that scales and one that collapses under its own weight.

Many founders in this situation consider bringing in outside help, whether hiring senior developers or engaging an agency. This can be the right choice, but it requires clear thinking about what you actually need.

When Does Adding More Capacity Not Help?

If your problem is capacity, adding more people who will work the same way will not help. More engineers vibe coding will produce more tangled code faster. You will hit the wall sooner, not later.

If your problem is expertise, the right help can transform the situation. Experienced engineers can look at a troubled codebase and quickly identify whether rehabilitation or rebuilding is appropriate. They can establish architectural foundations that enable sustainable development through our full-stack development approach.

What Questions Reveal Development Partner Quality?

The key question to ask any potential development partner is how they work, not just what they have built. Do they emphasize planning and architecture through UX research and strategic planning, or do they pride themselves on moving fast? Do they document decisions and establish patterns, or do they figure things out as they go?

For AI-assisted development specifically, ask how they incorporate AI tools into their workflow. Do they use AI to accelerate well-structured development, or do they rely on AI to figure things out? The answer reveals whether they will help you escape the vibe coding trap or dig you deeper into it.

The AI coding trap is ultimately a specific instance of a general pattern: tools that make something easier often make doing it badly easier too. Word processors made writing easier and also made producing mediocre documents easier. AI coding tools make producing code easier and also make producing unmaintainable code easier.

The response to this pattern is not to reject the tools but to develop discipline in using them. AI coding tools are genuinely valuable when paired with sound software engineering discipline.

The founders who will build the best products with AI assistance are those who understand that the tools accelerate execution but do not replace judgment. They will use AI to move faster within a framework of deliberate architectural decisions. They will maintain human understanding of their systems even as AI generates much of the code.

For those who have already hit the wall, the path forward is harder but not hopeless. With clear assessment of the situation, honest evaluation of rebuild versus rehabilitate, and commitment to sustainable practices going forward, a troubled codebase can become a solid foundation. The lesson, expensive as it may have been, will serve you for every product you build thereafter.