Will AI replace software engineers?

We stand at a sharp curve of change triggered by the rapid development of AI. We can't see what's behind the curve. It's a fertile ground for fear-mongering and speculation. That’s why talk of the collapse of the software development profession often gains traction on social media, and news of major tech layoffs quickly makes headlines.

Why hire a programmer when GenAI tools can generate working code snippets? Today, even those with limited coding knowledge can create functional applications using low-code and no-code solutions. But on the other hand... Are AI tools so perfect at this point that they don't need any supervision? And what about data security?

At Happy Team, we started a debate: Will AI replace software engineers? We looked at the question from two very different angles: one, a visionary – excited about AI and optimistic about its potential; the other, a realist – pragmatic, cautious, and focused on keeping humans at the centre.

Is it possible to decide who’s really leading the way? Let’s find out.

What are AI's capabilities in coding?

Kuba: "With Copilot, ChatGPT and similar solutions, developers write code twice as fast. And with Bubble or Webflow, you need less code overall."

Emil: "But Bubble isn’t designed to handle complex, secure backend systems like those used in banking. No-code is more for MVPs than core business."

Our take: AI tools are speeding things up, but they still fall short on the hard stuff, like secure backends and scalable systems. In 2025, AI is a powerful co-pilot, but not the captain. It makes work easier but doesn't replace engineering thinking, especially when it comes to complex systems, security and scalability.

We can't underestimate the impact of artificial intelligence tools on the developers' workflow. For example, GitHub's Copilot is trained on the syntax and patterns of hundreds of programming languages and can complete code in real time, while Replit excels at prototyping and testing small pieces of code. However, each has its limitations, such as hallucinations or low flexibility.

AI speeds up coding but doesn't replace engineering thinking. Generative AI for MVPs? Great. But for global platforms, you still need a team of developers.

Is it still worth spending money on hiring software developers?

Kuba: "If AI writes 60% of the code, do you really need that many humans?"

Emil: "And who will verify if AI did it correctly? Mistakes are costly, especially in the final product."

Our take: This round goes to… a realistic approach. The number of AI-generated code lines shouldn't be the only performance indicator. What AI writes must be equally correct and:

• compatible with the system architecture,
• secure,
• tailored to the business needs.

Without experienced developers who understand the context and take responsibility for quality, AI projects will quickly accumulate a pile of errors that are hard to spot at first glance.

AI isn't eliminating the need to hire people. It's shifting the emphasis on IT recruitment. We're no longer hiring just for execution – we need developers who think strategically, analyse context, and guide AI tools effectively. So instead of asking: "Is it still worth investing in developers?", ask: "What kind of developers do we need now?". The answer is: "ones who can work using AI".

Teams that understand this shift will be more efficient and agile. However, they will still be human teams.

Can AI be a real alternative for experienced human programmers?

Kuba: “AI is not a junior – it’s a powerful repository of patterns that it analyses faster than a human.”

Emil: “However, it doesn’t understand the business context. It won’t come up with a better architecture. It won’t resolve team conflicts.”

Our take: Different perspectives, but this time, both are spot on. A vision in which AI becomes an integral tool to support the entire software development cycle seems possible. Some tasks can be handed over to AI. On the other hand, there are still areas where human creativity, intuition and experience remain irreplaceable.

The demand for entry-level developers will decrease. Companies will be interested in employees specialised in specific technologies, such as machine learning, data analytics or cybersecurity. A study by Peter Knudsen and Je Jiang shows that programmers don't believe in being replaced by artificial intelligence. They are much more optimistic about AI in terms of increasing their productivity, with the majority of those surveyed predicting a minimum 20% efficiency improvement.

What can AI already do (as of April 2025)?

Kuba: "AI works great in prototyping, refactoring, documentation, and unit testing. It's a significant acceleration."

Emil: "Agreed. But only if you have someone who understands what the code is doing. AI alone is not enough."

Our take: Both perspectives hit the nail on the head, but each from a different angle. AI does indeed speed up many elements of development, but it only works effectively in the hands of people who can use and supervise it properly. It's not magic, it's just a tool. And every tool needs an operator.

AI doesn't replace thinking, yet it eliminates routine. This reduces development time and pressure on IT teams. For companies, this translates into real benefits in time-to-market, but only if AI supports people rather than trying to replace them.

What areas are worth handing over to AI?

1. Prototyping: AI generates quick functionality sketches and MVPs, which shortens the time between the idea and the first version. Faster market validation means lower investment risk.
2. Refactoring: AI tools clean up and optimise code, eliminating technology debt on the fly. This results in fewer bugs and faster implementation of new features.
3. Documentation: AI automatically describes methods and classes, something that is often ignored. This helps new developers settle in faster.
4. Unit testing: AI generates automated tests while the code is written. This results in fewer code revisions.

What are the AI limits in 2025?

Kuba: “The limitations are shifting. Models learn from projects, data, and mistakes.”

Emil: “And they still hallucinate, don't understand project logic, and cannot manage application state.”

Our take: At this stage, the realistic perspective seems more in tune with where AI actually is. AI models in 2025 still fail to overcome core limitations such as understanding project logic or business context. AI is impressive but needs human guidance.

LLM doesn't understand why something works (or not). It only predicts the 'fit' based on patterns. For example, ChatGPT can generate API code that looks fine. However, after implementation, you may be surprised by inconsistent endpoints. The time gained by generating the code automatically will be wasted on debugging and fixing errors. If your company replaced software engineers with AI, even the best language model wouldn't explain why software development projects fail.

How is AI changing software development?

Kuba: "From now on, human developers become orchestrators – they don't write line by line but assemble the system."

Emil: "But to be a conductor, you need to know the notes. AI doesn't replace knowledge; it changes its use."

Our take: In this round, both perspectives bring valuable insights, though the argument seems to lean slightly toward the more grounded view. One side rightly points out that the developer’s role is shifting – there's less line-by-line coding and more focus on orchestration and decision-making. But the counterpoint is equally important: you can’t effectively run systems you don’t understand. AI simplifies many tasks, but it doesn’t replace technical knowledge – it reshapes it and brings new competencies to the forefront.

The vision of being a manager is appealing for many people. However, people overseeing product delivery using AI in logistics should have basic industry knowledge. Also, developers can't manage the software development process without knowing its specifics.

How does AI affect hiring and product development strategies?

Kuba: "A 5-person team with AI will accomplish what a 10-person team used to do without AI. It's a new model of efficiency."

Emil: "Yes, but only if those people know how to work with AI. Recruitment will change, not disappear."

Our take: Both arguments reveal important sides of the same shift. Yes, AI can boost efficiency – smaller teams can achieve more, faster. But that only works if people know how to work with AI, not just alongside it. This doesn’t mean developers are becoming redundant — their roles are evolving. The competence profile is changing, and with faster delivery cycles, we might actually see more developer roles, not fewer, as companies take on more parallel projects.

The U.S. Bureau of Labor Statistics (BLS) predicts a 17.9% increase in software developer jobs between 2023 and 2033. The demand in the US is expected to be around 300,000 positions. If AI were to completely take over the role of humans, this trend would look very different.

AI is changing the approach to software development, but it doesn't change the fact that we need people. Product teams will create roles that didn't exist a few years ago, such as:

• AI integrators who can embed language models and generative tools into a product.
• Prompt engineers who understand the relationship between the prompt quality and the result.
• AI product owners who know how to build a roadmap around artificial intelligence capabilities.

Hiring full-stack developers with AI skills may become the golden standard. These people will know the frontend and backend while working effectively with language models, optimising prompts and controlling the generated code quality. They will be not only efficient but also more resilient to technological change.

An AI team of five people can work like a team of 10 only if everyone knows what they are doing. The time to market still depends on the decisions, integration, testing, and understanding of the business context. In other words, the people.

Conclusion

Thanks for staying with us through the debate.

Visionary thinking, when grounded in facts, helps us reach beyond ready-made answers – and that’s exactly the point. The future won’t be built on bold declarations alone, but on a deeper understanding of the complex changes unfolding right now.

Some of those once-visionary ideas are already becoming reality. AI analyses faster than humans. It automates, simplifies, and speeds things up – from prototyping to unit testing. Tools like Copilot, ChatGPT, Replit, and Make have become a natural part of modern development environments.

But reality also reminds us of AI’s limits. It doesn’t grasp business context, design system architecture, resolve team conflicts, or take responsibility for ethical decisions. There's no quality, security, or reliability without people.

AI won’t replace programmers – AI will replace companies that don’t use it.

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