The authors, Igor Omelianchuk and Andrew Lychuk, have spent decades modernizing legacy systems. Both experts completed 30+ projects deep, working with organizations where brittle code, broken workflows, and compliance debt are the daily reality.

Igor and Andrew have seen what bad architecture costs.

Now they’re watching AI-generated code create a version of the same problem, faster than most teams realize. Here’s what they think businesses need to understand about an AI-generated codebase.

AI-Generated Code: The New Development Reality

AI is making an incredible leap from being a mere programmer’s assistant to becoming an independent code generator. Moreover, AI programming has already become a standard part of software development, helping IT experts write, review, and optimize code faster than ever before. It fulfills tasks of different complexity, from creating boilerplate elements to suggesting complex algorithms. 

Ultimately, AI-powered code generation assistants dramatically change how modern software is built.

To realize how quickly the transformation is happening, let’s explore the AI adoption rates.

The AI penetration significantly benefits development teams. Routine coding tasks that previously took hours of manual work can now be completed in minutes. 

Andrew Lychuk illustrates: “current agents are pretty good with the boilerplate code and with the UI development. It usually takes a lot of time to create a perfect layout based on the design from the Figma. And the AI can convert it in a minute and developer just needs to make several tweaks until it gets a good look.”

This way, AI coding shifts the engineering focus from writing repetitive code to designing architectures, solving business problems, and validating complex logic. 

→ Companies get shorter development cycles, faster feature releases, and improved developer productivity.

However, higher development speed carries more than purely benefits. It also changes the economics of software engineering.

Generated code eventually becomes part of the application that, in turn, must be reviewed, tested, secured, documented, and supported. Therefore, by expediting the process of code creation, AI evokes a large pool of engineering responsibilities that follow.

Faster AI development extends engineering tasks

Generative AI can create APIs, database queries, UI components, configuration files, and even complete application modules. 

What AI lacks, however, is the understanding of broader system context, architectural decisions, business constraints, or long-term supportability.

As Igor Omelianchuk specifies, “Don’t expect from the AI agents to generate the perfect code because they will not do this until you give them the perfect context of what you’re trying to build. And if you don’t do this, assume you will have a big headache.”

As a result, engineering teams are facing the need to identify and fix the AI flaws, including misaligned coding patterns, redundant business logic, architectural deviations from established standards, and hidden security threats. 

These issues are deceptive because they may go unnoticed during the initial development stages. But they accumulate gradually and show up months later, with maintenance or expansion attempts.

AI code generation changes developer roles and sets new challenges

Someone may suppose that since AI writes code automatically, it reduces development times and costs. In reality, AI mainly changes the direction of the engineering effort.

While AI takes over code production, software engineers increasingly deal with code governance:

• validating AI-generated implementations; 
• reviewing architectural consistency; 
• performing additional testing and QA; 
• identifying security and compliance risks; 
• refactoring generated code to meet enterprise standards. 

Andrew Lychuk highlights the core focus of engineering work: 

“The main value of the developers is to solve the task, not to generate the code, because now code generation becomes cheaper. But the task solving and the maintenance in the long run becomes more expensive if we start with poorly developed and poorly reviewed code.”

For organizations that operate legacy systems, generative AI adoption brings additional challenges. AI code spurs new services much faster than enterprises can modernize the existing architecture to shape an adequate foundation. As new AI-produced elements interact with outdated applications, technical complexity even grows instead of shrinking.

This is where AI-powered application modernization can balance generative opportunities with the available resources and optimize productivity within the new role distribution. 

It transforms AI code from a source of technical debt into a scalable base for innovation. Companies establish a system where architecture is evolving, coding standards are kept, and existing applications are optimized. 

This way, organizations attain faster development without risking quality or inflicting unexpected issues or workloads.

Why AI-Generated Apps Often Fail at the Final Stage

There’s a pattern that Igor and Andrew see more and more. 

A customer builds an application using AI coding tools – Cursor, Copilot, or one of the growing number of vibe coding platforms.

The code runs, sometimes even looks good. Most features work. So they bring it to the development team with a demand to finish the last 5% and ship it. That 5% rarely gets finished.

The Illusion of 95% Complete Software

It’s not a new story. If you are a developer or have at least some tech background, you understand the pain that lies behind the situation.

“Clients turn to us with systems that are 95% complete,’ only for the engineering team to discover that the remaining 5% is structurally impossible without rebuilding what’s already there. What’s changed is the source. It used to be junior developers or misaligned teams. Now it’s AI agents,” says Andrew Lychuk.

This is not the fault of business owners. Naturally, they want their systems to launch as soon as possible and operate smoothly, especially since the systems often sit at the core of business processes. For them, it is a matter of speed, competitiveness, and cost optimization.

The growing number of founders bringing AI-generated code to development teams

The market pressure is different now. Founders are told they can launch products in days instead of months. Social media is full of “I built a SaaS with AI in a weekend” stories. Investors push for speed. Teams want lower development costs.

AI coding tools promise all three at once: faster delivery, fewer engineers, and lower budgets.

The problem is that speed in software is deceptive. Early development is the easiest and most visually rewarding phase. You can generate interfaces, APIs, dashboards, and workflows quickly. But the complexity appears when the product needs to evolve, scale, integrate, handle edge cases, survive failures, and support real operational load.

Why working demos are not production-ready systems

The illusion of completeness is convincing because working demos aren’t the same as production-ready systems. An application can look functional, handle basic flows, pass simple tests, render a polished UI, and still be completely unfit for real users, real load, and real business conditions. Production means handling failure gracefully. It means security that holds.

It means a codebase that another developer can actually read and extend. AI-generated code, as it stands today, rarely delivers that.

Yes, there’s no point in denying that tools for code generation are improving. A year ago, the output was noticeably worse. But improvement in output quality hasn’t solved the underlying problem: these systems are being built without the architectural thinking that makes software last.

The Architecture Problem Behind “Vibe Coding”

Software architecture is the foundation.

It’s not a metaphor; it’s the literal reason why some systems scale and others collapse under their own weight, and why software architects are among the highest-paid people in any technology organization. Establishing the right structure at the start is one of the few decisions that’s genuinely hard to undo. 

AI coding tools skip this step entirely.

How AI generates code without understanding the full system context

When a developer prompts an AI agent to build a feature, the agent responds to that prompt. Then the next prompt. Then the next. It has no model of the whole system. It doesn’t ask what the application will look like in two years, how many users it needs to support, or what integrations will eventually be required.

A human architect would ask all of those questions before writing a line of code. The AI starts writing immediately.

The result is code that works in isolation but doesn’t cohere as a system.

Each prompt adds another layer. Errors get “fixed” by adding more code on top, not by correcting the underlying logic.

Andrew describes what this looks like in practice: 

“The AI acknowledges a mistake, then generates another hundred lines rather than correcting the root cause. Over time, the codebase becomes a tangle of dependencies, dead code, and patches on patches exactly what developers call spaghetti code.” says Andrew Lychuk

The risks of weak architecture and technical debt

In some cases, 95% of the generated code is technically present but contributes nothing. It doesn’t break the application, but it doesn’t support it either. It just sits there, making everything harder to understand and change. 

This is what makes the final 5% impossible. You can’t complete a system you can’t understand, and you can’t understand a system that was never designed as one.

Think of it like building a house. If the foundation is weak, you can still build most of the house on top of it. From the outside, it may even look almost finished. But at some point, the structure becomes unstable: walls crack, the house feels unsafe, and finishing the last details will not solve the real problem.

The owner might think, “This construction company did a bad job. Let’s hire another team to finish it.”

But when the new team comes in, they quickly realize the issue is not with the final details. The foundation itself was built incorrectly. So instead of simply completing the house, they have to go back, inspect the base, undo a lot of previous work, and rebuild the structure properly.

The same thing happens with software. 

A product can look 95% complete, but if the architecture underneath is flawed, the remaining 5% may be impossible to finish without reworking the core of the system. 

The Hidden Cost of AI-Generated Code: Why IT Budgets Keep Growing

One of the biggest misconceptions about AI-driven code generation is that writing code faster equals lower spending on software development. 

AI reduces coding effort, but not software lifecycle costs

• While approximately 30% of modern enterprise code is AI-generated, according to Microsoft and Google,
• 100% of code still needs engineering support throughout its lifecycle.

Producing code in seconds, AI cannot fully understand architectural context, business goals, security/compliance policies, or forecasted maintenance needs. Although companies leverage AI to write code, it’s the job of human experts to check every component, validate it, document, integrate, control, and eventually modernize.

Therefore, every generated feature multiplies the engineering workload and, subsequently, costs after development.

“When we have simple tasks, we just have a smaller human review effort there. And when we have bigger tasks like feature integration tasks, like API in points development, like any other feature development, we just need to have bigger review effort there,” practically interprets Igor Omelianchuk.

Even a greater effort is required from organizations operating large legacy environments, where quickly developed modern services need to be integrated into old architectures. This may include redesigning dependencies, updating interfaces, and eliminating technical debt accumulated over the years.

Hidden AI costs extend beyond tokens

Only model subscriptions or API invoices don’t show the whole picture of the total AI investment.

Enterprises often underestimate expenses associated with infrastructure, orchestration, governance, monitoring, security, compliance, and human oversight. As AI adoption matures, indirect engineering costs also expand, often even exceeding the cost of generating the code itself.

More than 40% of agentic AI projects are forecasted to fade by the end of 2027, primarily because of escalating costs, vague business value, or inadequate risk controls.

What lies beneath the visible AI expenses:

These indirect costs continue to grow with the increasing AI code volumes, potentially creating new bottlenecks in software delivery.

How AI scales operational costs

Enterprise AI plans are not unlimited. Every prompt consumes tokens, and every AI-generated response has a respective computational cost. 

How it works: 

• When a single developer asks an AI assistant to explain an error message, it consumes a few thousand tokens. 
• When hundreds of engineers submit dozens of requests every day, monthly consumption may reach billions of tokens.

 Uber shares a practical experiment, when the company encouraged thousands of engineers to actively use AI coding assistants. Developers naturally selected the most capable models that were also the most expensive ones.

As a result, the company’s annual AI budget was exhausted within only a few months. 

The root cause lay not in the excessive AI usage, but in the absence of centralized governance and architectural control over the exploitation of AI resources.

Continuous modernization optimizes AI software economics

The challenge, therefore, is not an AI-powered code generator itself. It is managing the expanding volume of software that AI produces.

The faster organizations generate code, the faster they accumulate technical debt, and the more effort is gradually required to keep it under control. 

Andrew Lychuk explains: 

“If you do not follow consistent development practice with the AI tools, they will produce a lot of different functions that do same thing, for instance.”

Engineering standards should evolve accordingly, making AI-powered application modernization a continuous practice, not a one-time migration or refactoring.

Competent AI modernization helps organizations address duplicate business logic, inconsistent coding practices, outdated dependencies, and misaligned architectures. This enables companies to minimize the costs of software support, scaling, and security implementation. 

Ultimately, the foundation for future profitability lies not in the quantity of the generated AI code but in the capability to continuously govern, modernize, and optimize it. Modernization helps allocate resources efficiently, prevents inflation of maintenance costs, and ensures that rapidly expanding applications retain their business value over time.

When Modernization Turns Into a Full Rebuild

When a business brings AI-generated software to Igor and Andrew’s team, the first job is not to give timelines; it is to assess the system properly.

Before anyone adds a feature or fixes a bug, the team has to understand what’s actually there:

• which code is functional, 
• which is dead weight, 
• what the architecture looks like, 
• and whether the system can be extended at all. 

Most systems built on legacy frameworks depend heavily on scarce expertise and undocumented logic that lies in the heads of key seniors. The system is getting harder and harder to read, understand, and more expensive to maintain.

Signs that software cannot be expanded anymore

The clearest signal that software can’t be saved in the way it is now is when it can no longer scale as your business does. The business wants to add a new integration, support more users, or introduce a new workflow, and the system simply won’t accommodate it.

The root of the problem lies not in the feature’s complexity. It lies on the foundation that does not allow for growth. The walls have been built without the structural capacity to add another floor.

“There is a particular reason why software architects are one of the most highly paid positions in the software company, because establishing a proper architecture of the software product is basically the first path to success from technical perspective. And if it’s not established properly, or if it’s not really taken into account with regards to the big vision of the project with the plans for further development, plans for further expansion of the project, you can get into a situation where the code cannot be expanded,” mentions Igor Omelianchuk

Why some systems require rewriting instead of fixing

The worst is that technical debt eventually becomes business debt. At first, you notice slower feature development, more bugs, and increasing developer frustration. Later, the launches get delayed, integrations fail, and teams burn out as they spend more time maintaining systems than improving them.

At that point, the honest conversation may be about starting from scratch rather than modernizing what already exists. What Igor and Andrew agree on is that in most cases, the full rebuild doesn’t happen because the existing AI code is worthless, but because the cost of working around the limitations exceeds the cost of rebuilding it correctly.

Some systems can be modernized incrementally. Others need to be rewritten from scratch. 

The role of software audits and technical reviews

The only way to know if your system needs any technical intervention is to perform a detailed review of the software. 

Igor and Andrew’s team performs either a complex technical audit, which takes 2-3 weeks, or a basic code review to identify the current state of a system and evaluate its readiness and necessity for modernization.

In both cases, you will get an independent expert view of how your system works and what may be holding it back from scaling. You will also get a step-by-step modernization plan if needed.

Unfortunately, audits and code reviews are underused. Founders often treat them as an admission of failure rather than a standard business practice. But prevention is better than cure, they say. 

A code review before committing to a development roadmap is the same logic as a building inspection before buying a property.

It tells you what you actually have, not what you assumed you had, and whether what you have aligns with your business goals. It’s the information you need to make a reasonable decision, whether to continue with what exists, start fresh, or make no change at all.

AI-Generated Code and Intellectual Property Concerns

Another disputative question is the ownership of AI-generated code. The legal frameworks around the ownership of AI-generated code are still being written, so the question has not been fully resolved yet.

The difference between business logic and technical implementation

The standard assumption is that code is intellectual property. A company owns what it builds, whether that code was written by an engineer or generated by an AI. But the question of whether AI-generated code constitutes a know-how, something genuinely novel, something proprietary, is more complicated.

Andrew draws a useful distinction. Business logic can absolutely be a know-how. The decisions a company makes about how to structure its processes, what problem to solve, and how to solve it in a way that creates value are proprietary. But the technical implementation of the decisions, when generated by a large language model (LLM), is a different matter.

“We should understand that companies that create LLM, teach LLMs on some existing text. So everything you see, generated by any model, was already created by someone, and it’s just a replica or copy of someone’s implementation. So that’s why the technical implementation of the business ideas, from my point of view, can’t be really considered as a know-how,” says Andrew Lychuk

Can AI-generated solutions be considered unique?

LLMs are trained on existing information. Everything they produce is a pattern drawn from what humans have already built. There is no invention in the output, only recombination. Which means that two companies working in the same domain, using the same AI tools, could end up with nearly identical implementations, because the AI recognized the same pattern in both cases. 

The developers who worked on them could be either accused of using the other project’s code, approach, or some kind of solution. But in fact, this may not be a theft.

Again, AI tools work with what has already been invented, and similar patterns may be traced in companies not related to each other or, on the contrary, in the same domain.

Igor points to a practical implication: unique formulas, proprietary financial models, and original business frameworks are protected. The scaffolding around them, the server architecture, and the UI that an AI generates to run the application are closer to common knowledge than intellectual property.

The Future of AI-Assisted Development: Why AI Tools Will Continue Evolving

No matter how much one could advocate for human engineers, AI coding tools will keep getting better, and the economics demand it. Every major technology company is investing heavily in code generation, and the competitive pressure to ship faster, with smaller teams and lower costs, isn’t going away.

The output quality improves with each generation, and the range of tasks these tools can handle expands consistently. Context windows are growing, models are getting better at reasoning across larger codebases, and the gap between AI-generated and manually written code is closing. That trajectory continues regardless of current limitations.

The growing importance of software architects and code reviews

As more code gets generated faster, the ability to evaluate that code, assess its architecture, identify its weaknesses, and determine whether it’s fit for production becomes more valuable.

Software architects and senior engineers aren’t made obsolete by AI. They become the quality control layer that AI currently can’t replace.

The same is true of code reviews and technical audits: as AI-generated codebases proliferate, the demand for qualified humans to assess them will grow.

What founders should understand before relying on AI-generated products

What founders should understand is that AI tools are useful for exploration, prototyping, and accelerating early development. Regardless of how tempting it may be to delegate a large part of code production to AI tools, they are not a substitute for the architectural thinking that humans have.

A demo built with AI is a starting point, not a finished product.

Before investing significantly in a codebase, whether AI-generated or otherwise, get it reviewed. Understand what you have. Know whether it can scale, whether it’s secure, and whether it can support the business you’re trying to build. The cost of not having it tends to show up later, and it tends to be higher than expected.