Artificial intelligence is being integrated into nearly every professional workflow. Its effects are clear in software coding. AI tools can now write code, build applications, and automate tasks. These tasks earlier required the expertise of trained software engineers. Many now believe that AI could eventually replace software engineers.
At first glance, this idea appears convincing. Today, even those without coding skills can create impressive apps. This is now known as “vibe coding.” In these cases, a person clearly says what they want. Then, the AI creates the software. If the instructions are clear enough, the result can be a functional app that serves a real purpose and can even be published on an app store.
In these cases, the user keeps ownership of the resulting software. While many people may use the app, the creator remains responsible for its quality and performance.
A similar change is happening inside organizations. AI can now turn many tasks, once done in spreadsheets, into small apps. You don’t need much technical knowledge. You can automate repetitive tasks and make dashboards instead of using static spreadsheets. Plus, you can customize systems to fit customer needs. Many things that needed big budgets and expert teams are now affordable for regular users.
The situation shifts when software is made for sale, not just personal use or convenience.
For the customer, the method used to create the software does not matter. The customer is concerned only with the outcome. They want software that is accurate, reliable, responsive, secure, and stable. They also expect updates and fixes when needed.
This introduces a completely different level of responsibility.
In such a case, AI becomes only one part of the process. The company creates, delivers, and maintains the software for the client. If defects arise, the organization can’t blame the AI. It is still legally and operationally responsible for the final product.
This is where the role of the software engineer returns in a very important way.
While AI may generate the code, someone still needs to understand what the code is doing. If a client reports a problem, the company must find out where it started. Next, they need to fix it and ensure future updates won’t disrupt existing systems. Asking AI to create a new version every time isn’t always practical. This is especially true for large or mission-critical systems.
Organizations will need software engineers who can work with AI, not be replaced by it. Their role may change, but their importance remains.
The future software engineer will likely write less code by hand. Instead, they will focus on managing AI tools. They will review generated software, understand system architecture, and validate outputs. Their work will also involve improving robustness and ensuring long-term maintainability. The engineer acts as both a technical supervisor and a systems manager.
This difference becomes especially important when third-party clients are involved. Vibe coding is great for personal projects or launching products independently. But when providing software services to clients, reliability and accountability are key. Companies must keep their software updated, fix issues, and make changes as needed.
So, the belief that AI will fully replace software engineers seems unrealistic. Instead, the profession is likely to evolve into something different.
Another important aspect of AI-driven coding is cost.
In the past, companies mainly hired human engineers to write software. They paid salaries to developers and bought some software tools to help them. The engineer knew the code they were writing. They also managed the development process directly.
With vibe coding, however, the process becomes more like interacting with a black box. A user describes what they want, and the AI generates the application. The further a person is from actual coding knowledge, the more they depend on AI to bridge that gap.
But this dependency is not free.
AI systems rely on large data centers that require massive amounts of electricity and hardware. AI providers often charge users through tokens, credits, or usage-based pricing. This is due to the high infrastructure costs. The more assistance required from the AI, the more tokens are consumed.
This creates an interesting economic reality.
A person with strong coding skills might need fewer AI interactions. They can guide the system well and do parts of the work on their own. However, a person with little or no coding skills might depend too much on AI. This can lead to much higher costs.
As AI use grows, token costs could become a major expense for organizations. The balance between human engineers and AI might depend more on economics than just technology or talent.
Large companies with deep financial resources may be able to rely heavily on AI systems and absorb these costs. Smaller companies may prefer engineers who use AI wisely. This helps keep costs down.
Even vibe coders might need to think about how much AI they can afford.
There is also the question of maintaining AI-generated code over long periods.
Even traditional software can be hard to understand as systems get larger. Developers often need time to revisit code they wrote months or years ago. Yet there is usually a logical structure behind human-written code.
AI-generated code introduces additional complexity.
If the instructions aren’t very clear, AI might produce code that works but can be hard to follow or poorly designed. Talk about terms like “slop” is growing. “Slop” means poorly optimized or messy AI-generated code. Beyond messy structure, AI hallucinations are a common issue. This occurs when the system provides wrong or confusing outputs that are hard to notice.
This creates real risks.
Code may function initially while still containing hidden weaknesses, inefficiencies, or errors. Relying too much on AI without human checks can be risky. This is especially true in work settings where stability and reliability are key.
Companies and people using AI for coding should check the tech and cost effects. AI is automating routine tasks and accelerating the development cycle. It lowers the barrier to entry by putting powerful tools in more hands. However, these tools do not replace the necessity for human expertise.
Instead, it is changing the nature of that expertise.
The future might not be just about human coding or only AI-made software. It will likely belong to those who can combine human expertise with AI capabilities. They will use AI to boost speed and productivity. However, they will still rely on human understanding, judgment, accountability, and control.
