What’s the laziest thing you’ve ever used AI for (no judgment 😄)?

I am starting a question of the day where I will ask one question and everyone in the community will get to ask a question. I will try to answer all of them as per my experience and knowledge - Let’s get started. What is a realistic thing AI agents will be doing for ordinary workers by 2028 that they are not doing today?

Artificial Intelligence (AI) is no longer a buzzword — it’s reshaping industries from healthcare and finance to transportation and retail. As companies adopt intelligent systems at scale, the role of the AI Engineer has emerged as one of the most sought-after technical careers. AI Engineers take powerful AI models and tools and apply them to real-world problems, enhancing user experiences and driving innovation across products. Whether you’re just starting or transitioning from software development, this roadmap provides a practical, step-by-step guide to build the skills, experiences, and professional confidence needed to thrive in this exciting field. ————————————————————— 1. Understand the Role of an AI Engineer Before diving in, it’s important to clarify what an AI Engineer does. Unlike AI researchers, who invent new algorithms, or traditional Machine Learning Engineers, AI Engineers often focus on applying pre-trained models and existing AI tools to production-grade solutions — from recommendation systems to conversational agents — and integrating them into broader software architectures. This means working with both software engineering fundamentals and AI-specific tooling to deliver real business value. ————————————————————— 2. Build a Strong Technical Foundation a) Programming & Computer Science Fundamentals - Master Python — the lingua franca of AI — including packages such as NumPy, Pandas, and Matplotlib for data manipulation and visualization. - Learn software engineering principles (data structures, algorithms, object-oriented design), version control with Git, and best practices for writing maintainable code. b) Mathematics that Powers AI AI systems are rooted in mathematical concepts such as: - Linear Algebra — for vector operations in neural networks - Probability & Statistics — for understanding model predictions and uncertainty - Calculus — for optimization and gradient-based learning algorithms This mathematical literacy helps you understand why AI works, not just how to use it.