The rapid progress of generative AI is set to transform the software development life cycle (SDLC), revolutionizing the way humans work in the industry. According to a recent whitepaper by Crowdbotics, the future of SDLC will be characterized by near-instantaneous code generation, natural language as the primary programming interface, and humans acting as verifiers rather than doers. This paradigm shift will have far-reaching implications for speed, teams, intelligence, resources, and consumption in the SDLC.

The traditional two-week sprint model, popularized by Scrum in the early 2000s, will become obsolete as AI coding assistants increase developer speed by 50% or more, streamlining non-coding tasks such as reporting and project management. Estimates suggest that AI could replace up to 80% of project management activities by 2030, paving the way for shorter, more dynamic sprint cycles.

The role of human software engineers will undergo a significant transformation as AI models generate code quickly and cost-effectively. Humans will shift from creators to verifiers, overseeing AI-generated results and providing input. This new team structure will consist of multiple AI agents, each with a specific role in the software development process, such as defining project scope and objectives, and project planning and quality analysis.

Knowledge management, a time-consuming and often poorly executed aspect of software development, will become automated with the help of large language models (LLMs). LLMs will capture, store, and make available the wealth of content created during the SDLC, enabling efficient knowledge access and proactive application of context models. For instance, GitHub Copilot context models can be shared between developers, allowing them to pick up where a previous session left off.

Global development teams will form a relay, with AI facilitating seamless handoffs and context transfer between teams. This "follow-the-sun" delivery model will maximize efficiency, as development resources can be truly global, both physically and temporally.

The increased efficiency and accessibility of software development will lead to a surge in demand for new software applications, contrary to expectations. This phenomenon, known as the Jevons paradox, is driven by factors such as faster development speed, lower costs, expanded domains, and a larger talent pool. AI will play a central role in stimulating and meeting this demand, fundamentally rethinking how we build software with AI at the core of every process, tool, and methodology.

The Crowdbotics whitepaper presents a vision for the future of SDLC, which needs to be tested and refined. As new AI dev tools emerge daily, the industry is poised for significant change. The authors invite feedback and encourage readers to explore their research topics further.

This development marks a significant milestone in the evolution of software development, with far-reaching implications for the industry and its practitioners. As AI continues to advance, it will be essential to monitor its impact on the SDLC and adapt to the changing landscape.