Has the programming language finally become human?

Today, AI generates code in seconds. Interfaces, APIs, integrations, and even application architectures now appear almost instantly after you enter a well-written prompt. It sounds like a breakthrough for many businesses: faster delivery, lower development costs, swift prototyping, and minimized modernization cycles.

And it really is, to some extent.

AI-assisted programming plays an important role in software modernization and product engineering, giving rise to the concept of “vibe coding.” It entered the scene in 2025, simplifying software development to a natural language prompt.

So, has coding really become easy, convenient, and fast, without requiring years of learning and practice? Do AI-generated software products bring the same business value as the ones created by human engineers?

The acknowledged IT experts, Igor Omelianchuk, Andrew Lychuk and Yuriy Diachenko, discuss the strengths and weaknesses of vibe coding and how it can be applied to optimize programming without introducing unwanted risks.

What Is Vibe Coding And Why Is Everyone Talking About It?

“Vibe coding” is a popular term for an intuitive, fast, AI-assisted programming where developers focus more on the idea and desired outcome than on meticulous code composition.

In other words, it means describing functionality to AI tools in “human” language and getting a quick result.

The rise of AI coding tools

AI-generated development has rapidly evolved from a trending experiment into a standard part of modern software engineering. Teams actively use AI tools, including GitHub Copilot, Cursor, or Replit AI, to generate code, build interfaces, write tests, analyze requirements, and expedite project discovery.

Yuriy Diachenko summarizes: “Most development and modernization operations are now facilitated by AI agents.”

The favors are obvious:

• Faster delivery
• Lower entry barriers
• Rapid prototyping
• Quicker MVP launches
• Reduced manual work

Modern AI tools already handle a large amount of repetitive work, especially:

• UI and layout generation
• Boilerplate code
• Test generation
• Foundational infrastructure setup
• Requirement analysis and discovery

Why AI-generated development became mainstream

For startups, product owners, and even entrepreneurs without a technical background, AI coding tools open enticing possibilities.

As Igor Omelianchuk notes, “modern platforms already allow teams to create a real prototype in a matter of days” and quickly test ideas and processes.

Businesses also tend toward the “easy path,” increasingly expecting IT teams to use AI-enabled workflows to expedite delivery and reduce costs. Demos, almost instantly prepared by AI, seem a convenient shortcut to traditional development.

However, like every breakthrough technology, vibe coding has its flip side.

Experts warn about creating unrealistic expectations:

“It seems like AI can do anything, but we are not sure how it will do it,” says Igor Omelianchuk.

Demo-ready vs production-ready software

To understand the possibilities and limitations of AI coding, let’s highlight the difference between a prototype that can be almost instantly built by AI and production software.

A prototype just demonstrates functionality under pre-established conditions.

Production-ready software must sustain:

• Maintainability
• Scalability
• Security
• Adaptability to future changes
• Stability under altering loads

AI successfully generates features that work “here and now.” But without proper architecture, documentation, and human validation, these solutions often become difficult to extend and expensive to support in the long term.

Both experts emphasize that while AI agents create code fast, speed alone does not guarantee a resilient product. Demo versions created by AI today may still require substantial refactoring, restructuring, and engineering review before they can survive in the real world tomorrow.

AI Is A Tool – Not A Replacement For Engineers

So, is it possible that AI replaces software engineers?

In fact, this is one of the biggest misconceptions about AI-generated development.

Experienced IT teams recognize that AI is not an autonomous developer, but a powerful productivity tool that still depends heavily on human expertise.

Writing code vs. solving the engineering problem

Yuriy Diachenko puts it straight: “The main value of developers is to solve the task, not to generate the code.”

While code generation becomes cheaper, task solving and long-term maintenance become more expensive if you start with poorly developed and poorly reviewed code.

When software engineers start the project, they focus on a specific goal.

Developers ask multiple questions to extract as much knowledge about the project as possible and to reveal the main pain. Only with detailed and structured information can they allow AI to generate some code.

It’s human judgment and experience that allows for addressing the engineering problem and applying AI tools the proper way.

Why AI-generated code always requires review

Even when AI produces technically functional output, it may still contain hidden risks:

• Duplicated logic
• Inconsistent architecture
• Inability to scale
• Poor maintainability
• Security or integration issues
• Low flexibility for edge-case handling

Igor Omeianchuk explains: “Anything that is generated by the AI needs to be verified by a human.”

Software developers prioritize scalable architecture, guarantee long-term stability, and prevent technical debt. AI, in turn, should be integrated into a structured engineering process with code reviews, pull requests, testing, and architectural oversight.

Why senior developers use AI more effectively

The unexpected paradox:

• AI often amplifies the developer skills gap instead of eliminating it.

At the dawn of AI agents, they were assumed to quickly elevate junior developers to senior-level productivity.

In practice, experienced engineers usually achieve much better results with AI tools because they know how to evaluate, challenge, and refine the generated output.

This happens because senior developers understand:

• How systems behave under load
• Which architectures are scalable
• Where technical debt may appear
• How the system responds to change

While most junior developers primarily focus on whether the code “works right now,” experienced engineers envisage future evolution.

The “one prompt” misconception

“AI does not automatically understand the business context, long-term product goals, or operational complexity behind a system,” says Yuriy Diachenko.

If not instructed properly, AI will fill knowledge gaps with assumptions based on patterns it has previously seen.

The resulting product may work, but become fragile, inconsistent, or difficult to support later.

Although the idea of having a perfect solution built by AI with a single line of prompt seems tempting, it’s another misconception.

AI can dramatically accelerate development. But without experienced engineers in the loop, faster generation can simply lead to faster accumulation of technical debt.

The Hidden Problem: Technical Debt Starts On Day One

One of the biggest risks of AI-enabled coding is that technical debt can appear almost immediately.

AI-generated applications often look clean in demos and may even pass tests or static analysis checks. Still, beneath, they may contain architectural inconsistencies, duplicated logic, and scalability issues that become serious problems later.

Yuriy Diachenko exemplifies: “We had a project where every API endpoint was implemented differently because the AI generated each feature separately instead of following a unified architectural pattern.”

The clean architecture, however, would provide transparent layers, minimize code duplication, and use only several patterns.

Igor Omelianchuk comments: “It would be helpful if all the decisions and all the prompts taking place during the development were loaded into the memory base to give the AI agents the context to understand what they are building.”

“Working” does not mean maintainable

Yuriy Diachenko specifies why long-term maintainability can become challenging: “AI-built code often introduces windows to be implemented on later stages,” meaning invisible technical gaps that may not affect the product immediately but create growing complexity over time.

So, while a less experienced developer may see just a functional output, an experienced engineer looks for:

• Scalability bottlenecks
• Future flexibility
• Edge-case behavior
• Performance under load
• Long-term maintainability

Why maintenance becomes expensive

The problem is not that AI writes bad code.

Rapidly generated systems may lack architectural consistency or strategic planning.

Products evolve, and those hidden weaknesses show up:

• Adding new features becomes slower
• Refactoring becomes unavoidable
• Bugs appear in unexpected places
• Scaling requires partial rewrites

As a result, many AI-composed MVPs eventually require substantial restructuring before they can handle sustainable growth.

Fast generation can accelerate development, but without a well-organized engineering process, it can accelerate technical debt, leading to expensive and resource-consuming rework.

Why AI-generated Products Become Hard To Scale

Most AI-generated products fail not during launch, but during growth.

The first version may impress investors, validate an idea, and even attract users. Problems usually begin when the product needs to evolve.

AI builds features. Engineers build solutions

AI is undeniably efficient in generating isolated functionality. But to craft a scalable product, you need strategic architectural thinking:

• How future modules will connect
• How integrations will sustain higher load
• How data travels throughout the system
• How new features will influence existing logic

Junior developers, as well as AI, can disregard this foundation. Without it, system complexity accumulates with every new feature.

Edge cases break the illusion of stability

AI-generated systems often successfully handle standard scenarios. But real-world conditions are far from ideal.

Unexpected user behavior, legacy integrations, and growing traffic quickly reveal weaknesses.

Igor Omelianchuk specifies: “It’s often the case when AI generates the functionality that works for the time being. But once we need to add some features, the code becomes not scalable.”

That’s why the resilient operation of many AI-generated products may eventually require complete rewrites: not because of poor AI coding, but because scalability wasn’t integrated from the beginning.

The Biggest Misconception About AI Development

“The greatest misconception is the expectation that AI will create the perfect logic without giving it the perfect context,” Yuriy Diachenko highlights.

There is a big difference between generating code and understanding the product itself.

Without deep knowledge of business goals, workflows, integrations, user behavior, and future scalability requirements, AI starts guessing instead of applying context. And those assumptions often turn into hidden problems later.

Igor Omelianchuk supports:

“If you do not provide the exact context of the product that you want to build, it will not build it in a way that you will be able to maintain later.”

Fast prototyping helps teams validate ideas, create MVPs, and visualize concepts in days instead of months. But a prototype is different from a scalable product architecture.

Ultimately, without proper product understanding and engineering oversight, AI-accelerated development can also accelerate long-term problems.

When Your AI-Generated Application Is 95% Complete but Still Cannot Be Released

The vibe coding challenges described above concern products in development or already growing. Another scenario sits between these stages: the founder has an application that looks almost complete and needs a team to take it to release.

What Founders Expect When They Bring a Vibe-Coded App to a Development Team

Igor Omelianchuk describes a request Corsac now receives regularly: founders bring an application they vibe-coded themselves, consider it mostly working, and expect the team to complete it, release it to production, assess the risks, and make sure everything works.

The screens open, the main user flows run, and the demo may already have convinced the first investors or customers. From the founder’s side, the remaining work looks like a final stretch, so the budget and the timeline are planned for a short engagement.

Andrew Lychuk notes that Corsac received such requests long before AI coding tools appeared, and the outcome was usually the same:

“Customers came to us and said that their system is 95% ready; it’s only 5% to be done, just get it finished and go to release. And this 5% is, in most cases, just something that can’t be done.”

• An application can look 95% finished and still be far from 95% ready for launch, because the percentage counts visible features and says nothing about what they stand on.

Why the Last 5% Is Often the Hardest Part of the Project

The phrase “last 5%” suggests a small amount of work. In practice, it contains everything that tests the product as a whole: 

• the release to production, 
• the risk assessment Igor mentions, 
• the check that every flow works together under real conditions.

Before AI tools, the same pattern appeared whenever a product was built by people without enough engineering experience: the code looked almost finished, while the system could not be completed because the architecture underneath was never established.

AI coding tools reproduce this pattern at a much higher speed, and a founder without a technical background can now reach the “almost finished” state on their own. 

Igor acknowledges that code generation tools keep improving and work substantially better than a year ago, yet Corsac still does not see them “ready to create anything close to production quality.”

What Happens When a New Team Takes Over an Almost Finished Product

Andrew explains the handover with the example of a house on a weak foundation, which looks almost finished but has become shaky and unstable:

“The owner of the house says: okay, this company that built the house is not good, so let’s hire another one just to finish it. And the other company comes, assesses it, and sees that the foundation is not correct. So instead of finishing the house, you have to reconsider and redo a lot of what was done before, and dig down to the base.”

The new team first has to assess readiness: 

1. Whether the existing architecture can carry the missing functionality, 
2. How much of the original request can be built on this base. 

The scope that comes out of this assessment rarely matches the “5%” the founder planned for.

• The tool that produced the first 95% of the product is rarely able to finish the remaining 5%, since the missing part is the foundation it never laid.

Why Fixing AI-Generated Code Can Create More Problems Than It Solves

One of the vibe coding problems Andrew Lychuk describes in detail is the way an AI agent handles its own mistakes. It shows, step by step, how technical debt accumulates in an AI-built codebase.

AI Agent Adds New Code on Top of a Broken Solution

Andrew describes a situation most people who have worked with an AI coding assistant will recognize:

“Most of us have met the situation when the answer from AI is not really correct. You point it out to the AI model, and you get an answer: yes, you are correct, let me redo it.”

The agreement sounds like a correction, and the founder reasonably assumes the bug is gone. Andrew explains what usually happens inside the code:

“Especially when you create code, it’s not fixing the problem; it’s just adding another hundred or a thousand lines of code on top of the previous solution. So you can imagine what kind of spaghetti code you get at the end.”

The agent treats the bug report as one more task and generates a new implementation for the latest message. Andrew connects this to the way such applications are built: the agent receives the product task by task and never sees the whole system. 

• Each “you are correct” from an AI agent has to be checked in the code itself, since the agent’s agreement says nothing about what actually changed.

Switching to another AI model changes little: without the context and complexity of the whole system, the result stays the same.

More Generated Code Does Not Mean More Working Functionality

After many such iterations, the size of the codebase stops reflecting what the product can do. In the worst cases, Andrew says, most of the code, possibly as much as 95% of it, may sit in the repository without contributing anything to the system. 

He lists what accumulates there:

“All these dependencies, all this unused code, or the outdated code, or the so-called fixed code is there, and it’s like a big mess.”

Andrew names four kinds of code that pile up in such a repository:

• Dependencies added for an earlier implementation that remained after it was replaced.
• Unused code: functions and components that nothing calls anymore.
• Outdated code: earlier versions of the same logic living next to the newer ones.
• “So-called fixed” code: fixes stacked on top of a bug that left the original problem in place.

Each category has its own cost: 

1. Unneeded dependencies still need updates and security checks, 
2. Outdated logic causes conflicts when someone edits the wrong version of a function, 
3. Stacked fixes hide the real cause of a bug.

• The volume of generated code says nothing about the volume of implemented functionality, and after many rounds of AI fixes, the gap between them keeps widening.

Why Completing the Last 5% Starts With an Inventory of Existing Code

This is why adding the “last 5%” to such a system cannot start with writing new code. The first step is to analyze the whole codebase and establish which lines actually matter for the system.

In practice, this is an inventory of the codebase: 

1. Which code is actually executed, 
2. Which modules the working features depend on, 
3. Which parts are leftovers of earlier iterations. 

Only with this map can the team estimate the real remaining scope.

• Before anyone adds a feature to an AI-built application, someone has to establish which of its code actually works.

Should You Fix, Modernize, or Rewrite an AI-Built Application?

For the owner of an AI-generated application, a costly mistake is to keep investing in the codebase before anyone has checked whether it can carry the product further. 

→ This is the point where vibe coding risks shift from the code to the owner’s budget.

Why Continuing Development on an AI-Generated Codebase Is Not Always the Right Call

Igor Omelianchuk ties the answer to architecture. The architect’s decisions take into account the long-term vision of the project and its planned expansion, and when this work is skipped, the consequences surface once the business needs the product to grow.

According to Igor, this is one of the common reasons companies turn to development teams to expand, rewrite, or modernize software: the owners see that the product can no longer be expanded or meet their business goals.

AI agents usually skip the architectural stage, since they start writing code right after the first request, so a vibe-coded product may hit the expansion problem before its first release. 

Igor explains where it leads:

“If this step is omitted, and the proper architecture is not set by the person who knows how to do this, this first step to success has failed. Then you can easily get into a situation where it’s better to start everything over than to try to fix whatever is already there. The system may look good, something in the system may work, but it will simply never be ready for production.“

• The longer development continues on an architecture that cannot be extended, the larger the share of the owner’s budget that goes into code that will later have to be replaced.

Continue, Modernize, or Rewrite: What a Code Review Tells the Owner

An assessment answers this question with evidence from the code itself, following the order Andrew Lychuk recommends for such a codebase:

1. Remove the unnecessary code.
2. Check what architecture the AI agent actually created.
3. Only then add or fix functionality.

After this cleanup, Igor describes the outcomes Corsac arrives at in such projects:

“Sometimes we can rebuild this software from scratch. We can try to modernize it if it was old software. Or sometimes we just review such software or such code, so that the owner of the system can make a more or less reasonable decision about whether to continue with the existing code or change the code completely and rewrite it from scratch.”

For the owner, the vibe coding pros and cons look different depending on the outcome:

What an Independent Code Review Gives the Owner Before the Next Investment

An independent review gives the owner an outside view of the codebase before the next round of budget is committed.

Corsac runs such reviews as a full technical audit, which takes two to three weeks, or a lighter code review. 

→ In both cases, the owner learns how the system works, what prevents it from growing, and which of the three outcomes fits the product, along with a step-by-step plan if modernization is needed.

For teams that decide to keep and evolve the existing code, Corsac’s AI Code Support covers the next steps, including cleanup, refactoring, and documentation of AI-generated code.

Where AI Actually Delivers Huge Value

Despite the limitations, AI has already become an incredibly powerful development tool when applied correctly.

Its biggest strength is not replacing engineers, but rather complementing them by accelerating repetitive, time-consuming work and allowing them to focus on strategic questions.

UI generation and boilerplate automation

One of the areas where AI provides significant help is frontend and UI development.

“It takes a lot of time to create a perfect layout based on the design from Figma, and the AI can convert it in a minute, while a developer just needs to make several tweaks until it acquires a good look,” shares Igor Omelianchuk.

Yuriy Diachenko adds: “There’s so much code that should be written on the UI to make the perfect layout… The AI tool can do this.”

Thus, AI dramatically speeds up:

• Layout creation
• Component generation
• Styling
• Animations
• Repetitive frontend tasks

Instead of manually writing every interface element, developers only need to refine the user experience and business logic after the bulk of the work is done.

Faster discovery, documentation, and testing

AI can also help at the early stages of projects.

Engineers tend to apply AI for:

• project discovery,
• requirements analysis,
• codebase exploration,
• documentation generation,
• and automated test creation.

AI can quickly scan large codebases, summarize structures, identify dependencies, and generate initial documentation that engineers later validate and finalize.

Therefore, intelligent agents help teams collect information about the product, which is particularly useful in legacy modernization projects.

Productivity burst

Probably the most significant impact of AI is the reduction of mechanical coding work that developers need to do.

Yuriy Diachenko ironically compares: “Like a bunch of junior developers doing the tasks.”

But the core difference is speed.

AI can generate drafts, suggest implementations, automate repetitive tasks, and substantially accelerate prototyping. The work that previously used to take days can now be fulfilled in hours.

Still, the experts highlight that AI produces superior results when experienced engineers are fully involved in architecture, validation, and strategic decisions.

How To Use AI In Development The Right Way

A successful AI-assisted development involves discipline, strategic thinking, and human ownership.

Build an AI-assisted SDLC instead of a shortcut

“We should follow all the steps that are meant to be here with regular SDLC development,” recommends Yuri Diachenko.

Practically, this implies that the following stages remain in place:

• Requirements analysis
• Architecture planning
• Code review
• Testing
• Documentation
• Validation

This would help avoid fragile systems and growing technical debt.

Documentation and context matter

One of the biggest challenges in vibe coding is context management.

In fact, AI tools cannot really “understand” the product until engineering teams provide structured information about:

• business goals,
• system architecture,
• previous decisions,
• integrations,
• and future plans.

Igor and Yuri strongly recommend keeping:

• context files,
• memory banks,
• prompt history,
• and live documentation.

Igor Omelianchuk comments: “Intent doesn’t match the product that we receive because AI just didn’t get it to implement correctly.”

Ultimately, better output stems from a better context.

Human review is mandatory

“Anything generated by AI needs to be verified by a human.”

Quickly generated code without developer control can lead to scalability risks, architectural inconsistencies, duplicated logic, hidden edge cases, and maintainability problems. Quite often, they can be visible only to experienced professionals.

This is why senior engineers become even more important in AI-assisted development.

With AI on board, their role includes:

• Validating architecture
• Guiding AI outputs
• Reviewing scalability
• Ensuring maintainability
• Protecting long-term product quality

AI is most powerful when teams adapt correctly

The future of software development is not a contest between AI and engineers.

Instead, engineers become leaders who know how to leverage AI for maximum results.

AI, in turn, is treated as:

• a productivity driver,
• a prototyping accelerator,
• a support for repetitive tasks,
• and a tool for faster experimentation.

But the responsibility for product quality, engineering strategy, and long-term resilience will still rest on humans.

Who Owns AI-Generated Code? Intellectual Property and Know-How Concerns

Igor and Andrew treat ownership as an open question for the industry: who owns AI-generated code, and whether it can be a company’s know-how. The views below are the founders’ personal opinions, and they are not legal advice. The legal status of AI-generated code, including whether copyright applies to it, depends on the jurisdiction, the terms of the AI tools involved, and the circumstances in which a product was created, so specific cases should be reviewed with a qualified lawyer.

Can AI-Generated Code Be Considered a Company’s Unique Intellectual Property?

Igor Omelianchuk starts from the standard commercial view of software ownership:

“The standard approach is that the code is an IP, and apparently the company owns the IP. And it doesn’t matter whether this IP was created manually or generated by, let’s say, Claude. It’s still the property of the company.”

Whether the same code can be treated as know-how is far less settled: Igor notes there is little legislation on this yet and expects it to appear soon.

• Owning a codebase and owning something unique in it are two separate questions, and AI-generated code makes the difference much more visible.

Similar Code Across Projects: Common Patterns or Copied Know-How?

The uniqueness question becomes practical when two products start to look alike from the inside. Igor expects a new kind of dispute: similar AI-generated solutions may turn up in unrelated projects, and their developers may be accused of copying another project’s code or approach, while the AI agent may simply have produced similar solutions for similar cases.

In his opinion, this kind of similarity says little about copying:

“It cannot be considered know-how. It cannot be considered something unique. It’s just common knowledge, like common architectural patterns, which can be similar in various projects, but it doesn’t mean that they were copied from one project into another.”

For founders, this is one of the less obvious vibe coding risks: a codebase they consider proprietary may contain implementations that closely resemble someone else’s, created independently by the same kind of tool.

Business Logic vs. Technical Implementation: Where a Founder’s Know-How Actually Lives

Andrew Lychuk suggests looking at a product as two separate layers:

“We cannot give any legal advice, so we can only express our opinion. From my point of view, we should separate the business solution, the ideas that you want to integrate in your system and your product, and the technical realization. I think that the business solution could be know-how for sure, but by the very nature of how large language models work, there can’t be any invention in the code.”

Andrew bases this on the way LLMs are trained on existing text and code written by people. This is his personal reading of how the models work, and the question remains debated in the industry.

Igor turns the distinction into an example founders can apply to their own products:

“Let’s say you are providing some kind of financial reports, and you have unique formulas which you developed yourself. Those formulas are certainly your IP. But the actual technical realization, the UI, the server which runs the application, if it is generated by AI, is not your know-how. It’s more like common knowledge realized into an application by algorithms. Nothing more.”

A practical step for founders is to separate three things in their own documentation: 

1. Business idea, 
2. Original elements their team created, such as unique formulas and business rules, 
3. Code generated by AI. 

• When the company can show exactly what it considers proprietary, conversations about IP with investors or a development vendor become much more concrete.

Igor expects contracts and legal agreements to change significantly on this matter, since AI-generated code will remain an edge case that needs close attention. Andrew agrees.

Until legal practice settles, these questions are worth raising with a lawyer when drafting agreements for AI-assisted development:

• who holds the copyright and other rights to the code and deliverables created with AI tools;
• whether the vendor may use AI coding tools, and under which terms of service;
• how the client’s proprietary business logic is identified and protected;
• how the parties handle a claim that a generated solution resembles another company’s code.

In Conclusion

With the introduction of AI, software development has undergone evolutionary, if not revolutionary, change.

AI expedites coding, simplifies prototyping, automates repetitive tasks, and opens new possibilities for both engineers and businesses.

But vibe coding also reveals an important truth:

• Generating code and building sustainable software are two different things.

Applications created without architectural thinking, context, validation, and experienced review may work fine “today.” However, they become fragile, expensive, and difficult to support “tomorrow.”

The real value of AI in development lies not in replacing engineers but in amplifying their capabilities.

We are probably witnessing the biggest shift in modern software engineering:

The future belongs not to AI alone but to professionals who know how to combine AI productivity with human expertise, product understanding, and strategic engineering thinking.