The public argument about artificial intelligence often divides into two grand stories. One presents AI as the next stage of progress, built by unusually intelligent technologists who are preparing humanity for abundance or even superintelligence. The other, represented by the documentary Ghost in the Machine, tells a darker story of AI as the latest expression of eugenics, colonial extraction, corporate power, environmental burden, and authoritarian fantasy. Both stories contain truths. Both also become misleading when they harden into total explanations.
A better posture toward AI is neither faith nor refusal, but selective adoption under conditions of institutional pluralism. AI can be genuinely useful without its mythology being true. Its historical origins can contain morally disturbing elements without rendering every statistical or computational tool illegitimate. Its builders can be brilliant without possessing privileged knowledge of humanity’s future. Its critics can expose real exploitation without offering a complete theory of what AI fundamentally is. The task is to judge claims by evidence, attend to hidden labor and material costs, resist inevitability rhetoric, and preserve the human and institutional frictions that technology too easily removes.
The Limits of Genealogy
Ghost in the Machine is strongest when it refuses the fantasy that AI is an immaterial intelligence floating in the cloud. AI depends on capital, electricity, water, computing infrastructure, mined materials, human-generated data, and human labor. PBS describes the documentary as examining the values, biases, power structures, and societal and environmental consequences behind AI. That frame is valuable because it redirects attention from the magic of the interface to the institutions that make the interface possible.
But the documentary is weaker when it treats lineage as destiny.
Francis Galton and Karl Pearson were entangled with eugenics; Charles Spearman’s work on intelligence emerged in a hereditarian intellectual environment; IQ testing and statistical methods were used in projects that caused profound harm. These histories matter. They warn us that measures of “intelligence,” “fitness,” or “capacity” are never innocent merely because they are numerical. Yet the moral genealogy of an instrument and the validity of its later uses are not the same question. Correlation does not become eugenic when used to study vaccine uptake. Logistic regression does not transmit Pearson’s politics into epidemiology. Statistical tools developed in morally compromised environments can be repurposed for legitimate scientific ends.
The stronger criticism is not that every tool touched by eugenics is contaminated, but that constructs such as intelligence require continuous scrutiny. What exactly is being measured? Whose capacities count? Which tasks define the comparison? What inferences are justified? These questions becomes urgent when AI companies announce progress toward “human-level intelligence.”
Human abilities are radically heterogeneous, spanning writing, mathematical reasoning, spatial navigation, caregiving, social judgment, experimental design, persuasion, craft, and moral discernment. These capacities are not one thing. Claims about artificial general intelligence therefore depend on how the construct is defined, tested, and compared. Pointing to Galton is not enough. The harder task is to examine whether today’s benchmarks and metaphors smuggle in narrow assumptions about what intelligence is.
Prophecy, Power, and Uncertainty
The more unsettling feature of contemporary AI culture is not simply optimism, but the way some forecasts acquire the tone of historical inevitability. We are told that AGI is coming, superintelligence will follow, and the institutions building these systems therefore bear extraordinary responsibility for shepherding humanity through a transition unlike any before it. The structure resembles religious eschatology because it imagines a transformative event approaching, ordinary history becoming discontinuous afterward, and a particular community possessing unusual insight into the coming order.
The analogy should not be overused. A forecast is not false because it resembles prophecy. AI capabilities can be measured in ways eschatological claims cannot, and systems have improved on many concrete tasks. It is plausible that future models will outperform humans across large domains of economically important work. But uncertainty about that future should not be converted into authority in the present. The question is not whether superintelligence is impossible. The question is why a highly uncertain technological forecast should be treated as inevitable, especially when the institutions advancing the forecast gain capital, political access, and legitimacy from society believing it.
Venkatesh Rao’s critique of effective altruism sharpens this point. Rao argues that parts of the AI-safety world are skilled at quantifying uncertainty inside a model while remaining less exposed to uncertainty about the model itself, including what counts as value, which futures matter, how probabilities should be assigned, and who is entitled to organize society around those calculations. A claim such as “35 percent probability of superintelligence by a given year” sounds humble, but much has already been assumed before the number appears.
This critique applies to AI’s critics as well. “LLMs can never become generally capable” is also a prediction. “AI inevitably becomes fascism” is as deterministic as “AI inevitably becomes salvation.” The antidote to one totalizing narrative is not another totalizing narrative. It is disciplined pluralism in which competing traditions are strong enough to expose one another’s blind spots.
The People and Places Behind the Interface
The documentary’s most important contribution is its insistence that AI’s apparent autonomy hides human work. TIME reported in 2023 that OpenAI used outsourced Kenyan workers earning less than $2 per hour to help make ChatGPT less toxic by labeling disturbing content for safety systems. That labor did not mean that users were secretly chatting with Kenyan workers. It was mediated through data annotation, safety labeling, preference judgments, classifiers, and model-training pipelines. Still, the labor was causally implicated in the product, and its invisibility is part of the product’s smoothness.
This distinction matters. “AI depends on substantial and often invisible human labor” is true and morally important. “AI is merely repackaged human labor” is a stronger claim and does not follow. The first claim directs us toward labor standards, disclosure, psychological support, contractual accountability, and global inequality. The second can obscure what is computationally novel about the systems and therefore weakens criticism by overstating it.
The same is true of material costs. Data centers consume energy and water in particular places, under particular political arrangements, affecting particular communities. Aggregate comparisons can mislead if they erase local scarcity or unequal voice. A serious politics of AI must therefore include data workers, content moderators, communities living near infrastructure, and people in the Global South whose labor and resources are absorbed into systems marketed as frictionless intelligence.
Chesterton’s Fence and the Meaning of Friction
Part of the concern about AI is not only what it does badly, but what it does too well. AI makes many activities cheaper, including drafting, summarizing, tutoring, searching, coding, designing, and evaluating. When a task becomes cheaper, the temptation is to automate it. But before removing a human practice, we should ask what else that practice was doing.
A professor is not merely an inefficient mechanism for transmitting information. A physician is not merely a diagnostic classifier. A university is not merely a bundle of courses. A conversation is not merely information exchange. A writer is not merely a mechanism for producing text. Institutions often perform functions that are invisible when reduced to measurable outputs, such as apprenticeship, judgment, trust, accountability, identity formation, professional norms, and shared attention.
This applies personally as well. LLMs reduce the transaction costs of sustained intellectual engagement. A question produces an answer, which produces three more questions, which produce more answers. That is exhilarating and useful. It can also be exhausting. Some vanished frictions may have been doing cognitive work by making us wait, forget, draft slowly, sit with uncertainty, and struggle to formulate a problem before receiving assistance. The issue is not which frictions AI can remove, but which ones we should want removed.
Pluralism Against Moral Monopoly
Rao’s most useful concept is “moral-monopoly safety,” the danger that one faction acquires the authority to define AI’s meaning, risks, and future for everyone else. Effective altruists may notice catastrophic tail risks that others neglect. Engineers may understand technical possibilities that critics underestimate. Political economists may see ownership and power. Historians may recognize institutional fragility. Labor scholars may uncover exploitation. Anthropologists may expose culturally provincial assumptions. Accelerationists may remind us that suppressing useful technology has costs.
No single group should possess epistemic sovereignty over the future. But pluralism must not mean a debate among only articulate, well-funded factions. If data workers, infrastructure-hosting communities, ordinary users, public institutions, and non-Western societies are absent, pluralism becomes another elite settlement. The question is not simply which worldview is correct, but what institutional arrangements prevent any worldview from monopolizing power.
The practical test should be local and comparative. We should ask what AI enables here relative to the best non-AI alternative, what labor, resources, and institutional capacities it consumes, what forms of agency it augments, and what forms it transfers away. An LLM used to interrogate an argument may expand intellectual agency. An LLM used to produce an argument the user never worked through may diminish it. AI that helps a physician inspect evidence may strengthen judgment. An opaque system that makes consequential decisions no one can contest may evacuate judgment from institutions.
The right response to AI is therefore not blanket acceptance or blanket refusal. It is selective, evidence-sensitive use under democratic and intellectual constraint. Use AI where it demonstrably expands human capability. Investigate the labor and infrastructure that make it possible. Treat forecasts of inevitability as forecasts, not revelations. Preserve institutions and practices whose value exceeds their measurable output. And before removing a human friction, ask whether it was merely an obstacle—or part of thinking itself.