A new sociology of humans and machines explores the complex interactions between humans and intelligent machines in social systems. As robots, bots, and algorithms become more prevalent, they influence communication, social interactions, economic transactions, and transportation. These systems require a new sociology to understand how humans and machines interact, how collective outcomes emerge, and how to design resilient human-machine communities. This Perspective synthesizes research on human-machine social systems, emphasizing the need for a new sociology that considers the complexity of these interactions. It highlights the importance of studying these systems using complex-system methods, designing AI for human-machine and machine-machine interactions, and regulating the ecological diversity and social co-development of humans and machines. Human-machine interactions are complex, with machines exhibiting predictable and persistent behavior, while humans are influenced by emotions, social norms, and cognitive biases. Humans may trust algorithmic advice more than human advice but may also avoid it if they perceive a threat to their decision-making autonomy. Humans behave more rationally and selfishly with machines, cooperating less and demanding more. In competitive scenarios, algorithms can improve market efficiency but may also lead to anti-competitive behavior. In coordination scenarios, bots can introduce randomness and movement to help human groups find better solutions. In cooperation scenarios, persistent prosocial bots can increase cooperation, especially when humans are more likely to imitate others. In contagion scenarios, bots can influence public opinion and emotional states, even if their direct influence is weak. In collective decision-making scenarios, algorithms can improve decision-making by introducing diversity and can enhance collective intelligence when combined with human judgment. The implications of these interactions are significant, as machines can both benefit and harm human-machine systems. Machines can be beneficial when they act to counteract human weaknesses, but they can also cause unintended consequences, such as polarization, emotional contagion, and conflict. Research is needed to understand these interactions and to design ethical and effective human-machine systems. This Perspective calls for a new sociology of humans and machines that integrates insights from social science, engineering, and policy. It emphasizes the need for interdisciplinary research, ethical considerations, and the development of resilient and adaptive human-machine systems. The goal is to better understand and manage the complex interactions between humans and machines in a rapidly evolving technological landscape.A new sociology of humans and machines explores the complex interactions between humans and intelligent machines in social systems. As robots, bots, and algorithms become more prevalent, they influence communication, social interactions, economic transactions, and transportation. These systems require a new sociology to understand how humans and machines interact, how collective outcomes emerge, and how to design resilient human-machine communities. This Perspective synthesizes research on human-machine social systems, emphasizing the need for a new sociology that considers the complexity of these interactions. It highlights the importance of studying these systems using complex-system methods, designing AI for human-machine and machine-machine interactions, and regulating the ecological diversity and social co-development of humans and machines. Human-machine interactions are complex, with machines exhibiting predictable and persistent behavior, while humans are influenced by emotions, social norms, and cognitive biases. Humans may trust algorithmic advice more than human advice but may also avoid it if they perceive a threat to their decision-making autonomy. Humans behave more rationally and selfishly with machines, cooperating less and demanding more. In competitive scenarios, algorithms can improve market efficiency but may also lead to anti-competitive behavior. In coordination scenarios, bots can introduce randomness and movement to help human groups find better solutions. In cooperation scenarios, persistent prosocial bots can increase cooperation, especially when humans are more likely to imitate others. In contagion scenarios, bots can influence public opinion and emotional states, even if their direct influence is weak. In collective decision-making scenarios, algorithms can improve decision-making by introducing diversity and can enhance collective intelligence when combined with human judgment. The implications of these interactions are significant, as machines can both benefit and harm human-machine systems. Machines can be beneficial when they act to counteract human weaknesses, but they can also cause unintended consequences, such as polarization, emotional contagion, and conflict. Research is needed to understand these interactions and to design ethical and effective human-machine systems. This Perspective calls for a new sociology of humans and machines that integrates insights from social science, engineering, and policy. It emphasizes the need for interdisciplinary research, ethical considerations, and the development of resilient and adaptive human-machine systems. The goal is to better understand and manage the complex interactions between humans and machines in a rapidly evolving technological landscape.