AI MVPs: The Illusion of Speed

In the AI-driven world, speed and innovation often outpace stability, longevity, and meaningful user connection. Building digital products with AI does carry real risks. From creating something that lacks soul to relying on a tool that might be discontinued in months, you're constantly navigating uncertainty.

This is especially true when it comes to building Minimum Viable Products (MVPs) with AI. Of course, the speed and ease of use of low-code and AI platforms dramatically reduce time-to-market. But while AI can help get something off the ground fast, it often does so on shaky ground.

Without careful attention to infrastructure, data quality, and maintainability, areas these platforms often overlook, AI-based MVPs rarely scale. These consequences go beyond technical inefficiencies: they can erode trust among users, developers, and stakeholders alike. In the long run, developing an MVP with AI can become not just risky, but deeply inefficient.

In this article, we will therefore look into the risks associated with MVP development using AI, from fragile infrastructure to long-term inefficiencies and trust issues. More importantly, we’ll explore how to build for scalability from day one, ensuring your AI MVP isn't just fast to launch, but also built to last.

The Hidden AI MVP Risks

The Danger of Skipping Architectural Planning

One of the most overlooked aspects of AI-powered MVP development is architectural planning. In the rush to get a product to market, teams often prioritize working functionality over foundational soundness. This mindset is amplified when using AI and low-code tools, which make it easy to create a prototype without ever thinking through how the underlying system is structured.

They encourage speed by abstracting away complexity, but in doing so, they also obscure the architectural decisions. Yet, skipping architectural planning when developing an AI MVP introduces significant risks that can undermine both the immediate success and long-term viability of your product.

When architecture is treated as an afterthought, the resulting product may look functional on the surface but be riddled with brittle dependencies, inconsistent data flows, or little to no modularity. These issues may not be immediately obvious during the MVP phase, especially when early users are few and usage is light, but they tend to surface quickly once the product starts gaining traction.

A well-thought-out architecture allows for scaling. Without one, the process becomes painful instead: adding new features introduces regressions; integrations can easily fail; and performance issues multiply. What feels like progress in the short term often leads to expensive rebuilds later.

AI can be a powerful accelerator, but it can’t replace thoughtful system design. Architecture doesn’t have to be complex or heavy-handed at the MVP stage, but it does need to exist. Even lightweight planning around data models, component separation, and future extensibility can make a significant difference later.

Fragile Infrastructure and Technical Shortcuts

AI and low-code platforms aim to simplify software development, which is great for speed, but problematic for structural integrity. The simplicity of generating code or dragging and dropping components frequently leads to solutions that work just enough to demo, but are weak at their core.

These tools often encourage quick fixes and shortcuts that lead to complex, brittle, and poorly documented code that’s hard to maintain and even harder to extend. The result is a foundation that can't bear the weight of growth.

Over time, these shortcuts accumulate as technical debt that will eventually slow down future development and increase costs, sometimes requiring a complete rebuild to address these foundational flaws.

The intentional structure and contextual awareness that seasoned engineers contribute to complex systems are frequently absent from AI-generated code. Although it might "work," it rarely complies with best practices for maintainability, testability, or readability. If no team is actively reviewing and reinforcing structure, the codebase soon turns into a "black box" that is costly to fix, hard to scale, and difficult to trust.

Lack of Ownership Over Code, Infrastructure, and Data

When creating an MVP with AI or low-code solutions, it’s easy to miss another very significant concern: ownership. Many of these platforms effectively abstract the underlying systems, resulting in a working product, but leaving you with little control over how it’s created, where it is hosted, or what happens to your data.

At the code level, outputs generated automatically can be challenging to audit, enhance, or transfer. Depending on the platform's licensing agreements or the proprietary nature of the AI models employed, you might not even truly "own" the code in a legal or practical way. This can be a real issue if you need to refactor, migrate, or scale.

The situation with infrastructure isn’t much more promising. With hosted platforms, you often find yourself tied to a particular vendor’s environment, tools, and workflows. Even if this can accelerate initial development, it restricts your flexibility and provides little insight into what’s happening behind the scenes. If that vendor changes their pricing, phases out a feature, or goes out of business, your product could suddenly be jeopardized.

Then there’s the issue of data. Numerous AI tools handle your input and training data in ways that aren’t always clear. Where is your data kept? Who can access it? What occurs if the AI model changes or is discontinued entirely? Without straightforward answers, it becomes challenging to adhere to compliance requirements or establish trust with users and stakeholders.

What you gain in speed, you lose in control. Without ownership, it’s like building your product on rented land: it can be fast to launch, but uncertain in the long run.

Long-Term Inefficiencies and Bottlenecks

The problems with AI-built MVPs often don’t show up until it’s too late. For instance, when you’re trying to scale, onboard new users, or add extra features. What started out as a fat and promising build soon turned into a maze of inefficiencies that deplete resources like time, money, and morale.

Performance is one of the first cracks that show up. While usually functional, AI-generated code isn’t optimized for scale. Slow load times, memory leaks, or bottlenecks under moderate traffic are part of what you might start to experience with your product. You're stuck retrofitting fixes rather than moving forward because the system wasn't built with performance in mind.

Debugging turns into yet another hassle. It gets harder and harder to track down bugs when your codebase is largely AI-generated (which usually means that it’s also undocumented). Instead of focusing on fixing real issues, developers spend more time attempting to understand what the system is doing. To make it worse, because of a tightly coupled or opaque logic, changes in one area often create other side effects elsewhere.

Making sure you’re compliant becomes another headache. AI platforms often don’t provide the granularity or visibility necessary to maintain compliance with regulations like GDPR, security audits, and data residency. Closing these gaps becomes more painful and costly the further you are from launch.

These issues compound over time. A system that lacks clarity, structure, and control becomes slower to develop, harder to secure, and more fragile with every iteration. The net result? A product that becomes more expensive to maintain the more successful it gets, which is exactly the opposite of what you want from your MVP.

Case Study: When It Worked Until It Didn’t

Because no-code platforms offer a faster and easier alternative to custom development, many startups use them to build their MVPs. While this approach works well initially, scaling often becomes a challenge. Most products eventually run into the same bottlenecks: performance limitations, restricted customization, integration hurdles, vendor lock-in, and mounting technical debt.

This is exactly what happened to the job search platform, Teal. To quickly build and iterate its product, Teal launched a no-code MVP using Bubble, Typeform, Airtable, and Zapier. This approach helped them to validate their idea and achieve product-market fit. But as their product's complexity increased and more people began using it, Teal ran into the usual no-code bottlenecks. They had to handle growing demand and feature requirements by adding more traditional development to their no-code stack to overcome these issues.

This is not an isolated case. Many startups find themselves in the same position. The MVP does its job validating the market and proving demand, but because it wasn’t built with longevity in mind, it slows them down at the exact moment they need to move faster.

How To Build For Scalability From the Start

AI and low-code tools hold out the promise of quicker feedback loops, lower upfront costs, and faster prototyping. However, long-term viability shouldn't be sacrificed for speed. Building a scalable MVP with AI is possible, but you must start with the right mindset and proper guidelines.

Think Architecture Early

Create a flexible basic architecture before moving on to features. Define where core logic should reside, how data flows, and which components may need to be replaced later, even if you're using low-code tools or AI-generated code. This forethought will help minimize rework as your product develops and grows.

Design for Handoff and Handoff Early

Imagine that someone else (perhaps your future self, a developer, or another team) will take over your MVP at some point. Ensure that your AI-generated or platform-created code is neatly organized, thoroughly documented, and decoupled whenever feasible. If the platform supports exporting or integrating with custom code, consider that option from the start.

Use AI Where It Adds Strategic Value

Not all parts of your MVP require AI. Use it strategically and for areas where it truly accelerates, like content generation, customer support prototypes, or predictive analytics. Essential features should be carefully reviewed or even custom-built to guarantee stability and ownership.

Own Your Data

Make sure you understand who has access to your data, where it is kept, and how it is stored. Use tools that allow you to manage your hosting and data schema. This will not only help with compliance but also make future migration or scaling easier.

Have an Exit Strategy from Day One

Opt for tools that offer a migration path. This could mean exporting code, transitioning databases, or eventually operating your own infrastructure. Always plan for the possibility of moving away from proprietary tools, even if you never actually need to. Try to avoid vendor lock-in as much as possible.

Invest in Observability Early

Often, in the rush to get things up and running, logging, performance monitoring, and error tracking are neglected. Fortunately, you can save countless hours later with even simple tools like Sentry, LogRocket, or basic analytics. They help detect weak areas before they evolve into significant roadblocks.

Build Fast, But Plan For The Future

There’s no denying it: with AI making building things so fast and easy, it’s tempting to fully rely on it. Being able to ship fast is sometimes necessary, but building quickly shouldn’t equate to building recklessly. The most effective MVPs are not merely those that are released quickly; they are the ones that are intentionally designed to grow over time.

By knowing the risks of AI when it comes to development, you can avoid falling into the trap of short-term wins that more often than not turn into long-term liabilities. With the right mindset, architecture, and tooling, building something fast and sustainable is possible.

At Miyagami, we help companies navigate this balance: moving quickly without compromising the foundations of scale, trust, and performance. If you're thinking about building an AI-powered MVP or scaling one that's already outgrown its early structure, get in touch. Let’s build something worth growing.