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Blog · Review Essay · Last reviewed June 15, 2026

The AI Con and the Hype Machine

Emily M. Bender and Alex Hanna's The AI Con is a book about language as a control surface. Its central claim is not that every machine-learning system is fake. It is that the public label \"AI\" often converts uncertainty into deference: automation becomes inevitability, extraction becomes progress, and ordinary managerial choices become the supposed demands of the future.

The Book

The AI Con: How to Fight Big Tech's Hype and Create the Future We Want was published by Harper on May 13, 2025. Amazon lists the hardcover at 288 pages, with ISBN-10 0063418568 and ISBN-13 978-0063418561. HarperCollins describes the book as technology criticism about systems sold as artificial intelligence, the drawbacks of systems sold under that banner, and the way hype can cover concentrated power.

The authors bring complementary forms of authority to the argument. Bender is a University of Washington linguist and computational linguistics professor whose earlier work helped make scale, data, language, and meaning central questions in large-language-model criticism; the site's Emily M. Bender wiki page tracks that broader record. Hanna is a sociologist of technology, labor, and politics and Director of Research at the Distributed AI Research Institute. The book grows from that pairing: language critique joined to institutional critique.

The title's \"con\" should be read narrowly and carefully. The claim is not that statistics, machine learning, pattern recognition, or automation are imaginary. The con is the substitution of a brand category for a demonstrated system: a word that bundles unlike tools, hides tradeoffs, transfers authority to vendors, and asks the public to accept deployment before the task, evidence, limits, and affected people are visible.

Hype as Infrastructure

The book's strongest move is to treat hype as infrastructure rather than noise. A bad AI claim is not merely a mistaken sentence in a press release. It can help a company attract capital, discipline workers, influence procurement, weaken public skepticism, and make a contested deployment feel unavoidable. Once the slogan is installed, the product inherits borrowed authority before anyone has checked what the system actually does.

The mechanism is concrete. A demo becomes a news story; the news story becomes a budget line; the budget line becomes a procurement requirement; the procurement requirement becomes a dashboard; the dashboard becomes the institution's memory of what happened. By the time harmed users, workers, teachers, patients, applicants, or citizens meet the system, the argument has already moved from \"does this work?\" to \"how do we adapt to it?\" That is the same loop described in the site's review of The Hype Machine and in When the Benchmark Becomes the Curriculum: attention, measurement, and incentive systems train the reality they claim only to report.

That is why The AI Con belongs beside AI Snake Oil, Empire of AI, and Atlas of AI. Each book attacks a different layer of the same machine. Narayanan and Kapoor ask whether the claim is empirically supported. Hao follows the institution and its supply chain. Crawford maps the extraction behind the model. Bender and Hanna focus on the rhetorical gate that lets the others proceed: the moment a product becomes \"AI\" and starts receiving deference it has not earned.

Language, Labor, and Authority

Bender and Hanna are especially useful on anthropomorphic language. Calling a system a colleague, tutor, doctor, lawyer, artist, or intelligence does not only decorate it. The label changes the user's expectations, the buyer's tolerance for opacity, and the worker's place in the workflow. A generated answer starts to look like judgment. A statistical association starts to look like insight. A labor-saving device starts to look like a moral upgrade.

The OECD's current definition is useful here because it is operational rather than mystical: an AI system is a machine-based system that infers from inputs how to generate outputs such as predictions, content, recommendations, or decisions that can influence environments. That definition does not settle whether a deployment is wise, fair, lawful, or worth buying. It pulls the conversation back to inputs, outputs, autonomy, adaptiveness, and context. Bender and Hanna's language critique does the same cultural work: keep the noun small enough that evidence can catch it.

The labor argument is equally important. Hype often presents automation as if it arrives from nowhere, but actual AI products rely on datasets, moderation, annotation, evaluation, customer-service scripts, benchmark construction, repair work, and the institutional labor of fitting a system into a workplace. When the product is framed as autonomous intelligence, the people who made it usable disappear twice: first behind the interface, then behind the story that the interface is replacing them. The companion reviews of Feeding the Machine and Ghost Work fill in that hidden production chain.

That makes The AI Con a book about belief formation. The con is not that every machine-learning system is useless. The con is that a flexible public word can collapse unlike systems into one aura of inevitability. Search ranking, synthetic media, resume screening, customer chatbots, welfare triage, code assistants, and agentic workflow tools become one mythic object, and the myth then speaks on behalf of each product.

Evidence Discipline

The most practical response is a claim ledger. Every AI claim should name the task, deployment context, affected population, data source, human labor, benchmark or field evidence, baseline comparison, failure mode, appeal path, security boundary, monitoring plan, and accountable owner. Without that ledger, \"AI-powered\" is not a capability statement. It is a request for exemption from ordinary proof.

This is where the book connects to claim hygiene, AI evaluations, model and system cards, and algorithmic transparency. A benchmark score can be evidence, but it is not a governance answer by itself. A product demo can be useful, but it does not show distribution shift, edge cases, access barriers, labor displacement, appeal burden, or long-term institutional dependence. A responsible review asks how the system behaves when incentives, users, data, and oversight differ from the launch video.

The Governance Reading

The governance value of the book is practical: slow the word down. Before accepting an AI claim, ask what is being automated, what inputs are used, who labeled or supplied them, what output is produced, what evidence shows it works, who benefits, who can appeal, and what happens when the system is wrong. Those questions line up with the site's recurring concern that machine-readable authority needs contestable records, not stage magic.

Public institutions have begun naming adjacent problems. NIST's Generative AI Profile treats generative-AI risk as a lifecycle problem for design, development, use, and evaluation, not as a matter of impressive output alone. The FTC's Operation AI Comply made deceptive AI claims and AI-enabled deception an enforcement target in 2024, including claims that an \"AI lawyer\" could substitute for professional legal expertise without adequate evidence. The EU AI Act, published in the Official Journal in 2024, requires transparency for certain AI interactions and synthetic outputs, and Article 13 requires high-risk systems to provide information that lets deployers interpret outputs and use systems appropriately. Those documents do not reproduce Bender and Hanna's politics, but they confirm the central premise: hype is now a risk surface.

The safety implication is not only consumer protection. Inflated claims can move authority into systems before human oversight, incident review, security boundaries, and appeal rights exist. That matters for hiring, education, public benefits, law, health, finance, journalism, and workplace management. The useful internal links are AI governance, AI audits and assurance, human oversight, notice and appeal, and duty of care for AI platforms.

Where the Book Needs Care

The book is polemical by design, and that sharpness is part of its usefulness. Still, readers should avoid turning its critique into a reflexive refusal to evaluate real capability. Some AI systems are useful in narrow settings. Some accessibility tools, scientific workflows, translation aids, code assistants, and pattern-recognition systems can help when they are bounded, tested, documented, and accountable. The target is not computation. The target is the story that lets computation outrun evidence and consent.

The other limit is audience. The AI Con is built to arm citizens, workers, readers, and policymakers against manipulative language. It is not a technical manual for model evaluation, a full labor history of data work, a security framework for agentic systems, or a complete policy design for every AI sector. Its best use is as a front-door discipline: if the claim cannot survive plain questions about task, evidence, power, labor, and recourse, it should not get to hide behind the word \"AI.\"

What This Changes

The site studies how interfaces become institutions and how institutions train belief. The AI Con gives that study a compact warning: the first interface is often the label. Before a user ever meets a chatbot or agent, they meet a story about intelligence. That story can invite care, fear, reverence, obedience, purchase, resignation, or policy panic.

A responsible AI culture would not begin by asking whether the system is impressive. It would ask what social power is being rearranged by calling it intelligent. It would keep language ordinary until evidence earns stronger words. It would refuse the move from demo to destiny. Most of all, it would keep human labor, institutional incentives, data extraction, appeal rights, and shutdown authority visible at the moment hype tries to make them vanish.

Sources

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