A common model of AI suggests that there is a single measure of intelligence, often called AGI, and that AI systems are agents who can possess more or less of this intelligence. Cognitive science, in contrast, suggests that there are multiple forms of intelligence and that these intelligences trade-off against each other and have a distinctive developmental profile. The adult ability to accomplish goals and maximize utilities is often seen as the quintessential form of intelligence. However, this ability to exploit is in tension with the ability to explore and to create world models based on that exploration. Children are particularly adept at exploration and model-building, though at the cost of competent action and decision-making. Human intelligence also relies heavily on cultural transmission, passing on information from one generation to the next, and children are also particularly adept at such learning. Thinking about exploration and transmission can change our approach to AI systems. Large language models and similar systems are best understood as cultural technologies, like writing, pictures and print, that enable information transmission. In contrast, our empirical work suggests that RL systems employing an intrinsic objective of empowerment gain can help capture the exploration and theory formation we see in both children and scientists. Empowerment learning, in particular, may help construct causal models both in childhood and in science.