The Great Mystification of AI

The Great Mystification of AI

How the AI Narrative Manufactures Hype, Distorts Perceptions, and Masks Market-Driven Technology Cycles

Author: Monica Bianco, Ecosystems Cooperation advisor -CRF Italy

Abstract

Artificial Intelligence (AI) has become one of the most potent myths of the 21st century. Far from representing a genuine form of autonomous intelligence, today’s so-called AI systems are statistical tools developed and steered entirely by human cognition and purpose. The term “AI” itself is a product of careful narrative construction, designed to inflate expectations, attract investments, and shape public perceptions. This article critically analyzes how the myth of AI has been manufactured, why it misleads societies about the real nature of technological change, and how a market-driven approach sacrificed genuine innovation for the rapid commodification of probabilistic models. Recognizing the mystification surrounding AI is essential to restoring a realistic, responsible approach to digital innovation.

Introduction

There is no such thing as artificial intelligence. There is human intelligence, and there are artifacts created by humans — sophisticated, impressive, but fundamentally inert without human design, human interpretation, and human meaning. As Marcus (2022) argues, “what is currently branded as AI is, in truth, little more than advanced statistical interpolation across enormous datasets” [1]. Yet the mystification persists, fueled by an uncritical media landscape and a market hungry for futuristic promises.

The very choice of the term “Artificial Intelligence” was not neutral. It evokes images of sentient machines, autonomous decision-makers, and science fiction futures. Had these technologies been honestly named — as “statistical pattern recognition tools” or “advanced computational systems” — they would not have mobilized public fascination or multibillion-dollar investments. The mystification lies not in the tools themselves but in the deliberate framing that obscures their true nature.

Narrative Construction: How AI Became a Symbol

The AI boom has been less a technological inevitability than a carefully engineered communication phenomenon. Naming, framing, and repetition across media, politics, and academia created an illusion of imminent, autonomous machine intelligence. As Crawford (2021) writes, “AI is less a technical field than a political and social project to automate inequality and power asymmetries” [2].

This mystification serves distinct purposes. In the public imagination, “AI” promises transcendence: machines capable of surpassing human limitations. In the market, “AI” promises disruption: new territories to colonize with products, patents, and profits. In the media, “AI” delivers a perpetual spectacle of revolution, innovation, and impending doom, all of which sustain attention economies.

Critically, those who speak loudest about “AI ethics” — pundits, executives, policymakers — often lack even basic technical literacy. Few understand the inner workings of language models, computer vision systems, or reinforcement learning architectures. Yet their pronouncements shape societal debates, policy directions, and funding priorities. As Pasquale (2020) notes, “the myth of AI has allowed corporations to displace responsibility onto ‘algorithms’ while insulating themselves from public accountability” [3].

Market Forces and the Betrayal of Genuine Innovation

The AI narrative was also shaped by strategic technological choices driven by market imperatives. In the early 2010s, a crossroads appeared: pursue slow, foundational research into cognitive architectures (adaptive, embodied, developmental AI) or invest massively into deep learning — a scalable, data-hungry statistical paradigm promising immediate applications.

The choice was clear. Deep learning could produce marketable outputs: recommendation engines, speech recognition, predictive analytics. Investment flooded into this domain. As Bender et al. (2021) explain, “large language models demonstrate proficiency in surface-level pattern reproduction, not in semantic understanding or reasoning” [4].

Thus, the myth of intelligent machines was built atop infrastructures that fundamentally lacked cognitive capabilities. But they sufficed for commercial needs: selling AI-as-a-service, automating advertising, generating synthetic media, predicting consumer behavior. The AGI (Artificial General Intelligence) dream, if ever genuinely pursued, was sacrificed to the altar of quarterly profits.

Digital Infrastructure as Utility, Not Value Creator

Another critical mystification concerns the role of digital technologies themselves. Too often, digitalization is portrayed as an intrinsic source of value. In reality, as Lanier (2023) argues, “digital infrastructures are utilities; they generate value only when embedded in social, cultural, and economic contexts capable of transforming information into action” [5].

The digital, including AI, does not automatically produce growth, inclusion, or sustainability. On the contrary, in regions lacking education systems, industrial ecosystems, or institutional capacities, digitalization can exacerbate exclusion and dependency. Technology amplifies existing inequalities more often than it corrects them.

Mariana Mazzucato (2021) reinforces this view: “Value creation requires mission-driven engagement, public-private collaboration, and societal directionality — technology alone is not enough” [6]. Thus, AI, stripped of its mystique, is revealed for what it is: a tool. Powerful, yes. But inert without human intelligence, cultural frameworks, and democratic governance.

Conclusion: Toward De-Mystifying the Digital

Recognizing the great mystification of AI is not an exercise in pessimism but a necessary act of intellectual honesty. If society continues to project intelligence onto tools, it risks abdicating responsibility for their design, deployment, and consequences.

There is no autonomous intelligence lurking in servers or circuits. There are only tools, designed by human minds, governed by human choices, producing human consequences.

Only by stripping away the mythology can we build a future where digital technologies genuinely serve human dignity, societal needs, and planetary sustainability — rather than merely fueling speculative cycles of hype and disappointment.

References

1. Marcus, G. (2022). Rebooting AI: Building Artificial Intelligence We Can Trust. Pantheon.
2. Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press.
3. Pasquale, F. (2020). New Laws of Robotics: Defending Human Expertise in the Age of AI. Harvard University Press.
4. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” Proceedings of the ACM Conference on Fairness, Accountability, and Transparency.
5. Lanier, J. (2023). Who Owns the Future?. Simon & Schuster (updated edition).
6. Mazzucato, M. (2021). Mission Economy: A Moonshot Guide to Changing Capitalism. Penguin Books.