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Effective public engagement with complex technologies requires a nuanced understanding of how different audiences make sense of and communicate disruptive technologies with immense social implications. Using latent class analysis (LCA) on nationally-representative survey data (N = 2,700), we examine public attitudes on different aspects of AI, and segment the U.S. population based on their AI-related risk and benefit perceptions. Our analysis reveals five segments: the negative , perceiving risks outweighing benefits; the ambivalent , seeing high risks and benefits; the tepid , perceiving slightly more benefits than risks; the ambiguous , perceiving moderate risks and benefits; and the indifferent , perceiving low risks and benefits. For societal debates surrounding a deeply disruptive issue like AI, our findings suggest potential opportunities for engagement by soliciting input from individuals in segments with varying levels of support for AI, as well as a way to widen representation of voices and ensure responsible innovation of AI. • We classify Americans' AI perceptions into five segments: negative, ambivalent, tepid, ambiguous, and indifferent classes. • Views of AI vary both by the level of news attention and the content audiences attend to. • The negative and the ambivalent classes largely differ in support for AI, but agree that their voices should be heard. • The indifferent and the ambiguous classes include more minorities who may be disproportionately affected by AI. • Now is a great time to engage with the publics on issues related to AI because it is not overtly politicized. [ABSTRACT FROM AUTHOR]