The article discusses the evolving role of material scientists in the age of artificial intelligence (AI), emphasizing the potential of machine learning (ML) and AI to revolutionize research in material science. It outlines a conceptual framework called the "AI-ladder," which represents the progression from basic data-fitting techniques to advanced functionalities such as semi-autonomous experimentation, experimental design, knowledge generation, hypothesis formulation, and the orchestration of specialized AI modules. This framework helps in understanding the opportunities, challenges, and evolving skill sets required in the field. Material science has historically relied on human ingenuity, experimental expertise, and advanced characterization techniques. However, the discovery and development of new functional materials are complex and time-consuming, often hindered by the lack of theoretical models and the vast parameter space involved. AI and ML offer a potent toolkit to accelerate research, enabling the prediction of material properties, virtual screening of new materials, and the automation of experimental processes. At the first rung of the AI-ladder, ML models are used for regression and classification tasks, helping to predict material properties based on existing data. These models can also assist in data analysis, identifying patterns, and generating hypotheses. The second rung involves the integration of robotics and automation, allowing for the autonomous manipulation of physical environments and the execution of complex experimental tasks. This level of automation enhances data consistency and reduces the time required for experiments. The third rung introduces generative models, such as large language models, which can generate new hypotheses and suggest novel material systems. These models can also assist in literature reviews, identifying emerging trends and key discoveries. The fourth rung focuses on the orchestration of AI modules to create more versatile systems, enabling the integration of digital and physical components for complex tasks. This level of integration allows for the creation of comprehensive personal digital assistants that can perform a wide range of functions. The fifth rung and beyond explore the potential of general artificial intelligence (AGI), which could revolutionize material science by performing any intellectual task that a human can do. While AGI remains a subject of debate, its potential to transform research is significant. The article concludes by emphasizing the need for material scientists to adapt to the changing landscape, acquiring new skills and embracing AI to remain competitive in the field. The role of material scientists in the age of AI is expected to evolve, with a greater emphasis on data science, theoretical understanding, and the ability to formulate meaningful research questions.The article discusses the evolving role of material scientists in the age of artificial intelligence (AI), emphasizing the potential of machine learning (ML) and AI to revolutionize research in material science. It outlines a conceptual framework called the "AI-ladder," which represents the progression from basic data-fitting techniques to advanced functionalities such as semi-autonomous experimentation, experimental design, knowledge generation, hypothesis formulation, and the orchestration of specialized AI modules. This framework helps in understanding the opportunities, challenges, and evolving skill sets required in the field. Material science has historically relied on human ingenuity, experimental expertise, and advanced characterization techniques. However, the discovery and development of new functional materials are complex and time-consuming, often hindered by the lack of theoretical models and the vast parameter space involved. AI and ML offer a potent toolkit to accelerate research, enabling the prediction of material properties, virtual screening of new materials, and the automation of experimental processes. At the first rung of the AI-ladder, ML models are used for regression and classification tasks, helping to predict material properties based on existing data. These models can also assist in data analysis, identifying patterns, and generating hypotheses. The second rung involves the integration of robotics and automation, allowing for the autonomous manipulation of physical environments and the execution of complex experimental tasks. This level of automation enhances data consistency and reduces the time required for experiments. The third rung introduces generative models, such as large language models, which can generate new hypotheses and suggest novel material systems. These models can also assist in literature reviews, identifying emerging trends and key discoveries. The fourth rung focuses on the orchestration of AI modules to create more versatile systems, enabling the integration of digital and physical components for complex tasks. This level of integration allows for the creation of comprehensive personal digital assistants that can perform a wide range of functions. The fifth rung and beyond explore the potential of general artificial intelligence (AGI), which could revolutionize material science by performing any intellectual task that a human can do. While AGI remains a subject of debate, its potential to transform research is significant. The article concludes by emphasizing the need for material scientists to adapt to the changing landscape, acquiring new skills and embracing AI to remain competitive in the field. The role of material scientists in the age of AI is expected to evolve, with a greater emphasis on data science, theoretical understanding, and the ability to formulate meaningful research questions.