The text discusses the growing interest in Artificial Intelligence (AI) and its impact on engineering teams. Large Language Models (LLMs) are mentioned as a key trend, but also highlight the challenges of model interpretability and reliability. To overcome these challenges, developers need to rely on different approaches to test and build confidence in LLM-enabled applications, such as retrieval-augmented generation (RAG). Additionally, agentic AI systems promise to make decisions independently, but introduce transparency issues that require careful consideration. The text also touches on the importance of data-streaming platforms, event-driven architectures, and real-time data access in sustainingably building and scaling AI capabilities. Furthermore, it highlights the need for engineers to stay sharp on computer science fundamentals, increase their proficiency in popular languages, and understand real-time data processing and event guarantees.