Artificial intelligence is rapidly reshaping the way in which software is designed, developed, and maintained. From change recommendations and intelligent autocomplete to vulnerability detection, automated program repair, and agentic code generation, AI tooling is increasingly embedded across the software engineering lifecycle. These changes offer significant opportunities by accelerating development, amplifying capabilities, and unlocking new forms of automation that help software engineers scale up their work with an increased focus on the creative aspects. However, the integration of AI into the software engineering lifecycle also raises serious concerns. AI-generated code may be incorrect, insecure, or subtly biased. Developers risk over-relying on opaque systems, while sophisticated attackers may exploit generative models to insert backdoors or mislead human reviewers. As AI tools evolve from assistants to agents, we face a growing need to scrutinize their behavior, limitations, and impact on software quality, security, and developer cognition. This keynote surveys the emerging landscape of AI in software engineering, drawing on insights from our earlier research efforts that span change recommendation, log analysis, vulnerability detection, automated program repair, and autonomous evolutionary programming agents. We will reflect on the deeper implications of adopting AI in software engineering. What are we gaining, and what might we be losing? How can we ensure that human oversight, intent, and accountability remain central, and what role can the research community play as we move toward increasingly autonomous software engineering ecosystems?

Leon Moonen is a Professor and head of the Data-Driven Software Engineering Department (dataSED) at Simula Research Laboratory, Norway. He also holds a visiting professor position at the Department of Data Science and Analytics at BI Norwegian Business School. His research is aimed at the design and development of advanced, data-driven techniques and tools that support software engineers with the assessment, evolution, and operations of complex industrial software systems, with a particular interest in security and resilience in Software Systems, self-healing and self-adaptive technology and, more general, the application of machine learning and AI in Software Engineering. His research covers a wide range of topics, such as software analytics, software reverse engineering, software repository mining, machine learning, program comprehension, and empirical software engineering. Leon prefers to work in close collaboration with industry, to ensure that his research addresses questions of practical value, and to evaluate candidate solutions in real-life circumstances. Current projects investigate automated identification and repair of software security vulnerabilities, the use of LLMs to support cyber threat intelligence, adaptive bio-inspired techniques for creating autonomously self-healing systems, smart analytics of the vast amounts of logging data produced in continuous engineering, and recommendation systems for smarter evolution and testing of software-intensive systems.