A few days ago, I had a long conversation over a good whiskey with a close friend from Berlin’s startup scene about the new reality of software development. With 20 years of experience in various roles—CTO, architect, and now a senior engineer at a Berlin-based FinTech—he has a deep understanding of the industry.
Our discussion on the future of software development actually stemmed from a broader question: what kind of world will our children grow up in? He admitted that just two years ago, he envisioned a future career in software development for his daughter. But now, he’s not so sure what this profession will look like in 15 or 20 years. Even today, software development has evolved into an intense collaboration with AI—a structured dialogue about requirements, architecture, and more.
What makes his perspective particularly striking is that about a year and a half ago, this same friend firmly advised against junior developers using AI coding assistants. His reasoning? Senior developers were spending too much time cleaning up mediocre code—especially where AI-generated solutions failed to properly implement specific business logic.
Fast forward 18 months, and some of the early growing pains of AI-powered coding assistants have been addressed, thanks to more refined AI integration methods. The new reality of software development is clear: 64% of software developers have incorporated AI into their workflows. 62% use AI for code review. 58% leverage it for writing code. These insights come from a recent survey conducted by the platform Codacy.
Another acquaintance of mine, who holds a PhD in Machine Learning and Semantic Search, takes his predictions for the future even further. Traditionally, software development has been structured around three broad categories: Junior Developers, Mid-Level Developers (who typically hold stable jobs) and Senior Developers (responsible for solving complex problems). However, this landscape is undergoing a dramatic transformation. Over time, it will likely consolidate into just two distinct groups: Product Builders – Individuals who code and develop entire products, often independently. They heavily rely on AI-powered tools, such as AI coding assistants, to streamline their work. And: Frontier Engineers – Experts tackling the most challenging technical problems, such as Artificial General Intelligence (AGI) or autonomous driving.
This transition will happen at different speeds across different areas of development. Front-end development, for example, is expected to undergo this shift more rapidly than lower-level, machine-centric programming languages.
How to Leverage AI in Software Development
We are still far from achieving flawless, production-ready code at the push of a button. Nearly every blog on this topic emphasizes the crucial role of code review and the fact that high-quality, usable code is the result of an iterative process. However, the pace of development in this field is accelerating rapidly. Recently, for instance, Anthropic introduced Claude Code, an agent-based coding tool.
Software developer Aalap Davjekar captures this sentiment well in his article on Medium, where he presents a refined methodology for leveraging AI in software development.
”Here are some mindset shifts that really helped me:
Aalap Davjekar’s article series (divided into three parts) provides a detailed methodology for integrating AI into software development. He clearly outlines common pitfalls to avoid and offers proven prompt examples, making it an excellent starting point for those new to the topic.
The process begins with a project plan, followed by design and architecture. Davjekar also recommends setting up a knowledge base for the AI assistant—containing key requirements, design decisions, and other critical information—to enhance its effectiveness.
Here is a prompt template to kickstart database design:
Finally, the process moves on to coding itself. For this stage, the author and developer utilizes the following prompt template:
As Aalap Davjekar emphasizes from the start: “Verify and understand: Always review and understand the code AI generates.” This review process can also be assisted by AI. To facilitate this, the author suggests using the following prompt templates:
… and:
AI can also assist with code refactoring as well as testing, making the development process more efficient and streamlined:
Finally, AI can also generate comprehensive documentation, including:
