First things first – AI helps produce code. This is undoubtedly a significant breakthrough and will change a lot. Numerous tools are available to try out and witness this firsthand. However, paradoxically, writing code is not the primary challenge in software development. The real issue is determining which code needs to be written.
In software development, understanding business requirements is crucial. Yet, in practice, these requirements are often too vague to successfully build software. Even when requirements seem clear, misunderstandings frequently arise later – through overlooked steps, redefined processes, or stakeholders brought in late. This often leads to costly adjustments and change requests.
The key factor in software development remains the human element. Our work, for example, always involves teams of developers, UI/UX experts, architects, and product owners (POs). Communication is essential, as tasks must be distributed and organized – and this organization shapes the software itself. Conway’s Law, named after the American computer scientist Melvin Conway, states:
"The architecture of software mirrors the communication structures of the team."
This is something AI – for now – cannot influence.
Simplifying code production is nothing new. There is a long history of technical advancements aimed at making software development easier, typically by introducing new levels of abstraction. From binary code to assembly, and on to modern programming languages and frameworks – each step has simplified development. AI represents another leap forward, elevating development to a new layer of abstraction. Tools like GPT Engineer can already create entire applications based on specifications, asking clarifying questions if requirements are unclear.
Thus, developing software with well-defined requirements isn’t the issue. AI simplifies the less problematic aspects of development – but not its core.
Use Cases for GenAI in Software Development
Clearly Defined Projects – For projects with clear requirements, GenAI can efficiently execute and automate development tasks.
Efficiency Gains – GenAI accelerates development by automating manual tasks, enhancing overall productivity.
Quality Assurance – Using machine learning models, GenAI can automatically review code quality, detecting anomalies and potential errors.
Test Case Generation – AI can generate test cases, improving test coverage and accelerating high-quality software delivery.
Data Preparation and Analysis – AI algorithms can extract, clean, and format data automatically for further processing.
Drawbacks of GenAI in Software Development
Humans Are Irreplaceable – Task distribution and project organization remain human-driven, directly impacting software outcomes.
The Human Factor as a Core Challenge – Many project issues stem from human dynamics, an area where AI cannot provide solutions.
Unclear Requirements and Processes – Ambiguous requirements can lead to long-term complications, as essential factors may be overlooked.
Security Risks – Studies show that developers using AI-assisted coding often overestimate the security of their code, even though it may actually be less secure.
The Future of GenAI in Software Development
GenAI is likely to expand the size and complexity of software teams as more intricate systems are developed, increasing the role of software across industries. If the trend continues, AI will enable the creation of even more sophisticated systems, driving team growth accordingly. While GenAI enhances efficiency and accelerates delivery, the core challenge of software development – understanding what needs to be built – remains.
GenAI will undoubtedly transform software development, automating tasks and streamlining processes, but it will not solve the fundamental problem of clearly defining project goals and requirements.