Generative AI is transforming software development, offering new tools and processes that enhance productivity, cost, and quality. As a key enabling technology, it can be used for creative tasks and to replace repetitive, manual work. Large language models (LLMs) and generative AI (GPT) have created new opportunities, but also raise concerns about ethics, education, regulation, intellectual property, and criminal activities. This paper analyzes the potential of generative AI and LLMs for future software development paths, proposing four primary scenarios and model trajectories for transitions between them. The research aims to help the software development industry understand the potential, limitations, and risks of generative AI, as well as guidelines for its use. Generative AI is driving automation in software development, with tools like repositories, cloud services, and co-creation methods such as Lean, Agile, and DevOps improving productivity. The introduction of LLMs and GPT-based services has accelerated the transformation of software development. Developers use co-pilots, interpreters, and automated GPT models to optimize tools and workflows. AI enhances code discovery, production, review, testing, configuration, and optimization. It also supports tasks like documentation, debugging, and simulation of execution models. The paper presents four scenarios for the future of software development, each reflecting interactions between human roles, tools, AI, and processes. These scenarios assume that more automation is desirable, with human and AI roles co-existing. The scenarios can be parameterized to model different types of software development operations (SDOs). The analysis of these scenarios shows that generative AI can significantly improve productivity, especially in repetitive tasks, updates, and ideation. However, tasks requiring high creativity or complex skills have not produced satisfactory results. The paper also discusses the ethical and legal implications of generative AI, including issues of intellectual property, accountability, and the need for new regulations. It emphasizes the importance of continuous education and the need for new roles such as AI system supervision and optimization. Generative AI presents promising prospects for software development, offering strategic tools to drive productivity, resource optimization, and cost/time savings. The study concludes that further research is needed to examine the implementation of these scenarios in practical SDOs.Generative AI is transforming software development, offering new tools and processes that enhance productivity, cost, and quality. As a key enabling technology, it can be used for creative tasks and to replace repetitive, manual work. Large language models (LLMs) and generative AI (GPT) have created new opportunities, but also raise concerns about ethics, education, regulation, intellectual property, and criminal activities. This paper analyzes the potential of generative AI and LLMs for future software development paths, proposing four primary scenarios and model trajectories for transitions between them. The research aims to help the software development industry understand the potential, limitations, and risks of generative AI, as well as guidelines for its use. Generative AI is driving automation in software development, with tools like repositories, cloud services, and co-creation methods such as Lean, Agile, and DevOps improving productivity. The introduction of LLMs and GPT-based services has accelerated the transformation of software development. Developers use co-pilots, interpreters, and automated GPT models to optimize tools and workflows. AI enhances code discovery, production, review, testing, configuration, and optimization. It also supports tasks like documentation, debugging, and simulation of execution models. The paper presents four scenarios for the future of software development, each reflecting interactions between human roles, tools, AI, and processes. These scenarios assume that more automation is desirable, with human and AI roles co-existing. The scenarios can be parameterized to model different types of software development operations (SDOs). The analysis of these scenarios shows that generative AI can significantly improve productivity, especially in repetitive tasks, updates, and ideation. However, tasks requiring high creativity or complex skills have not produced satisfactory results. The paper also discusses the ethical and legal implications of generative AI, including issues of intellectual property, accountability, and the need for new regulations. It emphasizes the importance of continuous education and the need for new roles such as AI system supervision and optimization. Generative AI presents promising prospects for software development, offering strategic tools to drive productivity, resource optimization, and cost/time savings. The study concludes that further research is needed to examine the implementation of these scenarios in practical SDOs.