Theory of Machine is a conceptual framework for describing and analyzing people’s lay theories about the differences between human and algorithmic judgment and decision-making. Reminiscent of the Theory of Mind, it conceptualizes the idea of ascribing thought processes or mental states to algorithms. However, based on their own perceptions, from past experiences, and shaped by public media, people may conceive of humans and algorithms as functionally distinct ontological entities. Therefore, research on augmented judgment and decision-making should also focus on the differences between various algorithms in terms of (cognitive) abilities and behavior. A primarily algorithm-centric research agenda provides insights into a more fine-grained Theory of Machine that also distinguishes between different artificial intelligence (AI) systems. Accordingly, we provide empirical evidence from a study of N = 472 participants in a judge-advisor system showing that the interactive conversational interfaces of contemporary generative AI (i.e., ChatGPT) are effective in counteracting algorithm aversion, which often hinders the effective utilization of algorithmic advice. Indeed, both the opportunity to interactively engage with ChatGPT and the explanation it provides exert positive effects on algorithmic advice taking. However, making use of the opportunity to elicit an explanation does not further boost its positive effect on participants’ advice weighting. Thus, self-explanatory AI and the opportunity to interact with it constitute independent countermeasures to algorithm aversion. This finding not only highlights the potential of generative AI to improve augmented judgment and decision-making, but also poses a threat to human autonomy.