There’s no shortage of speculation about what generative AI portends for culture: Visions range from a “dead internet,” in which bots produce the majority of online content, to utopic redistribution scenarios, in which universal basic income grants humans unprecedented creative leisure. But for more than a decade—before many people had ever heard of a large language model (LLM)—artist Trevor Paglen has been making work about what generative AI is already doing to culture. His incisive new book, How to See Like a Machine: Images After AI, distills key insights from his practice to make the case that mainstream understanding of images remains stuck in an outdated paradigm. The old paradigm is semiotic and human-centered: Our species treats images as “representations, signs, allegories, or metaphors” to interpret. The new paradigm—which doesn’t supplant the old one so much as add another, less immediately apparent, layer to it—is operational: “a universe of images made by machines for other machines,” whose goal is to shape reality rather than merely represent it. For Paglen, seeing like a machine entails recognizing how images serve as “activations”—“stimuli that trigger automated, preconscious, or affective responses”—within technical or cultural circuits. He believes this recognition moves the critical question away from “What does this image say?” and toward “What does this image do?” Paglen acknowledges that the latter question is not new. Over the past half-century, media theorists such as Vilém Flusser and Paul Virilio, as well as artists such as Harun Farocki and Hito Steyerl, have perceptively addressed similar questions. Indeed, image activations “have existed throughout all known human history and across every culture,” as in the use of icons in premodern rituals. What’s new, according to Paglen, is the contemporary technological environment. He contends that in the past decade or so, “we have witnessed two great upheavals in