Emily M. Bender and Alex Hanna. The AI Con. How To Fight Big Tech’s Hype and Create the Future We Want. Harper Books. 2025.
Chapter Reviewed: Chapter 7: Do You Believe in Hope After Hype?
Review by: Rachael Mulvihill, PhD Candidate in Literary & Cultural Studies , Carnegie Mellon University.
The AI hype bubble hasn’t burst – but it’s starting to leak. In “Do You Believe In Hope After Hype?,” Emily M. Bender and Alex Hana warn that even as enthusiasm for artificial intelligence continues to swell, its residue will be “sticky,” coating creative industries with a “thick, sooty grime of limitless tech expansionism” that seeps into classrooms, workplaces, and governance long before any reckoning happens (195). Writing from this exact contemporary moment, Bender and Hanna refuse inevitability narratives about OpenAI and insist that hype itself is a political force worth resisting.
This chapter is written for educators adapting syllabi, workers pressured to adopt new tools, policymakers chasing competitiveness, and general users navigating platforms increasingly shaped by opaque systems. Rather than positioning themselves as anti-technology, Bender and Hanna make their stance explicit and unapologetic: contemporary AI discourse functions to obscure labor exploitation and power consolidation behind promises of efficiency and salvation. “Do You Believe In Hope After Hype?” unfold across four sections that move from identifying hype, to interrogating language, to proposing governance frameworks, and finally to outlining strategies for resistance and refusal.
The first section offers readers a practical way to recognize hype. If critical information about how a system works is unavailable, Bender and Hanna argue, “that this a good indication that you’re looking at pure hype” (167). Rather than treating opacity as a technical inconvenience, they frame it as a warning sign. They urge readers to ask basic but destabilizing questions like, “what is being automated,” “how is the system evaluated,” “who benefits from this technology, who is harmed, and what recourse do they have?” (165-168). These questions matter because they interrupt what they call the “word-shaped noises,” that AI produces that displace real narratives about the material conditions, labor, and extraction that sustain them (165).
Language itself becomes a site of struggle in the second section. Bender and Hanna deliberately avoid metaphors that mystify or seek to humanize automated systems, refusing terms like “superhuman” or “hallucination.” As they write, “metaphors have power, they structure the frames of discourse, and they can subtly and insidiously encourage certain ways of understanding technology and the social systems it is embedded in” (167). Calling predictable system failures “hallucinations” shift responsibility away from companies and onto the illusion of intelligence or narrative inevitabilities.
The chapter then turns toward governance, emphasizing the importance of friction in information access. Seamlessness, they argue, overwhelmingly benefits corporations, not users (171). They outline three governing principles: enforce existing laws and do not exempt AI; establish bright-line rules for unacceptable uses of AI; and force companies to prove that their products are not harmful (176). Transparency and disclosure ground critique in actionable frameworks rather than abstraction and injustice.
As a PhD Candidate at Carnegie Mellon University, a leading institution in AI education and research, I’m particularly drawn to Bender and Hanna’s refusal of the narrative that our role is to “coddle” innovation rather than interrogate it (163)1. This expectation is especially familiar in academic and technical environments, where skepticism is often framed as obstruction. Bender and Hanna name this dynamic directly: “Clearly, AI boosters want to be unfettered by regulation that might constrain their ability to amass power and capital, but they also sometimes even argue that it’s a moral imperative to be able to innovate quickly, because (in their worldview) AI is going to save us all” (177). Speed becomes virtue, critique becomes failure, and refusal becomes unethical or ungrateful2.
What gives this argument its force is their insistence that innovation is already doing harm. “We’ve been subjected to accelerating usage of AI as a pretext to surveil, arrest, and deport people; accelerating environmental impact of data centers to run the AI systems; and hundreds of car crashes [sic] as innocent bystanders are subjected to informal beta tests of Tesla’s misleadingly advertised ‘full self-driving’ technology” (177-178). This makes clear that the costs of innovation are neither hypothetical nor evenly distributed. In this context, the demand that scholars, educators, and workers support innovation at all costs reads less like optimism and more like complicity. Bender and Hanna offer critique and permission to slow down, to question, and to refuse participation in systems designed primarily for accumulation rather than care.
While fans or repeat users of OpenAI may struggle with the suggestions that are posed, Bender and Hanna make it hard to argue for complacency when the stakes are so high. When they write, “has your friend posted AI-generated artwork on Instagram? Make fun of it… troll the hell out of them. It is your right… synthetic media is cheap and tacky. Let them know,” the provocation is intentional (171). It’s a reminder that resistance can be every day and social, not just structural. The humor sits alongside hard evidence of racism baked into models, environmental damage from massive compute, exploitative labor practices, and mental health harms affecting those subjected to AI systems.
Ultimately, this chapter frames refusal as a collective political responsibility. Drawing from movements such as the Luddites, Black and Indigenous resistance, and feminist organizing, Bender and Hanna situate technological opposition within long histories of struggle over labor, autonomy, and survival. Their claim is clear-cut: if you are in a position to resist, then you should be (190–191). The future depends on it.
One response to “CBR: The AI Con. Chapter 7: Do You Believe in Hope After Hype?”
Thank you for this excellent chapter review. It is very effective that you define your own position as a PhD candidate at CMU, and what GenAI–the hype and the actual functionality–mean for you. The refusal to “coddle” seems important for all of us, and esp someone embarking on a career. Very perceptive and well written.