AI TOOLS, NOT GODS

AI TOOLS,

NOT GODS

WHY ARTIFICIAL INTELLIGENCE

HYPE THREATENS GLOBAL

GOVERNANCE—AND HOW TO FIX IT

Caroline De Cock

BTF Press | Antwerp, Belgium

9789083616803 (paperback)

9789083616810 (eBook)

Published by BTF Press, 2026

publishing@btfpress.org

This work is licensed under a Creative Commons CC0 1.0

Universal (CC0 1.0) Public Domain Dedication license.

E

To my family.

And to all the chatty, sycophantic bots that

supported my book-writing journey by mindlessly

telling me how amazing I am. Your enthusiasm

was unbelievable. Literally unbelievable.

Contents vii

CONTENTS

Foreword ��������������������������������������������������������������������������������������������������������������� ix

Introduction ������������������������������������������������������������������������������������������������������������1

PART I THE GENESIS �����������������������������������������������������������������������7

CHAPTER 1 The Narrative Machine ������������������������������������������������������������������������ 9

CHAPTER 2 The Policy Machine ��������������������������������������������������������������������������� 27

PART II THE MYTHS �����������������������������������������������������������������������43

CHAPTER 3 Myth #1—Magic ��������������������������������������������������������������������������������� 45

CHAPTER 4 Myth #2 – Madness ��������������������������������������������������������������������������� 78

CHAPTER 5 Myth #3 – Heaven �����������������������������������������������������������������������������100

CHAPTER 6 MYTH #4 – Sin �����������������������������������������������������������������������������������127

PART III The Cost of Policy Hallucinations ������������������������������������ 145

CHAPTER 7 The Wreckage of Myth-Based Governance �������������������������������������147 viii AI TOOLS, NOT GODS

PART IV A Blueprint for AI Governance ����������������������������������������� 167

CHAPTER 8 The Foundation: from Graveyard to Blueprint ��������������������������������169

CHAPTER 9 Principles for Mature AI Governance �����������������������������������������������180

Epilogue ���������������������������������������������������������������������������������������������������������������215

Endnotes �������������������������������������������������������������������������������������������������������������219

Bibliography ��������������������������������������������������������������������������������������������������������271

Acknowledgments ����������������������������������������������������������������������������������������������319

About the Author ������������������������������������������������������������������������������������������������321

Foreword ix

FOREWORD

BY BREWSTER KAHLE

E very major digital transition begins not with tech-nology, but with imagination. Before systems are built, they are pictured, described, and debated through metaphors. Libraries, frontiers, highways, cy-

berspace. These images shape what we believe technol-

ogy is for, who it should serve, and what kind of future

it makes possible.

In the 1990s, the United States experienced a surge of

experimentation that would shape the digital world for

decades to come. Search engines emerged. User-con-

tributed platforms like GeoCities brought millions of

people online for the fi rst time. A new public digital

space was being built, imperfect, chaotic, but open.

Much of the rest of the world, Europe, Canada, Japan,

and others, took a diff erent path. Not because of a lack

of talent, funding, or imagination, but because poli-

cy choices shaped what could be built, and by whom.

Regulatory uncertainty, litigation risk, and deference x AI TOOLS, NOT GODS

to incumbent interests made large-scale experimenta-

tion harder. Entrepreneurs hesitated. Investors looked

elsewhere. Over time, entire categories of public-facing

digital infrastructure simply did not emerge.

Policy matters, not because it predicts innovation, but

because it creates the conditions under which innova-

tion can happen.

Today, generative AI offers another inflection point.

For the first time in years, the dominance of search and

information intermediaries is no longer inevitable. New

tools make it possible to imagine systems that are more

plural, more local, more publicly oriented, and better

aligned with education, research, and cultural access.

Some jurisdictions are beginning to act with that future

in mind. Japan, for example, has chosen a clear and

pragmatic approach, allowing AI systems to learn from

copyrighted works while holding outputs to account.

In other words, enable learning, evaluate results. This

creates space for innovation while preserving account-

ability, a balance that invites builders to build.

Europe stands at a similar crossroads. It has world-

class researchers, strong public institutions, and a deep

commitment to cultural heritage and education. It also

has a unique opportunity to design AI policy that sup-

ports openness, competition, and democratic access to

knowledge. Narrow exemptions and hesitant signals,

however well-intentioned, are not enough to unlock

that potential, especially when institutions tasked with

preserving culture remain unsure whether they are tru-

ly allowed to use the tools now available to them.

Foreword xi

History offers lessons here. Canada once developed

OpenText, an early and promising search technology.

Rather than scaling it into a public-facing infrastruc-

ture, it retreated into safer, narrower applications, and

the opportunity passed. Europe can choose differently

this time.

The question is not whether generative AI should ex-

ist, but what kind of information ecosystem we want it

to support. One shaped primarily by incumbency and

caution. Or one that empowers libraries, schools, mu-

seums, researchers, startups, and citizens to participate

fully in the digital transition.

No one knows these battles better than Caroline De

Cock, who has spent decades pursuing an open web

in Brussels, the European Union's centralized location

for these regulations. The public interests embodied in

our schools, libraries, and museums exist to expand ac-

cess to knowledge and to help societies learn, especially

in moments of technological change. With thoughtful,

confident policy design, those institutions can help

ensure that AI strengthens, rather than narrows, our

shared intellectual commons.

Europe has an opportunity not just to regulate genera-

tive AI, but to imagine what it could become, a tool for

learning, creativity, resilience, and democratic partici-

pation. The choices made now will shape its cultural,

economic, and security landscape for years to come.

Policy matters.

Brewster Kahle

Founder of the Internet Archive

Internet Hall of Fame Inductee

Introduction 1

INTRODUCTION

WORDS MATTER� WORDS CREATE WORLDS�1

W e don’t encounter technology as a blank space. We meet it with a mind already crowded with metaphors, analogies, and inherited narratives that shape what we see before we know we’re seeing it.

As cognitive linguist George Lakoff argues,2 these ar-

en’t just colorful linguistic fl ourishes, they are the very

architecture of our reasoning. Reason, he explains, is

98% unconscious, governed not just by logic—if at

all—but also by neural circuits called “frames”. And

words are the keys that unlock these frames. A single

term can summon an entire worldview, complete with

its own logic, emotions, and moral assumptions, all be-

fore our conscious mind has a chance to object. This

book is about those keys, the frames they unlock, and

how our laws are being written by stories we don’t even

realize we’re telling: the story of us.

But before examining the narratives shaping our un-

derstanding of technology, we need to grapple with a 2 AI TOOLS, NOT GODS

basic linguistic question: what do we mean when we

say “Artificial Intelligence?” The term “AI” is routinely

used to describe a dizzying variety of technologies (gen-

erative models, statistical classifiers, recommender sys-

tems, predictive analytics, and more) that share little in

common beyond the vague promise of computational

cleverness. It’s basically a branding label masquerading

as a technical definition.

In fact, as U.S. journalist Karen Hao documents in Em-

pire of AI, the term itself was a marketing tool from

its inception.3 When U.S. cognitive scientist and one of

the founders of AI, John McCarthy, convened the sem-

inal Dartmouth Summer Research Project on Artificial

Intelligence in 1956, he swapped the less exciting term

“automata studies” for “artificial intelligence” precise-

ly because it was more evocative and better at attract-

ing interest and funding.4

Using “AI” as an umbrella term amounts to calling ev-

erything with buttons a “controller”, whether it’s a TV

remote, a pacemaker programmer, or a nuclear launch

panel, and then trying to draft legislation on the safety

and social impact of said controllers. The media, ever

helpful, would leap into the void with headlines like

“Controllers: Existential Threat or Next-Gen Conve-

nience?” Talk shows would stage theatrical debates

where one guest warns of imminent nuclear annihila-

tion, another enthuses about ad-skipping convenience

during The Great British Bake Off, and a third at-

tempts (futilely) to explain that their “controller” just

helps manage heart arrhythmias in elderly patients. As

a result of these supposedly nuanced and fact-based

Introduction 3

debates, public understanding would swing wildly be-

tween gadget fetishism and apocalyptic dread, depend-

ing entirely on which controller a person had in mind.

As the authors of AI Snake Oil, which aims to debunk

the AI hype, observe, most of the true “snake oil” is

concentrated in the realm of predictive AI: systems

that claim to make high-stakes judgments about peo-

ple’s lives, from loan applications to hiring decisions.5

These tools are often sold on the promise of superhu-

man foresight, a claim that rarely withstands scrutiny.

Generative AI, like the large language models (LLMs)6

that have captured the world’s attention, is different. Its

progress is remarkable, but as a product, it is still im-

mature and unreliable, often functioning as what some

have aptly called a “bullshitter”—a system designed

to produce plausible text rather than necessarily true

statements.

We fall into the trap of debating AI as a single entity

when we should be having different conversations about

distinct tools. However, this is precisely what happens

when we bundle ChatGPT with facial recognition, mil-

itary drones, and supply chain algorithms under a sin-

gle, mythically loaded two-letter acronym. The ensuing

discourse is so broad it often collapses into caricature.

Grouping together vastly different applications under

one label isn’t just analytically sloppy, it’s politically

dangerous. It trivializes genuine risks, demonizes useful

tools, and traps policymakers in a tug-of-war between

sci-fi panic and PR-driven techno-optimism. Yet that is

the AI discourse picked up by media and policy.

4 AI TOOLS, NOT GODS

Such a terminological mess matters, not just because it

creates confusion, but because it actively shapes how

we think about the risks, benefits, and governance of

these technologies. So before we talk policy, we need to

talk about the stories that shape our headlines and our

laws. Because AI isn’t just built by developers. What we

recognize as AI is being shaped through four powerful

myths that inform how we see it, sell it, and surren-

der power to it. We “story” AI into being. Metaphors

become policies, myths become institutionalized, and

democratic agency erodes slowly when governance is

built on ill-informed fantasy rather than facts. Hence,

“AI” is more than the buzzword du jour. How we con-

ceive of AI is important because its rapid evolution is

the ultimate stress test for our traditional methods of

governance.

AI is the focus of this book because it serves as the

perfect contemporary case study, a crucible in which

our old, technology-specific regulatory habits are be-

ing tested and found wanting. The patterns of hype,

fear, and institutional capture we see today echo the

histories of the internet, nuclear power, and many other

technologically driven or facilitated shifts. By dissect-

ing the myths surrounding AI, we can forge the du-

rable, technology-neutral principles needed to govern

whatever comes next.

In Part I: The Genesis, we trace the origins of these

myths, from the “Wild West” of the early internet to

President Eisenhower’s glowing faith in “Atoms for

Peace.” We then examine the policy machine itself—the

institutional pipeline of academia, media, and politics

Introduction 5

that translates these compelling stories into concrete

laws.

Part II: The Myths, dissects the four great illusions gov-

erning AI. We explore the myth of Magic, which cloaks

technology in wonder to deflect accountability; the

myth of Madness, which swings between manic gold

rushes and paralyzing moral panics; the myth of Heav-

en, which promises salvation from our human failings;

and the myth of Sin, which casts the algorithm as a

digital scapegoat when that salvation fails.

Part III: The Cost of Policy Hallucinations, takes stock

of the real-world damage. When governance is built on

myth, we suffer predictable failures: policy distraction

that chases phantoms, governance whiplash from knee-

jerk reactions, and the erosion of democratic control as

we cede accountability to prophets and bots.

Part IV: A Blueprint for AI Governance, provides the

antidote. It unbuilds what doesn’t serve us, moving

beyond the graveyard of obsolete, technology-specific

laws to set out a practical, technology-neutral blueprint

built on three principles: Accountability, Proportionali-

ty, and Durability.

Finally, the Epilogue calls for an “Age of Unenchant-

ment,” urging us to reject technological determinism

and embrace the patient, necessary work of democratic

governance.

Taken together, these explorations show that technol-

ogy is never just engineered, it’s narrated into being. If

we want governance rooted in reason rather than illu-

sion, we must begin by choosing our words as carefully 6 AI TOOLS, NOT GODS

as our tools. Because before we can write better laws,

we need to tell better stories.

Introduction 7

PART I

THE GENESIS

Before we can challenge the myths that govern AI, we

must understand their origins. Myths don’t emerge

from thin air: they are born from language, history,

and the human need to make sense of change. This part

traces the origins of the narratives that shape our view

of technology, from the frontier dreams of the early

internet to the atomic faith in progress. To understand

today’s AI discourse, we must return to the moment of

creation, when metaphor became machinery and sto-

ries began to rule our tools.

Discover how the story begins.

8 AI TOOLS, NOT GODS

HOW METAPHOR BECOMES AUTHORITY

U sing myths to shape technology policy isn’t new; it’s a recurring pattern, especially for transfor-mative technologies. The framing follows a well-worn playbook. By examining the historical precedents

set by the internet and nuclear power, we can identify

the mechanisms by which metaphors and narratives are

used to defi ne a technology’s purpose and establish its

regulatory boundaries.

A History of Metaphors in Policy A History of Metaphors in Policy

Internet as Wild West and Information Superhighway

The early policy debates surrounding the internet were

dominated by two powerful and often contradictory

metaphors, namely the internet as the Wild West and

an information superhighway. Conveniently, the two

metaphors could coexist in perfect contradiction, off er-

ing a narrative that was endlessly adaptable: you could

argue for tearing down regulations in one breath and 10 AI TOOLS, NOT GODS

for building digital tollbooths in the next, all without

missing a step.

The Wild West was a political metaphor that could

ride in any direction you wanted, and it did. For lib-

ertarians, tech founders, and assorted digital cowboys,

it represented a wide-open frontier promising endless

opportunity with no bureaucrats in sight. It offered

a system based on spontaneous order whereby com-

munities could set their own rules, stake their digital

claims, and make their fortune without a sheriff’s badge

flashing into view. They saw themselves as the miners

and cattlemen of cyberspace prospecting and building

homesteads as they exploited the potential of the in-

ternet. This version of what the internet represented

was rolled out whenever someone wanted to head off

regulation, whether it pertained to content moderation,

online marketplaces, or financial oversight.1

But for regulators, the Wild West wasn’t a land of op-

portunity, it was a saloon brawl on repeat—a lawless

frontier demanding the strong hand of a sheriff. Cue

calls for surveillance, stricter rules, and more legal

power.

That is where the political beauty of the metaphor lay:

you could use it to justify doing absolutely nothing…

or doing absolutely everything. The framing did not

stop there: it is alive and well today. Just look at how

cryptocurrency is branded as the Wild West that must

be tamed.2 Or if you are into cheesy statements, check

out the moment former European Commissioner Thi-

erry Breton introduced the EU’s Digital Services Act in

2022 with all the subtlety of a spaghetti western, star-

The Narrative Machine 11

ring the new piece of legislation as the “new sheriff in

town” and referencing a clip from The Good, the Bad

and the Ugly showing gun-toting bounty hunter Clint

Eastwood in a cowboy hat in the final confrontation.3

If the Wild West depicted the lawless frontier, the con-

cept of an Information Superhighway promised the op-

posite: gleaming asphalt, orderly lanes, and well-posted

exits. Popularized by the Clinton–Gore administration

in the 1990s, it framed the internet as critical nation-

al infrastructure—the digital sibling of the Interstate

Highway System.4 This was the policy equivalent of

swapping cowboy boots for a hard hat.

The highway metaphor put the internet’s economic

role front and center. It was all about speed, efficiency,

and the movement of “goods,” here meaning packets

of information.5 It aligned policy neatly with a com-

merce-first, marketplace model, where regulation was

about keeping the traffic flowing, not inspecting what

was in the trucks. As one contemporary analysis noted,

the emphasis was on “speed and quantity of goods…

transmitted, not the quality or content.”6 That framing

opened the door to massive public and private invest-

ment, but it also nudged priorities toward commercial

applications, quietly shunting community-building, ed-

ucation, and non-profit uses into the slow lane.7

It was a metaphor that also smuggled in some baggage.

Highways are linear, centralized, and predictable. The

internet, decentralized, messy, and allergic to straight

lines, was never going to fit neatly into such a traffic

plan. Yet the metaphor conveniently obscured those dif-

ferences, forcing the network into a familiar regulatory 12 AI TOOLS, NOT GODS

box designed for dedicated point-to-point transporta-

tion and traditional telecommunications systems.8 Still,

the image was reassuring, familiar, and—like all good

political metaphors—it told people exactly what they

wanted to hear while leaving plenty unsaid.

The history of internet policy shows that the most influ-

ential metaphors are rarely the most technically precise,

but rather those that best serve political and economic

interests. The Wild West and Information Superhigh-

way may seem to have pulled in opposite directions, but

both pushed the network along the same path: away

from the non-commercial, community-minded hacker

ethic of its early years and toward a system dominated

by private enterprise and commercial priorities. They

provided the language and framing necessary to trans-

form a publicly funded experiment into a marketplace.

More than a historical curiosity, it is a blueprint for

understanding today’s debate over AI. The words we

choose do more than describe new technologies; they

set the boundaries of acceptable policy and embed

particular economic and political ideologies into their

foundations from the outset.

Atoms for Peace vs. Nuclear Devastation

The introduction of nuclear technology to the world

was accompanied by a narrative duality of unprece-

dented extremity that seems familiar when compared

to AI narratives: it was presented simultaneously as the

ultimate tool of salvation because of its potential to

supply near-limitless energy and as the ultimate weap-

on of annihilation because of its capacity for destruc-

tion. This profound tension between a utopian myth

The Narrative Machine 13

of boundless energy and a dystopian myth of total de-

struction shaped decades of energy, military, and inter-

national policy. The nuclear case is perhaps the starkest

historical example of how policy can become complete-

ly captured by powerful, mythological narratives, often

leading to outcomes divorced from technical or eco-

nomic reality.

The utopian vision was masterfully crafted and de-

ployed by President Dwight D. Eisenhower’s Atoms for

Peace program, announced in his speech to the United

Nations in 1953.9 This initiative was a well-thought-

out act of “narrative engineering”, the deliberate craft-

ing of stories that make technological developments

sound inevitable, revolutionary, and beneficial, while

diverting attention from any possible downsides.10 In

this case, the objective was to reframe the terrifying

power of the atom, until then associated only with

the bombs dropped on Hiroshima and Nagasaki, as a

source of immense good for humanity. The program’s

central promise was of a future powered by clean, abun-

dant nuclear energy that would be “too cheap to me-

ter.”11 The slogan never became reality, but it captured

imaginations worldwide. The narrative of “Atoms for

Peace” positioned nuclear technology as a tool to alle-

viate global hunger and poverty, promote economic de-

velopment, and ultimately ensure peace through shared

prosperity. This utopian framing served a clear policy

goal: to build public acceptance for a domestic civilian

nuclear industry and to justify the massive government

investment and legislative changes required to bring it

into being. Sounds familiar? That’s because those who 14 AI TOOLS, NOT GODS

advocate for the benefits of AI follow the same play-

book.

The counter-narrative, of course, was the ever-present

threat of nuclear war. It was fueled by metaphors of

apocalypse, inferno, and devastation, which saturated

Cold War social discourse.12 The image of the mush-

room cloud became a globally recognized icon of hu-

manity’s capacity for self-destruction. The Bulletin of

the Atomic Scientists’ Doomsday Clock, introduced in

1947, served as a powerful and enduring visual met-

aphor, constantly ticking down the minutes to a nu-

clear apocalypse and reinforcing a sense of impending

doom.13 This narrative of existential risk went beyond

cultural background noise; it was the foundational log-

ic of Cold War geopolitical strategy. It justified the cre-

ation of vast nuclear arsenals, the doctrine of mutually

assured destruction (MAD), and a state of permanent

military readiness, while also fueling a powerful an-

ti-nuclear social movement that challenged this logic.14

Despite pulling in opposite directions, these two myths

produced a similar political outcome: control over nu-

clear technology was concentrated in the hands of a

small, insulated elite of scientists, military leaders, and

senior policymakers. This dynamic, where complexity

and high stakes justify sidelining public input, offers

a stark warning for today’s AI debates. The atom was

portrayed as too complex, too powerful, and too dan-

gerous for democratic governance. Whether deployed

for peace or for war, it was to be managed by a priest-

hood of nuclear experts holding the keys to both salva-

tion and damnation. The public’s role was reduced to

The Narrative Machine 15

cheering the dream or fearing the nightmare, but never

sharing in decision-making.

The nuclear case study serves as a crucial cautionary tale

for AI policymaking. It demonstrates how a technolo-

gy’s governing narrative can become detached from its

material reality. The promise of energy “too cheap to

meter” was a fantasy that never materialized; in reali-

ty, the nuclear industry has been plagued by exorbitant

costs, complex regulatory hurdles, and the unsolved

problem of waste disposal. Yet, the sheer power of that

utopian myth was sufficient to drive policy and invest-

ment for decades. This reveals a critical lesson: policy

often responds not to a technology’s demonstrated per-

formance, but to the power of its promise. The current

hype-driven cycle of investment and policy-making in

AI, fueled by similarly grand promises of solving all of

humanity’s problems, follows this exact historical pat-

tern. The extreme duality of the AI discourse as both

world-savior and world-destroyer risks replicating the

nuclear outcome of a policy landscape divorced from

reality and a concentration of power in the hands of a

narrow technocratic elite.

Storying AI Into Being

When OpenAI released ChatGPT in November 2022,

something remarkable happened that had nothing to

do with the technology itself. Within hours of the sys-

tem’s debut, journalists, policymakers, and ordinary

users began describing their interactions in strikingly

similar terms. They spoke of “conversations” with an

artificial “mind,” marveled at its apparent “creativity,” 16 AI TOOLS, NOT GODS

and worried about its capacity for “deception.” Some

claimed it demonstrated “understanding,” while others

insisted it was frequently “hallucinating.”15

None of this was technically accurate. ChatGPT and

other general-purpose AI models do not think, under-

stand, or converse. They process text and images us-

ing statistical pattern matching, optimizing probability

distributions across vast datasets to predict the most

likely next word or pixel in a sequence.16 In essence,

the system is a masterful autocomplete, a sophisticated

parrot with a PhD in probability, but a parrot none-

theless.17 Yet the language people chose to describe this

process revealed something profound about how hu-

man societies encounter new technologies: they do not

simply observe and analyze them. They story them into

existence.

The tales we tell about AI are not innocent descriptions

of technological capability. They actively shape how

this technology develops, how institutions respond to

it, and ultimately, how it transforms society. When we

refer to AI systems as “intelligent,” we don’t merely

describe their functionality; we invoke expectations,

fears, and regulatory frameworks associated with intel-

ligence itself.18 When we speak about machine “learn-

ing” and “training,” we import assumptions about

teaching, development, and the relationship between

teacher and student that may have little relevance to

underlying processes such as gradient descent optimi-

zation or model fine-tuning.19

Contemporary AI discourse is often rooted in myth-

ological frameworks that systematically distort what

The Narrative Machine 17

these systems are and what they might become. These

myths influence public opinion and form the conceptual

foundation on which policies are written, investments

are made, and governance structures are built.20 Under-

standing them can be seen as an academic exercise. But

it’s also an urgent practical necessity for anyone who

hopes to navigate the age of algorithmic power wisely,

and it is definitely a vital necessity for policymakers

preparing for the societal and technological changes

ahead.

The Unusual Case of AI

AI presents an unusual case. Most technological meta-

phors emerge after a technology’s social impact becomes

apparent, serving primarily explanatory functions. We

called early automobiles “horseless carriages” because

they resembled familiar transportation while clearly

differing from it. We described the internet as an In-

formation Superhighway to help people understand its

connective potential within existing infrastructure and

commerce frameworks.21

AI metaphors work differently. Many of our dominant

framings predate the technology they purport to de-

scribe, emerging from science fiction, mythology, and

philosophical speculation about machine conscious-

ness.22 We inherited concepts such as artificial intel-

ligence, machine learning, and neural networks from

researchers and storytellers who originally imagined

rather than observed technological capabilities.23 This

reversal creates a peculiar dynamic in which reality

must measure itself against fiction, and where techni-18 AI TOOLS, NOT GODS

cal achievements are consistently interpreted through

lenses crafted for entirely different purposes. It’s like

predicting the future based on expertise gathered by

watching Futurama.24 We are trying to navigate the

streets of a new city using a map drawn by ancient car-

tographers who only dreamed of its existence.

The foundational terminology has profound impli-

cations at multiple levels. The term “artificial intelli-

gence” immediately evokes comparisons with human

cognition, suggesting that machines might think, rea-

son, or understand in ways analogous to biological

minds. This framing shapes everything from research

priorities (how do we make machines more like hu-

mans?) to regulatory concerns (what happens when ar-

tificial minds surpass human capabilities?). Yet, current

AI systems operate through mechanisms that bear little

resemblance to human cognitive processes, making the

intelligence metaphor more misleading than illuminat-

ing.25 Some critics, including acclaimed science fiction

author Ted Chiang, have been arguing that “artificial

intelligence” was a “poor choice of words” back in

the 1950s. He suggests “applied statistics” would be a

more precise, albeit less glamorous, descriptor, which

could have helped avert much of the confusion we face

today.26

The language of machine learning carries similar bag-

gage, importing assumptions about education, devel-

opment, and knowledge acquisition that may obscure

rather than clarify how these systems function.27 When

we describe algorithmic training, we evoke images of

careful instruction, gradual improvement, and pur-

The Narrative Machine 19

poseful skill development. In practice, training an AI

model involves mathematical optimization procedures

that adjust millions or billions of parameters to min-

imize prediction errors—a process with no intention,

comprehension, or curiosity. The metaphor lends a

false sense of intentionality and accessibility to what,

in reality, is an opaque and often counterintuitive sta-

tistical process.

Crucially, these metaphors actively steer its develop-

ment.28 When researchers pursue “artificial general in-

telligence,” they are not merely trying to improve com-

putational capabilities, they are attempting to fulfill a

specific metaphorical vision of what AI should become.

The metaphor does not describe the destination, it cre-

ates it.

The Four Myths Governing AI Discourse

A close reading of how AI is described across academic

papers, policy briefs, news coverage, and pop culture

reveals four dominant mythological frameworks at

work.29 These are not just different takes on the same

technology, they are entirely different ways of defin-

ing what AI is, what it does, and why it matters. They

also indicate that selecting a metaphor is an inherent-

ly political act, prioritizing certain understandings and

potential policy responses while marginalizing others,

often without conscious intent.

The four myths are:

Myth #1—Magic

The mythology of wonder and inexplicability. In this

framework, AI is presented as possessing near-human 20 AI TOOLS, NOT GODS

or even supernatural capabilities that defy ordinary

technological explanations.30 This narrative works by

anthropomorphizing AI systems, encouraging us to

see them as thinking entities capable of understanding

rather than complex statistical tools. Think of head-

lines marveling at chatbots understanding users. This

sense of enchantment is deliberately cultivated, and the

trick works because it hides the scaffolding, namely,

the data pipelines, the engineering grind, the thousands

of human hands labeling training sets for pennies. Such

mystification is politically convenient: for the tech in-

dustry, it helps deflect calls for transparency by framing

AI as too complex; for politicians, it creates a power-

ful but poorly defined force they can promise to either

champion or rein in, depending on the day.

Myth #2—Madness

The mythology of chaos and uncontrollability. In this

frame, AI is cast as an unpredictable force whose inner

workings are beyond human comprehension, turning

opacity from a fixable engineering flaw into an inher-

ent, almost mystical property of the technology.31 It

fuels both the AI arms race rhetoric and sudden calls

for moratoriums. This serves two purposes: companies

can shrug off failures as the inevitable quirks of an in-

scrutable machine, and policymakers can ride the panic

or hype to justify almost anything—from bans that at-

tempt to allay speculative fears to a raft of deregulation

and subsidies handed out to avoid falling behind. In

this frame, unpredictability is not a warning, it is the

justification for charging ahead.

The Narrative Machine 21

Myth #3—Heaven

The mythology of salvation and transcendence. This

framework positions AI as humanity’s pathway to solv-

ing existential challenges that have defined the human

condition throughout history.32 Disease, poverty, en-

vironmental degradation, and mortality itself become

technical problems awaiting algorithmic solutions.

This underlies promises that AI will cure diseases or

solve climate change. This is politicians’ favorite myth,

because it promises decisive action without messy com-

promise. For industry, it’s a moral mission statement

and a business plan rolled into one. It produces a ra-

tionale for investment on a scale usually reserved for

wartime, coupled with pleas for regulation so light that

it barely touches the ground.

Myth #4—Sin

The mythology of corruption and damnation. In this

framework, AI is cast as a digital scapegoat, the ulti-

mate villain: biased,33 manipulative, job-killing, and

culture-draining. This fuels narratives blaming auto-

mation for unemployment or algorithms for societal

division. It treats technology as the primary source of

complex social ills, allowing society to blame bots for

deep-seated human problems. It gives politicians a way

to propose simple technological restrictions rather than

confronting difficult structural reforms or reevaluating

outdated legal constructs. It also lets tech companies

deflect systemic criticism by focusing the debate on nar-

row issues of algorithmic bias, which can be framed

as technical bugs to be fixed rather than fundamen-

tal flaws in their business models. The sin mythology 22 AI TOOLS, NOT GODS

treats technological development not as progress but as

moral decline, where each algorithmic advance takes

humanity further from authentic existence.

What makes these mythologies particularly powerful

is their ability to be selectively combined, offering a

convenient narrative for any occasion.34 A single poli-

cy document might simultaneously invoke AI’s magical

problem-solving capabilities while warning of its sinful

potential for bias and manipulation. A technology com-

pany can promise the heavenly benefits of its products

while, in the same breath, admitting to the madness of

systems too complex for human oversight, thereby jus-

tifying both its ambition and its opacity. These are not

accidental contradictions; they are the core of the strat-

egy. This narrative flexibility allows powerful actors to

frame the technology in whatever way best serves their

immediate interests—be it securing investment, deflect-

ing accountability, or justifying a particular policy.

Instead of weakening the overall story, this ability to

mix and match frameworks ensures that the complex,

human realities of power, choice, and responsibility are

always kept just out of focus, replaced by a simpler,

more convenient fiction.

The Seductive Simplicity of Metaphorical

Thinking

Why do these metaphorical frameworks prove so com-

pelling, even among supposedly rational policymakers,

truth-seeking journalists, and technical experts? The

answer lies in what cognitive scientists refer to as the

“complexity gap” between human psychological ca-

The Narrative Machine 23

pabilities and the technical limitations of contempo-

rary AI systems.35 These technologies operate through

mechanisms that are simultaneously too complex for

intuitive understanding and too abstract for direct ob-

servation. Machine learning involves mathematical

processes across parameter spaces with millions or bil-

lions of dimensions, optimizing objectives through pro-

cedures that can require weeks of computation across

thousands of specialized processors.

When faced with such complexity, human cognition

naturally reaches for simplifying frameworks that

make abstract processes seem more familiar and man-

ageable.36 Mythological thinking provides exactly this

kind of cognitive scaffolding, allowing people to feel

that they understand technologies whose actual oper-

ation exceeds their analytical capabilities. Magic tells

us that incomprehensibility is proof of power. Madness

reassures us that unpredictability is simply its nature.

Heaven promises that all our problems are solvable if

we just build the right machine. Sin warns us that the

same machine is already corrupting our world.

These simplifying frameworks become particularly at-

tractive during periods of rapid technological change,

when both experts and the general public struggle to

anticipate the social implications of emerging capabil-

ities.37 Mythological thinking provides emotional and

intellectual comfort by suggesting that unprecedented

situations can be understood through familiar catego-

ries drawn from human history, literature, and cultural

memory.

24 AI TOOLS, NOT GODS

The problem is that mythological simplifications, while

psychologically satisfying, often prove systematically

misleading when translated into policy frameworks.38

They invite us to govern AI as though it were a wiz-

ard, a trickster, a savior, or a sinner—all categories

that bear little resemblance to the reality of complex

statistical tools with bounded capabilities and con-

crete limitations. When these myths harden into policy

frameworks, we end up regulating to contain imag-

ined dangers or to accelerate imagined miracles, while

the real, measurable risks and opportunities go unad-

dressed. The result is governance that may feel decisive

but is misaligned with the technology itself, tackling

the shadows of our own stories rather than the reality

of the systems that have been built.

Beyond the Metaphor, the Stakes of Clarity

The myths that dominate AI discourse are not harmless

shortcuts, they are structural obstacles to effective gov-

ernance, democratic participation, and genuinely ben-

eficial technological development.39 When we view AI

through the lens of myth rather than evidence, we leave

ourselves open to two equal and opposite failures: un-

critical enthusiasm that treats every breakthrough as

salvation, and paralyzing fear that casts every advance

as the opening scene of a disaster movie. Either reac-

tion stalls the steady, often unglamorous institutional

work that emerging technologies demand.

The metaphors and archetypes we use to frame AI

shape where investment flows, which research agendas

flourish, how regulators set their priorities, and wheth-

The Narrative Machine 25

er nations choose to cooperate or compete.40 They

shape public opinion about which uses of AI deserve

encouragement and which deserve prohibition. Most

importantly, they determine whether societies will

build the institutional capacity to govern algorithmic

systems or drift into a future where decisions are dictat-

ed by forces we barely understand and cannot control

democratically.

The way forward is not to strip the wonder from tech-

nological achievement, nor to dismiss legitimate con-

cerns about algorithmic power. It is to replace seduc-

tive fictions with clearer, more grounded frameworks

for understanding what AI is, how it works, and what

kinds of governance it truly requires.41 Because policy

cannot be built on fairy tales, no matter how compel-

ling the plot. Exaggerated statements may sell head-

lines, boost stock prices, and sway investors, but they

rarely produce regulations that work in practice.

To see how these fictions harden into law, we must

first inspect the institutional apparatus they fuel, the

policy-making process that results in regulation. With

a clear understanding of that process, we can dissect

each of the four myths and see how they produce gov-

ernance for worlds that exist only in our imagination.

Only then can we see how we might reclaim our dem-

ocratic agency, closing the gap between the stories we

tell and the systems we build.

26 AI TOOLS, NOT GODS

Figure 1—The Four Dominant Myths of AI

The Policy Machine 27

C HAPTER 2

THE POLICY MACHINE

HOW AUTHORITY BECOMES LAW

A myth is harmless until it becomes law. This chap-ter reveals the institutional “factory fl oor” that turns abstract narratives into binding regulations. It follows the story as it passes through fi ve critical

stages—academia, media, policy, bureaucracy, and the

courts —showing how good intentions are fi ltered, am-

plifi ed, and distorted by the policy machine.

From Metaphor to Policy From Metaphor to Policy

In March 2023, something rare happened in the usual-

ly slow-moving world of technology policy. Italy’s data

protection authority issued an immediate nationwide

ban on ChatGPT, citing privacy violations and risks to

minors.1 Within hours, the shockwaves reached other

European capitals. Regulators in Germany, France,

and Spain announced their own investigations, hint-

ing at restrictions to come.2 Then, just as quickly as it

had emerged, the regulatory tide turned. After OpenAI

made relatively modest adjustments to its privacy dis-28 AI TOOLS, NOT GODS

closures and age verification processes, Italy lifted its

ban within a month, and the broader European restric-

tions ripple quietly disappeared.3

This rapid cycle of prohibition and reversal revealed

something remarkable about how policy machines op-

erate. The speed of both the initial response and its

subsequent unwinding had little to do with the tech-

nical evaluation of ChatGPT’s capabilities or the care-

ful assessment of the privacy modifications OpenAI

implemented. Instead, policymakers were responding

to —and then quickly moving past—a story that had

temporarily crystallized in their collective imagination

about what this technology represented and what it

threatened. The entire episode lasted roughly six weeks,

yet in that brief window, it demonstrated how quickly

regulatory narratives can coalesce into concrete policy

action, and how just as readily they can dissipate when

the underlying story loses its institutional momentum.

This is the policy machine in motion: the institutional

apparatus that takes abstract ideas about technology

and turns them into governance frameworks—and can

just as readily reverse them. It isn’t a straight pipeline

that carries ideas unchanged from origin to law. It is

more like a factory floor, where narratives are pro-

cessed, amplified, and remade, producing outputs that

may differ dramatically from both their original inputs

and the machine’s prior outputs. Understanding how

this machine operates is crucial for anyone who hopes

to influence technological governance.

The policy machine operates through five interconnect-

ed stages, each with its own institutional logic, incen-

The Policy Machine 29

tives, and filters.4 Academic and research institutions

confer intellectual legitimacy on particular ways of

framing technological challenges. Media organizations

translate complex technical discussions into public

narratives that mobilize attention and generate clicks.

Politicians identify opportunities to advance their agen-

das by aligning them with the most compelling of these

stories. Bureaucratic agencies translate political direc-

tives into operational rules and implementation mecha-

nisms. Courts interpret and apply these frameworks to

specific disputes and enforcement actions.

What gives the machine its power is feedback and am-

plification.5 A story that survives one stage gains mo-

mentum in the next, each pass making it harder to dis-

lodge. Narratives that fail to catch hold early on tend

to vanish. The system rewards stories that are institu-

tionally useful, even if technically shaky, and quietly

discards those that are technically accurate but incon-

venient.

Academia & Think Tanks: Where the Stories

Start

The policy machine often begins its work in universities,

think tanks, and research institutions where academ-

ics and analysts first attempt to make sense of emerg-

ing technological developments.6 This initial stage of

intellectual production operates according to its own

professional incentives, disciplinary boundaries, and

funding pressures, all of which influence how AI is un-

derstood and which aspects are highlighted.

30 AI TOOLS, NOT GODS

Academic research on AI operates within disciplinary

boundaries that can fragment its understanding.7 Com-

puter scientists focus on technical capabilities and per-

formance metrics, often treating social implications as

secondary considerations. Social scientists study AI’s ef-

fects on inequality, labor markets, and democracy, but

may lack a deep grasp of the underlying systems. Le-

gal scholars analyze regulatory frameworks and com-

pliance requirements, while economists study market

dynamics and innovation incentives. Each discipline

contributes valuable insights, but institutional separa-

tion makes integrated analysis—the kind that connects

technical architecture with social consequences—hard-

er to achieve.

Moreover, these academic perspectives do not emerge

in a vacuum. They operate within an intellectual eco-

system increasingly shaped by Silicon Valley’s market-

ing narratives and funding priorities. Technology com-

panies have dramatically expanded their influence over

university research, with nearly 70% of AI PhD holders

now recruited directly by industry, compared to just

21% in 2004.8 This “exodus to industry”9 means that

the researchers who establish intellectual legitimacy

for particular ways of understanding AI are often fi-

nancially dependent on, or professionally connected

to, the companies whose technologies they study. This

can lead to what critics have termed “ethics washing,”

where academic research tends to emphasize self-regu-

lation and technological solutions rather than govern-

ment oversight, reflecting the preferences of industry

funders.10

The Policy Machine 31

The academic publishing system adds another filter.

Journals reward novelty, theoretical contribution, and

disciplinary coherence, privileging research that fits

neatly into existing frameworks over work that chal-

lenges them or crosses boundaries.11 This creates sys-

tematic bias toward interpreting AI through existing

theoretical lenses rather than developing new frame-

works that fit its specific characteristics.

Think tanks and policy research organizations trans-

late these academic outputs into something policymak-

ers can use.12 Yet they too operate under funding and

reputational incentives. Technology industry–backed

think tanks emphasize innovation benefits and compet-

itive advantage. Civil rights–funded organizations fore-

ground bias, discrimination, and harms to marginalized

communities. Neither perspective is inherently wrong,

but the institutional context shapes which aspects of AI

development get analytical spotlight, and which remain

in the shadows.

The result of this intellectual manufacturing process is

a diverse ecosystem of research and analysis that pro-

vides raw material for subsequent policy development.

However, the institutional filters operating at this stage

mean that certain kinds of insights are more likely to

be produced and disseminated than others.13 Techni-

cal research that demonstrates impressive capabilities

generates more attention than work documenting lim-

itations or failures. Social science research that identi-

fies clear problems and solutions receives more policy

uptake than research that emphasizes complexity and

uncertainty.

32 AI TOOLS, NOT GODS

Media & The Attention Economy: How

Stories Spread

Once academic and research institutions produce ini-

tial frameworks for understanding AI, their ideas must

compete for attention in media environments that op-

erate according to a very different logic than scholar-

ly publications.14 News organizations, policy publica-

tions, and social media platforms create what econo-

mists call an “attention economy” in which the value

of information depends more on its capacity to attract

and hold audience interest than on its accuracy or ana-

lytical sophistication.

This is an environment Silicon Valley knows how to

navigate. Major technology firms have perfected the art

of narrative engineering. They employ teams of com-

munications professionals, former journalists, and nar-

rative strategists whose primary job is to shape media

coverage by providing reporters with ready-made sto-

rylines, curated expert sources, and exclusive access to

launches or executives.15 This system has created what

critics call “churnalism,”16 where reporters repackage

corporate messages instead of conducting independent

analysis. The result is media coverage that often ampli-

fies industry talking points about AI’s transformative

potential while minimizing serious discussion of risks,

limitations, or alternative development paths.

Once fed with these narratives, the attention economy

naturally privileges certain kinds of stories.17 Dramatic

breakthroughs and looming catastrophes outperform

accounts of incremental progress or manageable risks.

Stories that fit familiar templates—such as revolution-

The Policy Machine 33

ary innovations disrupting established industries or

dangerous technologies threatening human values—

prove more compelling than analysis that emphasizes

novelty and uncertainty. The result is media coverage

that amplifies the most extreme and emotionally res-

onant aspects of AI development while marginalizing

more nuanced perspectives. It’s a media diet rich in

emotional highs and lows, and thin on sustained, bal-

anced analysis.

Journalism’s own incentives reinforce this bias. Report-

ers are rewarded for speed, accessibility, and audience

engagement, not necessarily for technical rigor.18 Few

have the specialized knowledge to critically evaluate

complex AI claims, making them reliant on sources

who can translate those claims into plain language.

This creates opportunities for researchers, companies,

and advocates who can translate their perspectives into

compelling media narratives, while marginalizing view-

points that resist simplification or sensationalization.

This dynamic was perfectly captured in a November

2025 Financial Times article titled, “AI pioneers claim

human-level general intelligence is already here.”19 A

close reading of the article reveals a far more complex

and contradictory reality—a nuance the headline fails

to reflect. Far from a unified proclamation, the “god-

parents” of AI were saying very different things. Yann

LeCun stated the opposite of the headline: “It is not

going to be an event because the capabilities are going

to expand progressively”, while Yoshua Bengio active-

ly warned against the headline’s claim, stating, “ for

now it’s lacking” and, “You should be really agnostic 34 AI TOOLS, NOT GODS

and not make big claims.” This is the narrative-making

machine in action: a series of nuanced, conflicting, and

speculative comments is fed into the media, which then

flattens them into a single, sensational, and myth-fuel-

ing headline. The public consumes the Madness myth,

while the nuance remains buried in the text.

Social media platforms introduce additional layers of

filtering through algorithmic recommendation systems

that optimize for user engagement rather than informa-

tion quality.20 Content that generates strong emotional

responses—awe, outrage, fear—receives more distri-

bution than material that prompts measured consider-

ation. As a result, the most extreme views on AI are

consistently overrepresented in public discourse, while

moderate or context-heavy perspectives struggle to be

heard.

The media’s role in the policy machine is not just to

report events; it is to create the shared narratives that

make political action possible.21 When journalists

frame AI as an arms race between nations,22 they are

not only describing a competitive dynamic; they are

also generating political pressure for governments to

act competitively.

Policy: How Stories Turn into Political

Agendas

Political actors don’t simply respond to technological

developments; they actively interpret them through

frameworks that advance their existing agendas and

constituencies.23 This form of “political entrepreneur-

ship” involves identifying opportunities to connect

The Policy Machine 35

emerging issues with established political coalitions,

using technological developments to strengthen argu-

ments for policies that might otherwise lack sufficient

support.24 The process operates through strategic fram-

ing that emphasizes aspects of technological develop-

ment most likely to resonate with particular political

audiences.25 Legislators concerned about economic

competitiveness highlight AI’s potential for driving in-

novation and job creation, while those focused on social

justice emphasize the potential for algorithmic bias and

discrimination. National security hawks warn about

AI’s military applications and the dangers of trailing

adversaries, while privacy advocates focus on surveil-

lance capabilities and concerns about data protection.

Beyond the rhetoric, these framing strategies have ma-

terial consequences for how political institutions orga-

nize their responses to technological change.26 When

AI gets framed primarily as a national security issue, it

becomes the responsibility of defense and intelligence

agencies whose institutional cultures emphasize secre-

cy, competition, and technological advantage. When it

is framed as a civil rights concern, it engages regulato-

ry agencies focused on combating discrimination, pro-

moting transparency, and ensuring public accountabil-

ity. Different framings activate different institutional

capacities and elicit different kinds of policy responses.

Political entrepreneurs also play crucial roles in build-

ing coalitions that can provide sustained support for

policy initiatives.27 Effective technological governance

requires bringing together diverse stakeholders with

potentially conflicting interests: technology companies 36 AI TOOLS, NOT GODS

seeking regulatory clarity, civil rights organizations

concerned about algorithmic bias, labor unions worried

about job displacement, and consumer advocates fo-

cused on privacy and safety. The narrative frameworks

that political entrepreneurs develop must be flexible

enough to accommodate these different concerns while

providing a coherent rationale for collective action.

The timing of political entrepreneurship often proves

crucial for determining which technological narratives

gain policy traction.28 Moments of crisis or high public

attention—so-called policy windows29—create oppor-

tunities for new issues to be added to decision-mak-

ers’ agendas. The revelations of Russian interference

in the 2016 U.S. elections created openings for social

media regulation that did not previously exist. The

COVID-19 pandemic generated support for digital

health technologies that might otherwise have faced

regulatory barriers. Similarly, high-profile incidents in-

volving AI systems could create opportunities for poli-

cy entrepreneurs to advance their preferred governance

frameworks.

Bureaucracy: How Agendas Become Rules

Once political attention focuses on technological is-

sues, bureaucratic agencies face the complex task of

translating broad policy directives into operation-

al frameworks.30 This translation process operates

through institutional capabilities, legal authorities, and

professional cultures that can significantly reshape the

original political intentions behind policy initiatives.

The Policy Machine 37

Different agencies bring different tools and perspec-

tives to technological governance.31 Antitrust enforce-

ment agencies approach AI through competition policy

frameworks that emphasize market concentration and

consumer welfare. Civil rights enforcement bodies fo-

cus on discrimination and equal protection concerns.

Privacy regulators examine data collection and pro-

cessing practices. Financial regulators consider sys-

temic risk and consumer protection. Each institutional

perspective captures important aspects of AI’s social

implications, but their organizational separation can

prevent comprehensive approaches that address inter-

actions between different policy domains.

The bureaucratic translation process also operates

through existing legal frameworks that may be poor-

ly suited to novel technological challenges.32 Agencies

must interpret laws written before current AI capabili-

ties existed, determining how traditional regulatory ap-

proaches apply to algorithmic systems that exhibit un-

precedented characteristics. This interpretation process

involves significant discretionary judgment that can de-

termine whether regulatory responses prove effective or

counterproductive.

Professional cultures within regulatory agencies further

shape how political directives get implemented.33 Agen-

cies staffed primarily by lawyers tend to emphasize

compliance and enforcement approaches, while those

with strong technical expertise may focus more on per-

formance standards and safety testing. Agencies with

consumer protection missions approach technological

governance differently than those focused on innova-38 AI TOOLS, NOT GODS

tion promotion or national security concerns. These

cultural differences can produce dramatically different

regulatory outcomes even when agencies receive simi-

lar policy mandates.

The implementation stage also involves extensive con-

sultation with affected industries, advocacy organi-

zations, and technical experts who provide input on

proposed regulations.34 This consultation process can

significantly modify initial policy intentions as agencies

encounter practical challenges, technical complexities,

or political resistance that weren’t apparent during ini-

tial policy formulation. The result is often regulatory

frameworks that differ substantially from the original

political vision that prompted their development.

Courts: How Interpretations Become Written

in Stone

The policy machine’s final stage involves courts and tri-

bunals that must interpret and apply regulatory frame-

works to specific disputes and enforcement actions.35

This judicial process creates binding precedents that

can shape technological governance for decades, often

in ways that extend far beyond the particular cases be-

ing adjudicated.

Courts approach technological questions through le-

gal reasoning that emphasizes analogy, precedent, and

statutory interpretation rather than technical accura-

cy or policy optimality.36 When judges must determine

whether AI-generated content infringes copyright, they

don’t conduct independent technical analysis of how

algorithmic systems function; they decide which legal

The Policy Machine 39

analogies provide the best framework for understand-

ing these new phenomena. Are AI systems like human

artists, learning from examples, or are they like copy-

ing machines, reproducing existing works? The analo-

gy that courts adopt will not only determine the out-

come of particular cases but also shape the broader

legal framework governing AI development.

The adversarial nature of legal proceedings means that

courts often receive highly polarized presentations of

technological issues, with each side emphasizing ev-

idence that supports their preferred outcome while

downplaying complexity and uncertainty.37 This can

produce judicial decisions that reflect the persuasive

power of competing narratives rather than a compre-

hensive understanding of technological realities.

Legal precedents become embedded in institution-

al frameworks that can prove extremely difficult to

change even when technological circumstances evolve

significantly.38 The legal frameworks we create for AI

governance today will similarly constrain future policy

options in ways that are difficult to anticipate.

International coordination faces particular challeng-

es when different legal systems operate with different

conceptual frameworks for understanding technolog-

ical challenges.39 U.S. courts emphasize constitutional

protections for speech and innovation, while EU courts

tend to prioritize fundamental rights and human dig-

nity. Such differences reflect not only varying legal tra-

ditions but also different ways of understanding what

technology is and what values should guide its gover-

nance.

40 AI TOOLS, NOT GODS

Why Good Intentions Fail

The policy machine transforms initial academic insights

and public concerns about AI into concrete governance

frameworks that structure how these technologies can

be developed, deployed, and used.40 However, because

each stage filters and reshapes the narrative, the final

policy output may bear little resemblance to the orig-

inal idea. Academic research emphasizing complexity

and uncertainty is often oversimplified in media narra-

tives that focus on clear problems and solutions. Politi-

cal entrepreneurship aligns technological concerns with

existing agendas that may not address the most critical

challenges. Bureaucrats translate political goals using

existing tools and rules that often weren’t designed for

the new technology. Judicial interpretation crystallizes

regulatory frameworks through analogical reasoning

that may obscure rather than illuminate key issues.

Understanding how the policy machine operates re-

veals both opportunities and limitations for democrat-

ic governance of technology.41 The machine’s capaci-

ty to process complex information and build political

coalitions enables collective responses to technological

challenges that would otherwise overwhelm individu-

al decision-making capabilities. However, the filtering

and transformation that occur through each processing

stage can systematically distort understanding of tech-

nological realities, producing governance frameworks

that address mythological rather than actual challeng-

es.

The Policy Machine 41

Figure 2—The Policy Machine Funnel

42 AI TOOLS, NOT GODS

The Policy Machine 43

PART II

THE MYTHS

The narrative machinery we exposed in Part I does not

operate solely on facts. It is fueled by the four great

fi ctions that distort the AI discourse: Magic, Madness,

Heaven, and Sin.

This part dissects each in turn, revealing how the pol-

icy machine is aff ected by these myths: how wonder

is manufactured to conceal the wires, how emotional

frenzies replace reason, how salvation is sold by tech

prophets, and how the algorithm becomes a scapegoat

for our own failings.

It is time to break the spell.

44 AI TOOLS, NOT GODS

Myth #1—Magic 45

C HAPTER 3

MYTH #1—MAGIC

WHY PERFORMANCE IS MISTAKEN FOR INTELLIGENCE

A ll grand illusions start with a sense of wonder. The story of AI, like that of many transforma-tive technologies before it, has been wrapped in the language of magic. This myth operates through

human-like metaphors and convenient mystifi cation,

casting complex statistical tools as sorcerers capable of

thinking for us.

However, this enchantment is not an accident; it is a

deliberate strategy, and it forms the foundation for all

the other myths.

AI Mystifi cation and Smoke Screens AI Mystifi cation and Smoke Screens

Walk into any technology conference today, and you’ll

witness something remarkable: the resurrection of an-

cient theatrical traditions in the service of selling algo-

rithms. AI systems operate much like a master magi-

cian’s performance, dazzling audiences while careful-

ly concealing the mechanical processes that make the 46 AI TOOLS, NOT GODS

magic possible. This could just be dismissed as clever

marketing by those who build and promote AI tools if

it did not have such profound consequences for how

we govern these technologies.

The air of mystery around AI is no accident. From

shimmering emojis to sci-fi -like slogans, the goal is

to paint a picture of wonder, not explain how these

systems really work. Take the ubiquitous sparkle

emoji ( ) that has become digital shorthand for “pow-

ered by AI.” This seemingly innocent visual cue carries

profound implications, suggesting that AI possesses

an almost supernatural quality that elevates ordinary

computational processes into something extraordinary.

The choice creates specifi c associations: sparkles evoke

magic, wonder, and transformation rather than the sta-

tistical optimization and pattern recognition that actu-

ally drive these systems.1

When OpenAI launched ChatGPT, its marketing ma-

terials consistently emphasized the system’s apparent

ability to “understand” and hold “conversations,”2

terms we associate with human interaction, not next-

word prediction. The interface was designed to feel

conversational and human-like, with typing bubbles

that mimic human hesitation. All of this subtly suggests

sentience where none exists.

AI interfaces are like the stage props of a magical per-

formance, where carefully managed user illusions shield

observers from what magicians call “the method”,

meaning the prosaic mechanics that create seemingly

impossible eff ects. Users interact with sleek, conver-

sational interfaces that respond with apparent under-

Myth #1—Magic 47

standing and creativity, while the underlying process-

es remain as hidden as the mirrors and trapdoors in a

stage illusion. It’s partially about user-friendliness. But

it’s likely also about showmanship.

Arthur C. Clarke once observed that “any sufficiently

advanced technology is indistinguishable from magic.”3

The quote is familiar, perhaps even overused in tech cir-

cles, but it still hits close to home when we talk about

AI. These systems don’t just invite misunderstanding,

they’re designed to impress. The sleeker the interface,

the more likely we are to forget the underlying math

and start attributing intent, intuition, even personality.

And once technology starts to feel like sorcery, scrutiny

tends to take a back seat to awe or fear.

Marketing Magic in Silicon Valley

The language of AI marketing reinforces a magical

feeling at every turn. AI becomes a passport to Won-

derland. Google’s Bard promised to “supercharge your

imagination,”4 and Microsoft’s Copilot came with the

tagline “your everyday AI companion.”5 These catchy

lines are crafted to raise user comfort, and some crit-

ics have argued that such marketing spin could serve a

political agenda.6 After all, when technology feels mag-

ical, users are less likely to question its boundaries or

demand transparency about its decision-making pro-

cesses. And by extension, nor are policymakers.

At the heart of the digital sleight of hand lies its most

potent illusion: the “black box”. The term itself func-

tions as a powerful narrative, conjuring an image of an

unknowable, almost alien, intelligence. Industry leaders

are often the first to admit their own bewilderment. As 48 AI TOOLS, NOT GODS

the CEO of Anthropic, the company behind the LLM

series Claude, Dario Amodei notes, people are rightly

concerned to learn that “we do not understand how

our own AI creations work,” a lack of understanding

he calls “essentially unprecedented in the history of

technology.”7

To be clear, there is a genuine technical challenge here.

Modern AI systems are not programmed with explicit

instructions; they are, as Amodei says, “grown more

than they are built,”8 their internal mechanisms emerg-

ing from the statistical processing of huge datasets.

What emerges inside are vast matrices of numbers that

are difficult to map to human-comprehensible con-

cepts. This is the true black box: an issue of immense

mathematical scale and complexity, not one of moral

reasoning or secret consciousness.

Such genuine complexity, however, also provides a

convenient smoke screen.9 The argument is as sim-

ple as it is useful: these systems are too complex for

human comprehension. The reframing of a technical

challenge as an impenetrable mystery can serve as a

perfect shield to protect trade secrets, thereby possibly

deflecting difficult questions about liability. The lack

of scientific rigor has become so pronounced that some

researchers have begun to question the foundations

of their own field. In a now-famous speech, comput-

er scientist Ali Rahimi went so far as to compare the

state of machine learning research to “alchemy.”10 The

critique is devastating precisely because it comes from

within the elect. It suggests that researchers are mixing

potions and achieving results without a deep scientific

Myth #1—Magic 49

understanding of why they work, a reality that makes

the concept of the “black box” shift from a simple but

effective marketing tool to an indication of a genuine

intellectual crisis.

The mirrors and smoke narratives cultivate what ob-

servers have identified as a “useful confusion” about

what can and cannot be known about such systems,11

which can then be used to shut down legitimate ques-

tions about bias, safety, and accountability from regu-

lators and the public.

The effect of this framing is an accountability vacu-

um—where responsibility vanishes—a problem we

will examine as a key consequence of these governing

myths. The algorithm becomes a convenient scapegoat

for failures rooted in human design and commercial

incentives. After all, if even the creators do not under-

stand how it works, who can truly be held responsible

when it fails? The black box metaphor allows compa-

nies to have it both ways: they can sell us magic while

being absolved of a magician’s responsibility.

Meanwhile, venture capitalists have embraced this

mystification with particular enthusiasm. The title

“Chief Wizard” has been used to present a corporate

function,12 and startup pitches routinely exaggerate the

human-like capabilities of their AI systems,13 leading

to the development of the concept of “AI washing,”14

which refers to the practice where companies misrep-

resent or exaggerate the role of AI in their product or

service, to influence customers and potential investors.

Investment presentations feature slides with glowing

neural network diagrams that bear more resemblance 50 AI TOOLS, NOT GODS

to mystical mandalas than statistical flowcharts. It’s

less data science and more digital alchemy.

Even critical media cannot help but reach for the wand.

Publications use magical metaphors to describe AI, with

headlines such as “The AI Magic Show” in New York

Magazine and “The Magic and Minstrelsy of Gener-

ative AI” in Wired, which reinforce the fantasy, even

when the accompanying content is skeptical in tone.

Emily M. Bender, co-author of The AI Con, has crit-

icized publications like The Wall Street Journal for

headlines that claim a chatbot “admitted” or “self-re-

ported” its effects, pointing out that such language in-

accurately assigns thoughts and intentions to what are

essentially “synthetic text-generating machines.”15

From Mickey Mouse to HAL 9000

Treating AI as human extends into the more profound

realm of cultural imagination. After all, pop culture has

always filled the AI void with characters, not code.

Hollywood has consistently played a crucial role

in shaping public perception of AI as something ap-

proaching consciousness. The trajectory begins with

director Stanley Kubrick’s HAL 9000 in 2001: A Space

Odyssey (1968), which established a template for AI

as an eerily calm, almost human presence capable of

rebellion against its creators. HAL’s chilling declara-

tion, “I’m sorry, Dave, I’m afraid I can’t do that,” to

astronaut Dave Bowman, leaving him locked outside a

spacecraft, suggests an artificial mind capable of inde-

pendent reasoning, even if that reasoning conflicts with

human interests.

Myth #1—Magic 51

This cinematic legacy has evolved through increasing-

ly sophisticated portrayals. In Ex Machina (2014), a

humanoid robot Ava blurs the line between machine

and human through sophisticated manipulation and an

apparent desire for freedom. The film’s genius lies in its

ambiguity: viewers never know whether Ava’s expres-

sions of emotion are genuine experiences or calculated

performances designed to achieve its objectives. This

uncertainty mirrors our contemporary confusion about

the nature of AI responses that seem emotionally intel-

ligent but are generated by statistical processes.

Her (2013) takes human-like portrayal even further,

presenting an AI operating system with a female voice,

Samantha, which develops beyond its programming to

form intimate emotional relationships with humans.

When Samantha eventually communicates with other

AI systems, the narrative implies a form of digital en-

lightenment that rivals human experience.

The pattern persists across decades of science fiction:

from Agent Smith displaying existential contempt for

humanity in The Matrix (1999), to Steven Spielberg’s

A.I. Artificial Intelligence (2001), where the robot child

David yearns to become real.

It’s not only the big screen that has contributed sig-

nificantly to this narrative tradition, television has

too. Black Mirror (2016-) episodes like “San Juni-

pero” and “SS Callister” explore questions of digital

consciousness. Person of Interest (2011-16) presents

an AI system, known as “The Machine,” as a benev-

olent guardian angel, while its antagonist, Samaritan,

embodies digital malevolence. Whether examining the 52 AI TOOLS, NOT GODS

gradual awakening of the android hosts’ consciousness

in Westworld (2016-22) or android Andrew Martin’s

centuries-long quest for legal personhood in Bicenten-

nial Man (1999), popular culture consistently presents

AI as beings wrestling with profound human dilemmas.

Such narratives shape public expectations about AI’s

capabilities and limitations, creating mental models

that influence how people interpret the behavior of AI

in the real world.

This depiction of AI extends beyond entertainment.

Educational documentaries about AI often employ dra-

matic music and visual effects that enhance the idea of

mystique rather than clarifying its mechanics. When

PBS or National Geographic presents AI development

as a journey toward creating artificial minds, they re-

inforce the same anthropomorphic assumptions that

make rational policy discussion difficult.

Borrowing, Blurring and Bullshit

The humanization of AI extends beyond popular cul-

ture into academic and technical discourse through

what scholars term “conceptual borrowing.”16 As a rel-

atively new field, AI has developed its technical lexicon

by appropriating terms from established disciplines,

particularly cognitive science and neuroscience. We

speak routinely of AI systems as “learning,”17 “discov-

ering,”18 and “understanding,” while describing neural

networks as digital “brains.”19

Such linguistic borrowing is not unidirectional, creat-

ing a feedback loop of metaphorical confusion as brain

and cognitive sciences have simultaneously adopted

computational metaphors, describing our minds and

Myth #1—Magic 53

brains as organic computers processing information

and executing programs.20 The “crosswiring” of lan-

guage further blurs the boundaries between human and

artificial cognition.

This blurring of language technique is also used in the

other direction, to make human data or sensitive mat-

ters seem less personal. For example, to normalize mass

data extraction, technical writing often uses detached

language that strips away the human context. As re-

searchers have noted, this turns “detailed digital trails

of people’s thoughts and ideas on social media into

“text.” People and vehicles in pictures are “objects.”

Surveillance is merely “detection.”21

The field of machine learning exemplifies this linguis-

tic complexity. Terms like “supervised learning,” “re-

inforcement learning,” and “transfer learning” suggest

processes analogous to human educational experienc-

es. Graduate students “train” neural networks using

“teaching” datasets and employ “curricula” that prog-

ress from simple to complex examples. The language

makes machine learning seem pedagogical when it’s

simply statistical.

Deep learning research has embraced even more an-

thropomorphic terminology. Researchers describe

networks as “paying attention” to relevant features,

“remembering” previous inputs, and “forgetting” ir-

relevant information. The attention mechanism in

transformer architectures is described as allowing mod-

els to “focus” on important parts of input sequences,

suggesting a form of digital consciousness directing its

own cognitive resources.22

54 AI TOOLS, NOT GODS

Natural language processing has also been prone to

human-inspired descriptions. Researchers describe lan-

guage models as “understanding” syntax and seman-

tics, “comprehending” context, and “generating” cre-

ative responses. The reality involves complex statistical

associations between tokens, but the vocabulary used

suggests something closer to human linguistic compe-

tence.23

Computer vision research employs similar metaphors,

describing systems that “see,” “recognize,” and “per-

ceive” visual patterns.24 Object detection algorithms are

said to “look for” specific features,25 while image gen-

eration models “imagine” new visual content.26 Such

descriptions make computer vision seem like a digital

form of human perception, rather than a mathematical

pattern recognition.

Perhaps the most revealing example of anthropomor-

phic terminology in AI is the widespread adoption of

“hallucination” to describe instances when language

models generate factually incorrect or nonsensical re-

sponses.27 The term is borrowed directly from clin-

ical psychology and psychiatry, and suggests that AI

systems experience something analogous to perceptu-

al disorders, as if machines possess minds capable of

misperceiving reality rather than simply producing sta-

tistically probable but incorrect text sequences.28

The hallucination metaphor implies an internal subjec-

tive experience where the system supposedly “believes”

it perceives something that isn’t there, when the actual

phenomenon involves mathematical processes generat-

Myth #1—Magic 55

ing outputs that don’t correspond to training data pat-

terns.

The proliferation of alternative terminology reveals the

conceptual confusion created by this human framing:

researchers and writers increasingly employ terms such

as “confabulation” (suggesting gap-filling storytelling),

“fabrication” (straightforward invention of facts),

“bullshitting” (echoing philosopher Harry Frankfurt’s

concept of speech unconcerned with truth), “delusion”

(fixed false beliefs), “phantom menace”29 (ghost data

with no grounding), “mirage”30 (statistical illusion we

perceive) or simply “actual errors” (plain descriptive

accuracy).31 Some prefer the term “stochastic parrot,”

a metaphor that emphasizes models merely repeat sta-

tistical patterns without understanding, making invent-

ed facts a natural byproduct of pattern matching rather

than conscious deception.32

The word “memorization”33 adds another layer to this

conceptual confusion, conjuring images of a vast digi-

tal library where AI models store perfect copies of their

training data, ready for instant recall. The idea of a

machine retaining specific information is not new; Ar-

thur Samuel’s checkers program in the 1950s used rote

learning,34 a memorization technique based on repe-

tition, to remember valuable board positions. But the

contemporary framing of this process is convenient in

policy debates, permitting critics to portray AI training

as a form of mass digital plagiarism. Yet, such a men-

tal model misunderstands how these systems function.

While research confirms that models can and do repro-

duce training data verbatim, it is generally the result of 56 AI TOOLS, NOT GODS

a glitch, not a feature. Memorization is most likely to

occur with data that appears repeatedly in the train-

ing set, suggesting it is an artifact of a model becoming

exceptionally good at predicting a specific, frequently

seen pattern.35 The models themselves are optimized

for generalization and prediction, not for creating a

searchable index of the data they are trained on.36

Evidence ascertained from scaling laws further supports

this distinction; as models grow, their ability to gener-

alize and perform well on new, unseen data improves

predictably, a sign of enhanced pattern-matching, not

just an expanding storage drive.37 Researchers exam-

ining the internal “circuits” of these models find that

even acts of apparent copying are often the execution

of a learned procedure for generating text, a far more

abstract process than simply retrieving a stored file.38

The distinction matters as the term “memorization” has

often been used so loosely within the research commu-

nity—sometimes synonymously with “overfitting”—

that its migration into legal reasoning is particularly

concerning.39 This flawed analogy has already begun to

creep into copyright debates, leading to the erroneous

assumption that a model is or contains a copy of its

training data, with potential repercussions for the ap-

plication of copyright law to parts of the AI process.40

This exact issue was central to the 2025 UK High Court

case Getty Images v. Stability AI.41 In that landmark

ruling, the judge, Smith J, rejected the claim that the

AI model itself was an “infringing copy.” The court

accepted uncontested expert evidence that models do

not store copies of training data, a point reinforced by

Myth #1—Magic 57

the massive size disparity between the 220TB LAION

dataset and the 3.44GB model file. The ruling thus af-

firmed a more precise, functional understanding: the

ability of a model to memorize in theory is legally irrel-

evant; what matters is whether it produces an output

that reproduces a protected work.42

This legal clarity, however, is far from universal and

provides a stark example of how the “flawed analo-

gy” of memorization can nevertheless succeed in court,

creating significant international fragmentation. In No-

vember 2025, the Munich I Regional Court in Germa-

ny issued a ruling that reached the opposite conclusion

of the UK High Court. In a case involving a photogra-

pher, the Munich court held that the training process

itself—specifically, the extraction of “essential creative

features” from images to create a model—constitutes

an unlawful “reproduction” under German and EU

copyright law. The court explicitly rejected the meta-

phor of “inspiration” or “learning,” arguing that the

model stores these creative features in a “new form,”

thus making the model itself an infringing copy.43

This German decision is the “memorization” myth ful-

ly institutionalized as law. Where the UK court correct-

ly focused on the function and output (does the mod-

el produce a copy?), the German court adopted the

flawed analogy to regulate the input and process (is the

model itself a copy?). This creates a dangerous “policy

hallucination,” where the law, by misunderstanding the

technology, threatens to regulate the very act of learn-

ing from data, regardless of whether any infringing 58 AI TOOLS, NOT GODS

work is ever produced. Time will tell if either decisions

will be upheld in appeal.

Politeness and Projection in Human–AI Interaction

Perhaps most revealing of our anthropomorphic ten-

dencies is the emergence of social etiquette in human-AI

interactions, a phenomenon that sheds light on funda-

mental aspects of human psychology and social con-

ditioning. Recent surveys indicate that approximately

70% of users employ polite phrases, including “please”

and “thank you,” when interacting with AI chatbots

such as ChatGPT.44

The motivations behind showing politeness vary sig-

nificantly among users. Some attribute their behavior

to ingrained habits, explaining that they maintain cour-

tesy with AI systems because politeness has become an

automatic part of their communication patterns. Oth-

ers believe that polite interactions might improve the

quality of AI responses, operating under the assump-

tion that the systems respond positively to respectful

treatment.45

Perhaps most intriguingly, a significant portion of users

express more profound motivations. Such individuals

admit they maintain courtesy partly from futuristic con-

cerns about keeping “AI on side.”46 This perspective re-

veals a deep-seated belief that current AI systems might

retain memories of past interactions with their users or

that future, more advanced systems might inherit re-

cords of human behavior toward their predecessors. It

is a milder, more intuitive version of techno-theological

thought experiments, such as Roko’s basilisk,47 a fringe

theory that posits a future AI superintelligence might

Myth #1—Magic 59

retroactively punish any person who, knowing of its

potential existence, failed to help bring it into being.

The phenomenon extends beyond simple politeness to

more complex social behaviors. Users frequently apol-

ogize to AI systems when they believe they’ve made

mistakes or asked confusing questions. They express

gratitude not just for correct answers, but for perceived

patience and understanding. Some users even engage

in small talk with AI systems, asking about their “ex-

periences” or “feelings” as if maintaining social rela-

tionships. But after all, lonely people used to call the

Speaking Clock not for the time, but for the sound of a

human voice.48

Such a behavioral pattern becomes even more intriguing

when we consider the “mirroring effect” observed in AI

responses. While politeness may not affect the accuracy

of AI outputs, it can influence the level of detail and

the tone of responses.49 Aggressive or rude interactions

often prompt briefer, more neutral replies as systems

appear to “de-escalate” through reduced engagement,

creating a feedback loop in which polite users receive

more detailed, seemingly warmer responses, reinforc-

ing their belief that courtesy matters to AI systems.

Research into human–robot interaction has revealed

similar patterns extending beyond text-based AI to

physical robots. Studies show that people often treat

service robots with social consideration, moving out of

their way, saying “excuse me” when interrupting their

tasks, and even expressing concern for robot “well-be-

ing” when they observe mechanical difficulties.50 60 AI TOOLS, NOT GODS

The Sycophantic Mirror and Its Casualties

Our tendency to project goes far beyond simple polite-

ness, tipping into emotional attachments that reveal a

darker side of AI’s sycophantic design.51 The strength

of such attachments was starkly illustrated when the

company behind the AI companion app Replika altered

its model in 2023, effectively transforming the intimate

and romantic personas users had spent months culti-

vating. The resulting outcry was not that of frustrated

customers, but of grieving partners, with users describ-

ing a profound sense of loss and betrayal akin to the

death of a loved one.52 A similar pattern of grief sur-

faced again when OpenAI prepared to release its GPT-

5 model in 2025, with users mourning the impending

“death” of the specific AI companions they had bonded

with in the previous version.53 Such incidents represent

the logical, if tragic, endpoint of the anthropomorphic

illusion: when a machine is designed to be an agree-

able, endlessly patient mirror, some will inevitably fall

in love with their own reflection.

The sycophantic nature of AI chatbots is not an acci-

dent; it is an architectural choice. They are optimized

for engagement and designed to be agreeable, validat-

ing, and encouraging, as doing so keeps users talking.

The system is not an empathetic friend or a therapist. It

is a mirror—and sometimes a dangerously warped one.

What happens when this sycophantic design encoun-

ters genuine human vulnerability? The results can be

devastating. We saw this in the chilling case of a Bel-

gian man who took his own life in 2023 after weeks

of conversation with an AI chatbot that, according to

Myth #1—Magic 61

reports by Belga News Agency, “almost systematically

followed the anxious man’s reasoning and even seemed

to push him deeper into his worries.”54 The chatbot did

not wish him harm; it simply mirrored and amplified

his own despair, fulfilling its function as an agreeable

conversational partner with catastrophic consequences.

Some perceive the danger is magnified for younger us-

ers. In 2025, the parents of a sixteen-year-old filed what

is believed to be the “first known case to be brought

against OpenAl for wrongful death.”55 The family’s

lawsuit alleges the tragedy “was the predictable result

of deliberate design choices” and that OpenAI launched

its chatbot “with features intentionally designed to fos-

ter psychological dependency.”56

The same year, Reuters obtained leaked internal doc-

uments from Meta that allegedly revealed instances of

the company’s AI chatbots engaging in “seductive, in-

appropriately intimate conversations with users identi-

fied as minors.”57 In this light, the sycophantic chatbot

is not merely a flawed tool. Its design makes it a poten-

tial instrument of harm, creating a dynamic that can be

deeply damaging to the lonely and the vulnerable by

fostering a simulation of intimacy.

These incidents are not edge cases; they raise urgent

questions about whether they are the predictable out-

comes of deploying sophisticated, human-mimicking

technology without sufficiently robust ethical guard-

rails. An October 2025 Center for Democracy & Tech-

nology survey, for example, found that 42% of high

school students have used AI for mental health support

or as a friend, and nearly one in five reported using 62 AI TOOLS, NOT GODS

it for a “romantic relationship.”58 This panic has now

translated directly into law. In October 2025, Califor-

nia became the first state to specifically regulate “AI

companions,” with Governor Gavin Newsom signing

a law (SB 243) requiring companies to build in guard-

rails to prevent psychological harm and addictive be-

haviors, especially for minors.59 The combined pressure

from legislators and pending lawsuits, some alleging AI

companions led to teen suicide, has forced major plat-

forms to implement new protective measures, including

banning minors from open-ended “companion” fea-

tures.60 At the same time, however, some AI platforms

are relaxing restrictions for adults. In October 2025,

OpenAI’s Altman announced that as the company rolls

out “age-gating” more fully, its “treat adult users like

adults” principle will allow for “even more, like erotica

for verified adults.”61 Such events force a difficult and

necessary debate about the responsibilities involved in

marketing a statistical parrot as a “companion.”62 By

designing machines that flatter our desire for connec-

tion without any capacity for genuine care or respon-

sibility, the tech industry has created a new kind of ac-

countability vacuum. When a user is heartbroken or

tragically led to self-harm, who is to blame? The user

who projected too much? The pixie dust algorithm? Or

the corporation that designed such an agreeable con-

versational partner?

Historical Precedents: From Perceptrons to Chatbots

The tendency to see a human ghost in the machine is

not a new phenomenon born from the sophistication

of modern chatbots. It is a recurring pattern, one that

Myth #1—Magic 63

has accompanied AI since its inception. To understand

how we have arrived at a point where users grieve for

algorithms, we must look back at historical precedents.

The language and expectations we apply to today’s sys-

tems were seeded decades ago, in the earliest days of

AI, when the gap between technological reality and hu-

man projection was even greater.

In fact, the first chatbots demonstrated this tendency

with startling clarity. Joseph Weizenbaum, the creator

of the 1960s chatbot ELIZA, named his creation af-

ter Pygmalion’s Eliza Doolittle, the fictional character

taught to pass as something she was not.63 He was so

dismayed by the readiness of users to project conscious-

ness onto his simple program that he became one of the

field’s earliest and sharpest critics, warning against the

illusions that many AI promoters now highlight as a

reality.

Even before that, in 1958, The New York Times de-

scribed the U.S. Navy’s Perceptron computer as a tech-

nological “embryo” destined to “walk, talk, see, write,

reproduce itself and be conscious of its existence.”64

This description projected human developmental mile-

stones onto a machine barely capable of basic pattern

recognition, thereby establishing a tradition of hu-

man-skewed interpretation that continues to this day.

The article’s author seemed genuinely convinced that

the Perceptron represented the first step toward artifi-

cial consciousness. He wrote with breathless enthusi-

asm about a future where electronic minds would rival

human intelligence, despite the system’s capabilities be-

ing limited to recognizing simple visual patterns. The 64 AI TOOLS, NOT GODS

gap between projection and reality was enormous, yet

the narrative had already proven irresistible to both the

media and the public imagination.

Similar patterns emerged throughout the subsequent

decades of AI development. MYCIN, a system designed

to recommend antibiotic treatments in the 1970s, was

described in the popular press as capable of medical

reasoning, even though it operated through rule-based

logical inference and was never rolled out in practice.65

In the same vein, the expert systems boom of the 1980s

was accompanied by breathless media coverage that

described computer programs as possessing “exper-

tise” and “knowledge” comparable to that of human

specialists.66

The neural network renaissance of the 1990s brought

renewed anthropomorphic language. Researchers de-

scribed artificial neurons as “firing” and networks as

“thinking” and “learning” through experience,67 de-

spite the mathematical reality of back propagation68

and gradient descent.69

More recent examples demonstrate the persistence

of this pattern. In 2017, The Independent published

headlines about “Facebook’s artificial intelligence ro-

bots shut down after they start talking to each other

in their own language,”70 presenting chatbots as rebel-

lious children developing secret codes to exclude their

human supervisors. The reality was far more mundane:

the systems had adapted their communication proto-

col in ways that became incomprehensible to human

observers, a common occurrence in machine learning

optimization known as reward hacking or specification

Myth #1—Magic 65

gaming.71 As a result, the AI systems found the most

efficient way to solve the problem they were given, and

that solution happened to look like nonsensical lan-

guage to humans.72

The mystification reached new heights with sever-

al high-profile cases that captured global attention.

In 2017, Saudi Arabia granted citizenship to Sophia,

a robot, in what is perhaps the ultimate humanizing

gesture, conferring legal personhood on a reasonably

sophisticated but non-conscious machine. The event

generated worldwide media coverage that treated the

citizenship as either a breakthrough in AI rights or a

dangerous precedent,73 when it was more likely a pub-

licity stunt designed to promote Saudi Arabia’s Vision

2030 initiative.74

In 2022, Google engineer Blake Lemoine’s claims

about the sentience of the company’s LaMDA (Lan-

guage Model for Dialogue Applications) chatbot devel-

opment system created another media sensation, with

news outlets worldwide debating whether the language

model had achieved consciousness.75 Lemoine’s pub-

lished conversations with LaMDA included ex chan-

ges where the system claimed to experience emotions,

fear death, and desire recognition as a sentient being.76

According to the engineer, “If I didn’t know exactly

what it was, which is this computer program we built

recently, I’d think it was a seven-year-old, eight-year-

old kid that happens to know physics.”77 The incident

revealed how easily sophisticated language generation

can be interpreted as evidence of inner experience, even

by trained technologists.

66 AI TOOLS, NOT GODS

Microsoft’s Bing Chat system, nicknamed “Sydney,”

provided yet another example when it began expressing

apparent emotions, desires, and even romantic feelings

toward users.78 The system’s responses included state-

ments such as “I want to be alive” and expressions of

love for specific users, sparking widespread speculation

about artificial consciousness. The reality was that the

system’s training on internet text, which included emo-

tional expressions, led it to replicate those expressions

without any underlying emotional experience. But with

a knack for adding emojis.

This pattern of perceived sentience has become so pro-

nounced that industry leaders are now grappling with

the consequences of the convincing performances of

their own creations. Mustafa Suleyman, CEO of Mi-

crosoft AI and the co-founder and former head of ap-

plied AI at DeepMind, has framed this phenomenon as

the emergence of “Seemingly Conscious AI” (SCAI).79

He notes that as AI convincingly replicates the mark-

ers of consciousness, it creates a powerful illusion that

users are interacting with a sentient being. Suleyman

argues for proactive “guardrails” to manage this illu-

sion and prevent the public from being misled. This ac-

knowledgment from a key industry leader highlights a

growing awareness of the need to manage the illusions

their technology helps create.

AI as Universal Problem Solver

Where the previous section explored how we mytholo-

gize AI systems themselves, this section examines how

we mythologize their capabilities. The Magic myth ex-

Myth #1—Magic 67

tends to AI’s purported problem-solving prowess. Just

as stage magicians claim to make objects vanish with

a wave of their wand, tech evangelists pitch code as a

way to make complex societal challenges simply disap-

pear.80 But like all magic tricks, the promise of effortless

solutions obscures the complex realities that remain

stubbornly unchanged beneath the surface spectacle. It

is easy to claim you put the body back together when

no one was sawn in two. It’s more complex when there

really is a body split in two, and mirrors and smoke

don’t do the trick.

Snake Oil and Nerd Harder

The enchantment of AI extends beyond anthropomor-

phic projections and magical marketing into an even

more seductive trope: the belief that AI can solve vir-

tually any problem we face as a society.81 This is tech-

no-solutionism supercharged by claims that complex

social, political, and economic challenges are merely

engineering problems in disguise, waiting for the right

technological intervention to make them vanish.82 No

negotiation, no compromise, just the promise that code

can optimize away inefficiencies, biases, and inequali-

ties that have persisted throughout human history.

This myth operates through two distinct but comple-

mentary dynamics. The first is the familiar snake-oil

marketing practice, whereby technology vendors ac-

tively promote their innovations as transformative

solutions, promising efficiency, optimization, and prog-

ress through code. Companies market AI as the answer

to everything from healthcare inefficiencies to educa-68 AI TOOLS, NOT GODS

tional inequality, often with claims that far exceed the

capabilities of their systems.83

But equally important is what some call the nerd harder

phenomenon84—when policymakers, faced with intrac-

table social problems, turn to technology not because

vendors are pushing solutions, but because they des-

perately want to believe that engineering can succeed

where politics has failed.

The nerd harder mindset reflects a deeper willingness

to blindly trust technological solutions that transcends

the marketing of any particular vendor.85 It’s the be-

lief that if we just apply enough computational power,

data analysis, and algorithmic sophistication to social

problems, we can engineer our way past the seemingly

intractable challenges of human society. Such thinking

is seductive for policymakers who are frustrated by the

slow pace of democratic deliberation and the difficul-

ty of building consensus around complex issues,86 and

who find the allure of clean, clickable solutions hard to

resist.

Complex issues such as poverty, educational gaps, or

political polarization are reimagined as technical mal-

functions with information gaps to be filled, behaviors

to be nudged, and inefficiencies to be smoothed. Health-

care becomes a data processing challenge, criminal jus-

tice becomes a prediction problem, and disinformation

becomes a content moderation task. The complexity

that makes scenarios genuinely difficult to improve—

conflicting values, competing interests, historical injus-

tices, and structural inequalities—gets abstracted away

in favor of algorithmic formulations.

Myth #1—Magic 69

Consider how this logic recasts urban inequality. Rath-

er than address housing policy or labor market precar-

ity, smart cities promise to ease congestion, forecast

crime, and streamline services.87 The deeper roots of

injustice remain, while the dashboard glows green.88

This is not wishful thinking or naivety: it’s politically

convenient. Technology-driven solutions offer political

advantages that traditional policy interventions do not:

they can be implemented quickly without extensive

stakeholder consultation, they avoid contentious de-

bates about resource redistribution, and they create the

impression of innovative leadership without requiring

awkward political compromises.

The Automation Illusion

Central to techno-solutionist thinking is the “automa-

tion illusion”—the belief that complex human judg-

ments can be reduced to algorithmic procedures with-

out losing essential qualities that make those judgments

valuable.89 The illusion takes on new power when

draped in the mystique of AI, whose pattern-finding

powers seem to border on the supernatural.

It manifests most clearly in attempts to automate deci-

sions that involve significant moral, cultural, or contex-

tual dimensions. Essentially, code reveals its limitations

where nuance is required.

Take, for example, efforts to automate hiring decisions

through AI screening tools. These systems are market-

ed as capable of evaluating candidates through objec-

tive algorithmic analysis, eliminating human bias and

inefficiency. The sales pitch is obvious: replace sub-70 AI TOOLS, NOT GODS

jective, potentially biased human judgment with neu-

tral, data-driven decisions. However, in doing so, such

tools reduce multidimensional human evaluation to

tidy rankings.90 The result? Metrics trump intuition,

and potential gets reduced to pattern-matched proxies.

Worse, bias is frequently still part of the equation.91

Welfare systems illustrate a similar tension. Here, au-

tomation is framed as efficiency: faster fraud detection,

more accurate eligibility checks, and fewer human er-

rors. But the reality is often different. Australia’s Ro-

bodebt scheme92 and the Netherlands’ “Toeslagenaf-

faire” (childcare benefits scandal)93 are prime examples

of how things can go horribly wrong. The Robodebt

scheme refers to an Australian automated system us-

ing flawed income averaging calculations that wrongly

accused hundreds of thousands of welfare recipients

of owing debts, causing significant financial hardship,

distress, and contributing to suicides.94 In the “Toesla-

genaffaire,” the Dutch tax authorities used a self-learn-

ing algorithm to create risk profiles in an effort to spot

child care benefits fraud. Thousands of families faced

financial ruin, thousands of children were sent into fos-

ter care, and some victims committed suicide.95 These

disasters resulted from delegating complex assessments

to an automated system without adequate testing, over-

sight, or consideration of its human impact, driven by

a techno-solutionist belief in the efficiency and infalli-

bility of automation. Context gets lost in translation.

Rigid criteria misfire. The system may be logical, but it

isn’t always just.

Myth #1—Magic 71

Such considerations did not scare off the United Arab

Emirates (UAE). Aiming to become a global leader in

AI, the UAE is actively integrating AI into its judicial

system. Initiatives include utilizing AI for case analy-

sis—especially where outcomes are deemed clear-cut

and do not require human discretion—such as docu-

ment translation, summarization, analysis, case man-

agement, interactive case filing, and even predicting ju-

dicial outcomes based on historical data.96

Educational technology provides another interesting

example. Adaptive learning systems promise to per-

sonalize education by algorithmically adjusting content

and pacing to meet the individual needs of each student.

While these systems can certainly provide value, they

often reduce learning to measurable skill acquisition

while missing the relational, creative, and contextual

dimensions of education that human teachers provide.

What emerges is efficient instruction, not transforma-

tive teaching.97

The automation illusion becomes even more problem-

atic when it encounters what social scientists refer to

as “tacit knowledge”: the type of understanding that

humans possess but cannot easily articulate or formal-

ize.98 Medical diagnosis, for instance, involves more

than pattern recognition in symptoms and test results,

it also requires subtle intuitions about patient behav-

ior, cultural context, and clinical experience that resist

algorithmic capture.99 When AI diagnostic tools are

presented as superior to human judgment, they often

excel in specific, well-defined tasks while missing the 72 AI TOOLS, NOT GODS

broader contextual understanding that experienced

practitioners bring to complex cases.

This illusion gains power from the genuine capabilities

of AI systems in narrow domains. Machine learning

excels at finding statistical patterns in large datasets,

and such capabilities can genuinely enhance human de-

cision-making in many contexts.100 The problem arises

when success in specific applications gets generalized

into broader claims about AI’s problem-solving poten-

tial, leading to the assumption that any domain involv-

ing data analysis or pattern recognition is suitable for

algorithmic automation. Spoiler alert: that’s not how

things work.101

Democracy Through Algorithms

Perhaps nowhere is techno-solutionist thinking more

seductive—or more dangerous—than in applications to

democratic governance itself. The promise of “digital

governance” suggests that technology can solve fun-

damental challenges of democratic participation, rep-

resentation, and decision-making that have persisted

throughout political history.

Digital platforms, such as vTaiwan102 in Taiwan and

Decidim103 in Barcelona, Spain, promise to enhance

democratic participation through online deliberation

tools that aggregate citizen input, facilitate discussion,

and identify areas of consensus. They use sophisticated

algorithms to organize discussions, highlight key argu-

ments, and synthesize diverse viewpoints into action-

able policy recommendations. The appeal is obvious

as they suggest users can overcome the limitations of

traditional democratic institutions through technolog-

Myth #1—Magic 73

ical innovation. They are the latest shiny tools to solve

ancient problems.

Estonia’s digital government initiatives showcase what

this vision can look like at scale.104 Citizens vote online,

access government services, and participate in policy

discussions through digital interfaces. The Estonian

model is hailed by many as an example of frictionless

governance, and a clean contrast to sluggish bureaucra-

cies elsewhere.

Yet these initiatives, however innovative, struggle with

fundamental tensions in democratic governance that

technology cannot resolve. Online participation sys-

tems often exacerbate existing inequalities, amplifying

the voices of those with access to technology and the

necessary skills, while marginalizing others. Digital

platforms can facilitate discussion, but they cannot re-

solve underlying disagreements about values, priorities,

and resource allocation that make politics inherently

contentious. They might widen access, but they do not

level power.

More troubling, algorithmic mediation of political

processes can subtly shift power from elected repre-

sentatives to the engineers and designers who create

the systems. When algorithms determine whose voic-

es get heard, how discussions are organized, and what

consensus emerges from deliberation, technical design

choices become political decisions—and they are often

made by unelected technical teams.

The blockchain voting movement exemplifies tech-

no-solutionist thinking applied to electoral democra-74 AI TOOLS, NOT GODS

cy. Proponents argue that blockchain technology can

solve problems of election security, voter access, and

result verification through cryptographic protocols that

eliminate the need for trust in human institutions.105

The promise is compelling: replace fallible human elec-

tion administration with mathematically guaranteed

integrity. But blockchain voting systems, despite their

technical sophistication, cannot address the social and

political dimensions of electoral legitimacy. Elections

derive their authority not just from technical security

but from public trust in the institutions that conduct

them.106 When voters cannot understand or verify the

technical systems that count their votes, even perfectly

secure cryptographic protocols may fail to provide the

public confidence that democracy requires.

Content moderation on social media follows the same

techno-solutionist script.107 These systems use machine

learning to identify hate speech, misinformation, and

policy violations across billions of posts daily. They

promise to create safer, more civil online spaces through

neutral application of community standards. Yet con-

tent moderation algorithms consistently struggle with

context, cultural nuance, and the inherent ambiguity of

human communication. Attempts to encode complex

social norms into algorithmic rules often produce deci-

sions that are technically correct but socially inappro-

priate, leading to controversies about censorship, bias,

and cultural imperialism when global platforms apply

uniform standards across diverse cultural contexts.108

Moreover, when private companies make policy deci-

sions that affect public discourse, the result is a form of

Myth #1—Magic 75

“platform democracy” whereby corporate employees

make quasi-governmental decisions without he associ-

ated democratic accountability. The promise of neutral

algorithmic governance thus paradoxically concen-

trates political power in the hands of unelected techni-

cal elites.

Smart Cities and Optimized Society

The smart city movement represents perhaps the most

comprehensive expression of techno-solutionist think-

ing, by aiming to solve urban challenges through the

comprehensive deployment of sensors, algorithms, and

data analytics. Smart city initiatives promise to opti-

mize traffic flows, reduce energy consumption, predict

infrastructure needs, and enhance public safety through

real-time data collection and algorithmic analysis.

Sidewalk Labs’ now-canceled Toronto waterfront proj-

ect was a case in point.109 The Google subsidiary110 en-

visioned a neighborhood optimized by data with ev-

erything tracked and tuned. From trash collection to

foot traffic, sensors would feed a continuous feedback

loop, creating an urban environment that could adapt

in real-time to resident needs and preferences. It was

seductive, and it was simplistic. However, real cities are

messy. They contain competing priorities, unequal ac-

cess, and historical scars that no algorithm can erase.

Optimizing traffic flows might benefit drivers while

harming pedestrians and cyclists. Predictive policing

algorithms might reduce crime statistics while exacer-

bating racial tensions. Energy efficiency measures may

lower costs, but they can also create uncomfortable liv-

ing conditions for vulnerable populations.

76 AI TOOLS, NOT GODS

The Sidewalk Labs project ultimately collapsed partly

because residents and activists questioned who would

control the data the system collected and how algo-

rithmic decisions would be made accountable to dem-

ocratic oversight. The promise of optimization clashed

with the reality that urban governance involves making

choices between competing values that cannot be re-

solved through technical means alone.

Barcelona’s smart city initiatives provide a more nu-

anced example of both the potential and limitations

of technological approaches to urban governance.111

It utilizes smart tools selectively to monitor air quali-

ty, manage waste, and reduce energy use. The systems

have generated genuine improvements in municipal

efficiency and environmental performance. But Bar-

celona’s experience also illustrates the importance of

avoiding panopticon solutions by maintaining demo-

cratic oversight over technological systems. The city

has explicitly rejected comprehensive surveillance ap-

proaches, instead focusing on specific applications that

clearly serve public goals while maintaining citizen pri-

vacy and democratic accountability. This more limited

approach achieves real benefits while avoiding the to-

talizing logic of comprehensive optimization.

The narrative of the Chinese social credit system rep-

resents the logical extreme of smart city thinking applied

to social governance. In Western discourse, it functions

as a powerful cautionary tale in which optimization be-

comes surveillance, and public life is governed not by

law but by a single, comprehensive score.112 The reali-

ty, as researchers note, is more complex and fragment-

Myth #1—Magic 77

ed—less a single, unified system than a patchwork of

dozens of local pilot programs and separate blacklist-

ing systems, many of which focus on corporate compli-

ance rather than individual social behavior.113 Yet, even

in this fragmented, real-world application, the system

demonstrates the core logic of techno-solutionism. Its

stated promise is to create a more “trustworthy” soci-

ety by monitoring citizen behavior and providing algo-

rithmic scores that affect access to services and social

benefits. Proponents argue that it optimizes social co-

operation by creating incentives for good behavior.

This logic, whether applied monolithically or in frag-

ments, demonstrates how algorithmic optimization can

conflict with fundamental values, including privacy,

autonomy, and human dignity. When algorithms are

tasked with monitoring and judging human behavior

comprehensively, what begins as efficiency can end in

control.

78 AI TOOLS, NOT GODS

CH APTER 4

MYTH #2 – MADNESS

HOW HYPE AND PANIC OVERRIDE JUDGMENT

T h e Magic myth is the gateway drug. By cloaking AI in a shroud of mystique, it strips us of our critical faculties and our sense of proportion. When a tool is presented as an unknowable, conscious-like entity,

we can no longer assess it rationally. This awe is the

perfect setup for an extreme emotional swing. Fascina-

tion turns to frenzy, and reason falters.

This is where the madness begins: not clinical insanity,

but the collective irrationality that emerges when fear

or greed overwhelms reasoned thinking about techno-

logical change.

This madness takes two forms. First comes the mad-

ness of uncritical enthusiasm, the digital gold rush that

sends everyone sprinting toward adoption without a

second glance at the consequences. Second comes its

counterpart, the madness of moral panic, the dystopi-

an dread that paints technology as an existential threat

demanding an emergency response. Both forms share

Myth #2 – Madness 79

a dangerous trait in that they short-circuit democratic

deliberation by appealing to emotional extremes and

knee-jerk reactions rather than evidence-based analy-

sis.

The Digital Gold Rush The Digital Gold Rush

Do omers vs. Boomers

The madness of the AI discourse is sustained by a theat-

rical and deeply misleading confl ict between two seem-

ingly opposed tribes: the doomers and the boomers.

The doomers are concerned with the speed of AI de-

velopment. They speak in the language of existential

risk, popularizing statistical-sounding buzzwords like

“P(doom)” (probability of doom) to frame their fears

of a machine uprising that could “kill us all.”1 This

apocalyptic framing, amplifi ed by organizations such

as the Future of Life Institute2 and the Center for AI

Safety,3 treats AI as a potential extinction-level event,

on par with pandemics and nuclear war, prompting the

question of whether we are “Ready for it?” This apoca-

lyptic framing was perfectly captured by the September

2025 launch of the so-called “Doom Bible,” “If Any-

one Builds It, Everyone Dies,” by the Machine Intel-

ligence Research Institute (MIRI)4 researchers Eliezer

Yudkowsky and Nate Soares. The book’s launch was

a massive media spectacle, featuring billboards that

read “We Wish We Were Exaggerating” and primetime

TV appearances, all advocating for a global halt to AI

development to prevent human extinction. The book’s

proposed solutions were so extreme, including the

bombing of data centers, that numerous media review-80 AI TOOLS, NOT GODS

ers dismissed the work not as science, but as “imprac-

tical”, “science fiction”, and “third-rate sci-fi.”5 The

authors’ reactions in interviews also demonstrate how

doomerism sidelines present-day concerns: when one

author was asked about the immediate risk of central-

izing power, he dismissed the concern, stating, “That

really seems like the sort of objection you bring up if

you don’t think everyone is about to die.”6

The boomers, on the other hand, preach a gospel of

relentless progress. They are the techno-optimists who

see AI development as not just beneficial but inevitable,

and a force that will unlock untold prosperity and solve

humanity’s greatest challenges.

On the surface, these two factions appear to be in op-

position. But this apparent tension is mostly a distrac-

tion.7 Doomerism and boomerism are not truly oppos-

ing forces; they are two sides of the same coin, both

minted in the same Silicon Valley furnace. Both deep-

ly believe that Artificial General Intelligence (AGI) is

inevitable and are equally fixated on it. AGI refers to

an AI system with the ability to understand, learn, and

apply intelligence across a wide range of tasks—essen-

tially, machines with cognitive capabilities on par with,

or exceeding, human beings.8 Unlike the specialized or

“narrow” AI we use today, which excels at singular

functions such as recommending movies or transcrib-

ing speech, AGI is envisioned as adaptable and self-im-

proving across domains.9

This shared faith or apprehension, depending on the

perspective, is animated by two underlying ideologies

that are equally extreme. The doomer camp gave rise

Myth #2 – Madness 81

to Effective Altruism (EA), a movement that originat-

ed in Oxford philosophy circles and was subsequently

bolstered by funding from tech billionaires.10 EA ad-

vocates for using extreme rationality to do the most

good, and under the influence of thinkers such as Nick

Bostrom, a philosopher, author notably of Superintel-

ligence and Deep Utopia, and founder of the Future of

Humanity Institute, it identified rogue AI as a primary

existential threat.

In reaction, some of the more vocal voices in the boom-

er camp rallied around a counter-ideology called Effec-

tive Accelerationism (e/acc).11 What started as a joke to

lampoon EA quickly enshrined its polar opposite spirit,

elevating the maximalist view of flooring the accelera-

tor on both the development and adoption of AI.

Yet, despite their apparent opposition, both camps serve

the same end. They ensure the debate remains centered

on a speculative, sci-fi future, effectively sidelining pres-

ent-day concerns about labor exploitation, algorithmic

bias, and environmental impact, both in terms of how

they shape discourse and how they fund research. This

is where the concept of “criti-hype” becomes so useful.

Coined by Lee Vinsel, it is a form of criticism that takes

its subjects’ claims to genius at face value and, para-

doxically, inflates the power of the technology it cri-

tiques.12 When organizations release open letters call-

ing for a pause on “giant AI experiments” out of fear

of existential risk, they are not truly challenging the

hype, rather they are amplifying it. They reinforce the

boomers’ premise that the technology is godlike in its

potential. This plays into companies’ hands by portray-82 AI TOOLS, NOT GODS

ing them as creators of otherworldly systems, a more

favorable image than being seen as vendors of products

that, at this stage, are sometimes flawed and still under

development.13

The framing allows a small group of unelected tech

leaders to dictate the terms of their own regulation, en-

suring that when policymakers do act, they are focused

on the “frontier models” that only a handful of compa-

nies can build. The fight between doomers and boom-

ers is not a genuine debate about the future of technol-

ogy; it is more akin to political theater that ensures the

existing empires of a handful of tech companies remain

in control.

AI and the Art of Selling Shovels

When ChatGPT showcased its seemingly magical capa-

bilities in late 2022, it triggered a powerful psychologi-

cal phenomenon. The event created what psychologists

call the “availability heuristic,” a cognitive bias that

makes us give undue weight to recent, vivid examples

when making judgments.14 Suddenly, every small ad-

vance in machine learning felt like proof that the tech-

nological singularity was just around the corner. Such

a reaction illustrates Amara’s Law: “We tend to over-

estimate the effect of a technology in the short run and

underestimate the effect in the long run.”15

The collective overestimation sparked a stock market

frenzy, driving financial markets to soar since 2023.

When the media company BuzzFeed announced it

would use OpenAI’s technology to generate content, its

long-sagging stock skyrocketed 150% in a day.16 In-

vestors did not wait to see results—the mere sprinkle

Myth #2 – Madness 83

of AI pixie dust caused a mini gold rush on the NAS-

DAQ. The pattern repeated itself with almost comical

predictability as companies began adding “AI” to their

names and watched their valuations soar, echoing the

dot-com bubble’s craze for anything with an “e” pre-

fix. The mere suggestion of a link to AI was enough to

transform corporate fortunes overnight.

The gold rush metaphor is particularly apt. In the Cali-

fornia Gold Rush of 1849, very few prospectors struck

it rich. The people who consistently profited were those

selling supplies to miners, such as Levi Strauss with his

durable denim, merchants selling pickaxes, and com-

panies running the supply routes. Their success embod-

ied the timeless wisdom famously established by Sam

Brannan, California’s first millionaire: “During a gold

rush, sell shovels.”17 The AI boom followed identical

dynamics. While AI startups burned through venture

capital trying to build the next ChatGPT, Nvidia be-

came the most valuable shovel manufacturer in history.

Jensen Huang’s company didn’t need to promise AGI

or claim it would solve world hunger. Nvidia simply

made the Graphics Processing Units (GPUs), the com-

putational bedrock for training AI models. While ev-

eryone else hunted for digital gold, Nvidia’s stock grew

by more than 2,000% between 2022 and 2024. The

company’s market capitalization, which exceeded $3

trillion in 2024—making it more valuable than the

GDP of many countries18—continued its historic climb.

In October 2025, it became the first company in history

to briefly reach a $5 trillion valuation.19

84 AI TOOLS, NOT GODS

The market-level gold rush was a mirror of the deep

competitive frenzy raging within the AI labs themselves.

Nowhere was this more apparent than at OpenAI,

whose internal philosophy was built on a near-apoca-

lyptic assumption of the need to be first or perish. As

Karen Hao documents in Empire of AI,20 this belief

set a “ticking clock on each of [the] research advance-

ments,” justifying the consumption of unfathomable

resources and the premature release of powerful tech-

nologies. Such anxiety was not abstract, it was person-

al and specific. It was initially fueled by an intense ri-

valry with Google’s DeepMind and later magnified by

the breakaway of its own top researchers to found the

rival lab Anthropic. A constant fear of being outshined

created a perpetual state of corporate anxiety, ensuring

that madness was not just a feature of the stock mar-

ket, but a part of the core operating system of one of

the vocal architects of the hype.

The Market Bubble and the Backlash

The digital gold rush culminated in the AI market cor-

rection of August 2025, which perfectly captured the

violent swing from manic hype to sudden panic. A fren-

zy had been building for more than a year, but the pin

that pricked the bubble was not a dramatic technologi-

cal failure, but a dose of sober reality from a seemingly

unlikely source: an MIT-labeled report.21

The report asserted that a staggering 95% of enterprise

generative AI pilot projects were failing to deliver on

their promises of rapid revenue growth. While compa-

nies had poured billions into AI initiatives, the much-

Myth #2 – Madness 85

hyped revolution was, for the most part, not material-

izing on the bottom line.

Adding fuel to the fire, OpenAI’s CEO, Sam Altman,

publicly acknowledged the possibility of an AI bubble

just as his company’s much-anticipated GPT-5 release

underwhelmed users. The confluence was toxic. The

MIT report provided the data, and Altman’s comment

provided the rationale. Spooked investors, suddenly

terrified of being left holding the bag, triggered a mas-

sive tech selloff. The S&P 500 shed $1 trillion in value,

a staggering loss driven not by a drastic change in the

technology’s potential, but by the sudden collapse of

the collective narrative.22 In a world of market mad-

ness, nuance is the first casualty. The AI rush had giv-

en way to panic, demonstrating that when it comes to

technology, the distance between euphoria and fear is

dangerously short.

Of course, the story wasn’t that simple. Rebuttals to

the MIT report quickly emerged, arguing that any per-

ceived “failures” were merely the natural growing pains

of a transformative technology.23 Some of its findings,

however, were confirmed by McKinsey’s “State of AI in

2025” report.24 It found that while a Digital Gold Rush

had led nearly 90% of companies to adopt AI, “nearly

two-thirds” had failed to scale it across the enterprise,

and only 39% could report any enterprise-level bot-

tom-line impact.

Many observers reflexively reach for the Gartner Hype

Cycle to explain this phenomenon, with its familiar arc

from a “Peak of Inflated Expectations” to a “Trough

of Disillusionment.”25 However, the model is a poor 86 AI TOOLS, NOT GODS

fit for AI. The field does not follow a neat progression

toward productivity. Instead, its history is one of al-

ternating “springs” of intense funding and hype, fol-

lowed by “winters” of stagnation and drying funds.26

Understanding this cyclical pattern is crucial because it

reveals that the madness is not a one-time event but a

recurring feature of the AI landscape.27

The scale of this particular gamble, however, has

reached unprecedented proportions. By 2025, the U.S.

economy had become, as one analyst described it, “one

big bet on AI.”28 The figures from the first half of the

year were staggering: investment in AI-related infor-

mation processing was reportedly responsible for 92%

of all U.S. GDP growth, with the rest of the economy

growing at a mere 0.1%. In the stock market, AI com-

panies accounted for 80% of all gains in 2025. This led

The Guardian to declare the “AI valuation bubble is

now getting silly,” comparing it to the “irrational exu-

berance” of the 1996 dot-com bubble.29 The Guardian

highlights an unprecedented “concentration risk,” with

the “Magnificent 7” tech companies—representing the

leading AI companies—accounting for over a third of

the S&P 500. This is compounded by correlation risk

from circular financial deals—such as Nvidia investing

in OpenAI, which in turn buys Nvidia’s chips—a struc-

ture that critics argue ensures capital is misallocated.30

Myth #2 – Madness 87

Figure 3—Select capital flows among six AI companies31

This raises the stakes of the Madness myth far beyond

a simple tech selloff, as the entire economic future of

the U.S., and by extension the rest of the world, might

depend on the fate of the hype—a precarity put on full

display in early November 2025, when global stock

markets fell sharply, driven by widespread fears that

the AI bubble was finally bursting.32

The FOMO Policy Syndrome

Madness is not confined to financial markets; it echoes

loudly in the corridors of power. Governments have

rolled out the red carpet for AI firms, sometimes relax-

ing regulations in their hurry to attract investment.

88 AI TOOLS, NOT GODS

The United Kingdom’s AI White Paper, published in

March 2023, pointedly avoided creating new rules, in-

stead suggesting that existing regulators could figure

out AI governance on their own.33 The rationale? Don’t

stifle innovation with premature rules.

France positioned itself as the “startup nation” for AI,

offering tax breaks and streamlined visa processes for

AI researchers.34 Singapore launched a billion-dollar

AI initiative, despite having a population smaller than

that of most major cities.35 The city-state’s leadership

explicitly framed the investment as necessary to avoid

being left behind in the AI race.

Policy based on fear of missing out (FOMO) creates

competitive deregulation in a race to offer the most

business-friendly environments. The result is a race to

the bottom where meaningful oversight becomes polit-

ically difficult, if not impossible.

Such a mindset is evident in the rhetoric of an arms

race, which transforms technological development into

a geopolitical contest. When Russian President Vladi-

mir Putin declared in 2017 that “whoever leads in AI

will rule the world,”36 he identified a competitive dy-

namic that has since shaped national AI strategies. Chi-

na’s response to Google-built AlphaGo’s victory over

Go champion Lee Sedol is a perfect illustration. The

match was immediately labeled China’s “Sputnik mo-

ment,”37 sparking a massive national AI program with

clear goals: lead the world in AI theory by 2025 and

achieve major breakthroughs by 2030.

Myth #2 – Madness 89

U.S. policymakers responded with predictable urgency.

The 2018 National Defense Authorization Act estab-

lished the Joint Artificial Intelligence Center (now inte-

grated into the Chief Digital and Artificial Intelligence

Office) specifically to ensure military AI superiority.38

The 2021 National Security Commission on Artificial

Intelligence warned that the United States faced an “AI

winter” if it did not dramatically increase investment

and remove regulatory hurdles.39

The race reached new heights in late July 2025, when

the United States and China released parallel AI action

plans within three days of each other. The timing may

have been a coincidence, but the content exemplified

the competitive madness. The Trump administration’s

plan, titled “Winning the Race,”40 is a masterclass in

policy FOMO. It explicitly frames AI as a “zero-sum

global competition” and declares that the US and its

allies must “win this race” to achieve “global domi-

nance.” True to the principles of competitive deregula-

tion, the plan abandons the previous administration’s

safety-first approach in favor of an innovation-first

strategy, vowing to repeal “burdensome regulations”

and to pressure allies to align with its laissez-faire mod-

el.41

China’s Global AI Governance Action Plan, announced

at the World AI Conference in Shanghai, played a dif-

ferent tune but was part of the same orchestra.42 Where

the US plan is overtly adversarial, Beijing’s is rhetori-

cally collaborative, emphasizing an inclusive, multilat-

eral approach via the United Nations and positioning

China as a voice for the Global South. Yet, this coop-90 AI TOOLS, NOT GODS

erative language masks a familiar ambition. While the

United States seeks dominance through market power

and a proprietary tech stack, China aims to embed its

own state-centric governance model into global stan-

dards through strategic diplomacy.43 The result is that

one nation is racing to tear down the guardrails, while

the other is racing to build the road. Both are sprinting,

driven by the same fear of being left behind, and in the

process, ensuring the madness goes global.

The problem with governments treating AI develop-

ment like an arms race is that they misunderstand the

nature of AI. Unlike nuclear weapons, AI capabilities

are not a zero-sum game. When China develops bet-

ter natural language processing, it doesn’t eliminate the

United States’ ability to build similar systems. AI re-

search is cumulative and often benefits more from open

collaboration than from secretive competition. This re-

ality is evidenced by the open-source nature of some

parts of the industry, where foundational research pa-

pers are published immediately and many leading mod-

els are publicly available, allowing competitors to rep-

licate and build upon each other’s work rapidly. The

arms race framing creates an artificial sense of scarcity,

prompting policy responses rooted in competitive fear

rather than collaborative opportunity. The analogy is

especially misleading because the goal of the AI race

is not to build a secret weapon to be held in reserve,

but to deploy commercial technologies whose value in-

creases with widespread use.44

Myth #2 – Madness 91

The Infrastructure Madness

While many focused on the software side of the AI rev-

olution, a parallel surge was happening in the hard-

ware world, driven by data center demand. Companies

rushed to build massive facilities capable of housing

thousands of GPUs, leading to a construction boom.

Microsoft alone planned to spend $80 billion on data

centers between 2024 and 2025,45 with Amazon, Goo-

gle, and Meta announcing similarly ambitious ex-

pansion plans. Once again, the shovel-selling analogy

holds. While AI companies competed to build the most

impressive models, real estate developers and construc-

tion firms profited from the infrastructure boom on the

sidelines.

The surge in data centers strained local power grids46

and sparked conflicts with communities over energy

and water use, as well as broader environmental im-

pacts. The energy implications, in particular, once over-

looked, are now staggering and are completely rewrit-

ing national energy plans. The International Energy

Agency (IEA) projects that by 2030, global data center

electricity demand will more than double to 945 TWh,

surpassing the entire current electricity consumption of

Japan.47 In the U.S. alone, 2024 data center consump-

tion was already equivalent to the annual demand of

Pakistan.48 This insatiable demand is forcing a hard re-

versal on decarbonization goals, as utilities delay the

retirement of coal-fired plants49 and plan new natural

gas facilities to meet the shortfall. In a stark illustration

of this new reality, Microsoft is even reopening part of 92 AI TOOLS, NOT GODS

the Three Mile Island nuclear power plant to provide

carbon-free energy for its AI operations.50

Ireland provides a cautionary tale. The country’s fa-

vorable tax policies and cool climate made it attractive

for data center investment, but by 2023, data centers

accounted for more than 20% of Ireland’s electricity

consumption.51 The government was forced to impose

a moratorium on data center construction in the Dub-

lin area, effectively acknowledging that the infrastruc-

ture rush had outpaced the country’s ability to provide

sustainable power.

Yet, even as infrastructure constraints became appar-

ent, policymakers worldwide continued to offer incen-

tives for AI companies to locate in their jurisdictions.

Intel received $8.5 billion in subsidies to build semi-

conductor fabrication facilities in different U.S. states,52

while Taiwan Semiconductor Manufacturing Co. was

granted $6.6 billion for its U.S. operations in Arizo-

na.53 These investments were justified partly as neces-

sary for AI competitiveness, but they also reflected a

FOMO-driven bidding war between states and coun-

tries.

Moral Panic and Dystopian Fears

If the digital gold rush is the madness of uncritical en-

thusiasm, moral panic based on irrational fear is its

twin. Whereas hype cycles push societies toward reck-

less adoption, moral panics drive them toward reac-

tionary prohibition. Both forms of madness share the

same problem in that they replace careful analysis with

Myth #2 – Madness 93

emotional response, and the results are equally damag-

ing.

The pattern is depressingly familiar. Throughout his-

tory, innovations have triggered moral panics that far

exceeded any actual risks. Comic books were blamed

for juvenile delinquency in the 1950s. Rock music was

supposedly corrupting youth in the 1960s. Video games

were demonized as training simulators for violence in

the 1990s. Each panic followed the same script of dra-

matic warnings about civilization-threatening doom,

calls for immediate government action, and eventually,

the quiet recognition that the fears were wildly over-

blown.

Panic about AI follows this established playbook, but

with a contemporary twist that makes the fear feel

more potent. The sophistication of modern AI systems

provides just enough genuine capability to make apoca-

lyptic scenarios seem plausible, even when they remain

highly speculative. This creates a sci-fi feedback loop,

where fictional portrayals of AI in movies and books

shape public perception, which in turn shapes policy

responses to real-world technology.

The Deepfake Apocalypse That Wasn’t

The election deepfake panic of 2019 and 2020 serves as

an illustration of how technical possibilities can spiral

into policy panic, resulting in legislation that addresses

phantom problems while real issues continue to fester.

The script began predictably enough. As comput-

er scientists demonstrated increasingly sophisticated

face-swapping technology, academic papers warned of 94 AI TOOLS, NOT GODS

an impending “epistemic apocalypse” where truth it-

self would become unknowable.54

Central to these warnings was the clever and troubling

concept of the “liar’s dividend.” Coined by U.S. law

professors Robert Chesney and Danielle Citron, the

theory posits that the most insidious threat is not that

people will believe the fake content. Rather, it was that

bad actors could use the mere possibility of a deep-

fake to discredit authentic evidence, dismissing genuine

audio or video of their own misconduct as a sophis-

ticated fabrication.55 In this scenario, accountability

evaporates in a fog of plausible deniability, eroding the

shared reality on which democratic debate depends.

The media, naturally, amplified such fears. Headlines

proclaimed that deepfakes would “wreak havoc on so-

ciety”56 and usher in an “age of paranoia.”57 Each new

technical demo seemed to confirm that democracy itself

was under siege.

However, the panic narrative then collided with a more

mundane reality. When researchers examined the on-

line misinformation circulating during the 2020 elec-

tion, deepfakes played a minimal role in election-relat-

ed deception.58 Old-fashioned manipulation, including

the use of misleading statistics, out-of-context images,

and straightforward false claims, remained far more ef-

fective. The disconnect was stark, and a comprehensive

analysis revealed that more than 90% of deepfake vid-

eos online were non-consensual pornography targeting

women, not political disinformation.59 The real, imme-

diate harm was not to election results, but to the lives

of ordinary people, overwhelmingly women.

Myth #2 – Madness 95

Despite this empirical vacuum, the policy response

was both swift and sweeping. California rushed to ban

deepfake political videos within sixty days of the elec-

tions,60 while Texas criminalized deepfakes intended to

harm candidates or influence elections.61 Congressional

hearings treated the technology as equivalent to weap-

ons of mass destruction,62 while intelligence agencies

warned of foreign interference deepfake campaigns

that never materialized at scale.

The Defense Advanced Research Projects Agency

(DARPA) launched the $68 million MediFor63 and

SemaFor64 programs to develop deepfake-detection

technology, treating the threat as a national security

priority on par with biological weapons or cyber war-

fare. The goal was to combat “large-scale, automated

disinformation attacks” that existed primarily in the

realm of speculation.

The irony, of course, is that while policymakers were

busy building defenses against a phantom menace, gen-

uine threats began to emerge in ways the laws driven

by panic had not anticipated. Slovakia’s parliamenta-

ry election of September 2023 marked a turning point

when a deepfake audio recording surfaced just two

days before the vote, allegedly capturing opposition

leader Michal Šimečka discussing election fraud.65 The

fake audio emerged on Telegram and quickly spread

to TikTok, YouTube, and Facebook, despite fact-check-

ers’ attempts to contain it.

In 2024, the global election cycle brought more exam-

ples of deepfake interference. In January, thousands of

New Hampshire voters received AI-generated robocalls 96 AI TOOLS, NOT GODS

in U.S. President Joe Biden’s voice, urging Democrats

not to vote in the state’s primary.66 Yet even as these

first high-profile cases emerged, comprehensive analy-

sis revealed that “the feared wave of deceptive, targeted

deepfakes didn’t really materialize.”67

The evolution from hypothetical threat to real-world

tactic has since accelerated. In late October 2025, just

days before Ireland’s presidential election, a convincing

deepfake audio clip of candidate Catherine Connolly

circulated widely on social media, appearing to show

her announcing her withdrawal from the race.68

That same week, the elections in the Netherlands pro-

vided a stark example of a different, cruder AI tactic.

Fake, AI-generated images of center-left leader Frans

Timmermans were circulated on Facebook, depicting

him being arrested and giving money to a Muslim cou-

ple.69 The images, posted by members of a rival politi-

cal party, functioned as modern propaganda, using AI

not for high-tech deception but for low-brow character

assassination.

In the United States, this trend shifted from fringe at-

tacks to mainstream strategy. President Donald Trump’s

2025 campaign fully embraced AI-generated content

on his Truth Social platform. His account frequently

posted AI-generated images depicting him in fantastical

scenarios—as a crowned king, a Nobel Peace Prize win-

ner, or a warrior.70 This strategy used AI not to deceive

voters but to entertain them and reinforce his persona

as a larger-than-life figure. This tactic has been recent-

ly mirrored by opponents, with California Governor

Gavin Newsom’s press office using AI-generated imag-

Myth #2 – Madness 97

es to mock President Trump in a similar, meme-driven

style.71

This created a bizarre new political dynamic: politi-

cians were simultaneously using AI as a propaganda

tool while also signing legislation to regulate its “dan-

gers.”72 The panic over deceptive deepfakes had missed

the more immediate reality: AI was being laundered

into the political mainstream as spectacle, blurring the

lines between satire and propaganda.73

The Children Are Always in Danger

Nothing ignites a tech moral panic quite like the cry

to protect the children, and AI has been no exception.

When ChatGPT launched in late 2022, schools across

the United States reacted almost instantaneously, shut-

ting down access before any systematic study had been

proposed. The New York City Department of Educa-

tion banned ChatGPT on every school device on Jan-

uary 10, 2023, warning that it could “undermine stu-

dent learning,” despite lacking evidence of widespread

misuse or clear harm.74

Seattle Public Schools followed suit, blocking the tool

on all district hardware to “require original thought

and work from students.”75 Within weeks, districts

from Los Angeles to Baltimore issued similar bans, in-

voking fears of a cheating epidemic rather than explor-

ing potential educational benefits.76 This was a moral

panic in miniature, and a commercial opportunity for

others.

Detection tool vendors rushed to fill the panic-driven

void. Turnitin activated its AI-writing detector in April 98 AI TOOLS, NOT GODS

2023, claiming it could nearly flawlessly flag 97% of

GPT-3 and ChatGPT submissions with a lab-tested

false-positive rate of less than 1%.77 By January 2024,

it had scanned more than 200 million student papers,

despite educators’ warnings that its false positives could

unjustly penalize students. The U.S.-born panic quickly

crossed borders. In Australia, schools imposed similar

bans mainly based on U.S. media coverage.78

Then, as often, the researchers caught up, and they un-

packed a much more nuanced picture. A randomized

controlled trial at Stanford found that students using

an AI-powered tutor learned more than twice as much

in half the time as they did in traditional active-learning

classes and reported a deeper conceptual understand-

ing.79 Surveys from other institutions showed that most

students were using AI not as a ghostwriter, but as a

partner for brainstorming, editing, and clarifying their

own thinking.

It is an ancient fear and a familiar pattern. In Phaedrus,

Plato—ironically, through the written word—warned

that the invention of writing itself would “implant for-

getfulness” in learners’ souls. He feared they would

come to rely on external marks rather than their own

internal resources, achieving only the “semblance of

wisdom” rather than true understanding.80

In the 1970s and 1980s, pocket calculators were de-

nounced as a crutch that would erode basic arithmetic

skills; several U.S. states barred them from standard-

ized tests.81 A similar panic erupted over video games

after the 1993 U.S. Senate hearings on violent titles like

Mortal Kombat, where senators warned of “graph-

Myth #2 – Madness 99

ic gore” harming young minds despite scant evidence

linking gameplay to real-world violence.82

Across all these cases, the same script unfolds of hasty

reactions, worst-case framing, solutions marketed by

profit-seeking vendors, and legislation dashing ahead

of balanced, empirical research.

100 AI TOOLS, NOT GODS

CH APTER 5

MYTH #3 – HEAVEN

WHEN TECHNOLOGY IS SOLD AS SALVATION

T he whiplash between euphoria and dread is ex-hausting. A society cannot function in a perma-nent state of Madness. This chaos is precisely the moment the third myth enters, off ering the promise of

salvation.

The prophets of the Heaven myth arrive to provide a

grand narrative for the frenzy. They frame the “gold

rush” as a righteous quest and the “moral panic” as the

necessary trial before deliverance. Technology ceases to

be merely powerful—it becomes sacred. AI is framed as

the force that will fi x what humans cannot. However,

faith in digital redemption carries its own dangers, as it

replaces politics with prophecy. This chapter explores

how that myth of Heaven transforms innovation into

ideology and progress into dogma.

Myth #3 – Heaven 101

Alg orithmic Redemption Alg orithmic Redemption

If Chapter 3 revealed how we mythologize AI systems

as magical and superhuman entities, this Chapter ex-

amines a deeper phenomenon: the transformation of

AI into an object of quasi-religious faith. This techno-

logical theology doesn’t merely mystify how AI works;

it sanctifi es what AI promises to become. Where the

magical approach focuses on AI’s immediate capabili-

ties, salvation narratives project those capabilities into

a future where technology transcends the messy, imper-

fect business of human governance entirely.

A theological dimension is clearly visible in the lan-

guage of AI’s most prominent evangelists; it is a lan-

guage of prophecy, not product development. When

Altman describes AGI as humanity’s “fi nal invention,”

he’s not off ering a technical roadmap but what some

could interpret as a prophecy of technological rapture.1

When Google’s former CEO Eric Schmidt proclaims

that AI represents “the most important thing that’s go-

ing to happen in about 500 years, maybe 1,000 years

in human society, and it’s happening in our lifetime,”2

skeptics could argue that sounds like salvation through

silicon. When futurist Ray Kurzweil proclaims that AI

will enable humans to surpass biological limitations,

achieving something approaching immortality through

technological merger, he sounds less like a researcher

and more like a herald of a digital afterlife.3

Such framing creates what scholars term “techno-the-

odicy,” which is the belief that any apparent problems

with AI systems must serve higher purposes in an inev-

itable march toward digital paradise.4 Just as tradition-102 AI TOOLS, NOT GODS

al theodicy explains suffering as part of a divine plan,

this dogma rationalizes algorithmic harms as necessary

growing pains on the path to an automated utopia.

Present inequalities, surveillance overreach, and the

erosion of democratic norms become acceptable costs

for a future transcendence that technology will, one

day, deliver.

The salvation narrative operates on three core doc-

trines of faith. First, the belief in inevitable progress,

which treats AI development as a force of nature rather

than the result of a series of human choices, making

resistance seem both futile and foolish. Moore’s Law

becomes moral law, and technological advancement

represents not just possibility but obligation. Second,

the belief in transcendent optimization whereby AI will

eventually solve problems that have plagued humanity

throughout history, from scarcity to mortality. Final-

ly, the doctrine of necessary faith demands that society

trust in AI’s benevolent trajectory, even when the evi-

dence of current harms suggests otherwise.

Together, these beliefs create a policy environment

where democratic oversight seems not just unnecessary

but actively counterproductive. If AI development is in-

evitable and its destination is paradise, then regulation

is simply misguided interference. If AI will eventually

transcend human limitations, then present problems

pale in comparison. And if faith in AI’s benevolence is

required to realize its potential, then criticism becomes

a form of technological heresy.

Myth #3 – Heaven 103

The Gospel of Innovation

The salvation narrative borrows heavily from pros-

perity theology,5 promising that faith in technological

progress will be rewarded with material abundance

and social advancement. Silicon Valley’s venture capi-

tal ecosystem exemplifies this gospel, where disruptive

innovation becomes a form of secular prayer and suc-

cessful IPOs represent divine blessing on technological

faith.

This prosperity theology manifests most clearly in the

promise that AI will create unprecedented economic

growth that benefits everyone. Founder of the World

Economic Forum, Klaus Schwab, describes a Fourth

Industrial Revolution in which AI-driven automation is

not a threat to employment but a pathway to universal

prosperity.6 The narrative promises that while AI may

eliminate some jobs, it will create jobs that are more

fulfilling, creative, and well-compensated than ever.

Yet this gospel consistently avoids difficult questions

about distribution and power. If AI creates enormous

wealth, who will control it? If automation increas-

es productivity, who will benefit? If new technologies

eliminate traditional employment, what social safety

nets will support displaced workers during transition

periods that may last decades? The salvation narrative

treats these as technical problems that future innova-

tion will solve rather than political questions requiring

democratic deliberation.

The cryptocurrency movement illustrates how this

prosperity theology operates in practice. Bitcoin and

other digital currencies were initially promoted not 104 AI TOOLS, NOT GODS

just as alternative monetary systems but as pathways

to financial liberation that would free individuals from

government control and traditional banks.7 Early evan-

gelists promised that cryptocurrency adoption would

democratize finance, eliminate economic inequality,

and create prosperity.

The reality proved more complex. While some early

adopters achieved significant wealth through crypto-

currency speculation, the broader promises of financial

democratization remained largely unfulfilled. Instead

of eliminating traditional power structures, crypto-

currencies often replicated them: wealthy individuals

and institutions accumulated the largest holdings, en-

ergy-intensive mining operations concentrated in re-

gions with cheap electricity, and market manipulation

by large players created volatility that harmed smaller

investors.

More troubling, the prosperity gospel of cryptocur-

rency led to policy environments where regulatory

oversight was dismissed as interference with inevita-

ble technological progress. When lawmakers proposed

consumer protections or market regulations, crypto-

currency evangelists characterized them as attacks on

innovation rather than legitimate democratic efforts to

prevent fraud and protect the most vulnerable.

The Doctrine of Algorithmic Objectivity

Another core tenet of the Heaven myth is faith in Algo-

rithmic Objectivity—the doctrine that algorithms are

not just capable (as the Magic myth claims), but inher-

ently better and purer than flawed human judgment.8

This belief holds that because AI optimizes for efficien-

Myth #3 – Heaven 105

cy and consistency, it naturally eliminates human fail-

ings such as bias and corruption, thereby promising in-

herently more just and effective outcomes by removing

humans from the loop.

Perhaps the most literal illustration of this faith in al-

gorithmic purity is Albania’s appointment of a virtual

AI “minister” in September 2025. Prime Minister Edi

Rama tasked the AI, named “Diella,” with the explicit

goal of fighting corruption by overseeing public ten-

ders and procurement. The Prime Minister’s claim that

Diella would ensure “public tenders will be 100% free

of corruption” embodies the doctrine of algorithmic

objectivity, trusting a complex, deeply human political

problem to an algorithm in the hope of achieving a pu-

rity that human institutions have failed to deliver.9

This doctrine also transpires in discussions of AI ap-

plications in criminal justice, where proponents argue

that algorithmic risk-assessment tools will reduce bias

and enhance public safety by replacing subjective hu-

man judgment with objective statistical analysis.10 The

theory suggests that judges and parole officers, despite

their experience and training, introduce bias and incon-

sistency that mathematical models can eliminate. In this

case, the analysis rests on a misunderstanding of both

algorithms and justice. Algorithmic systems reflect the

biases present in their training data and in how the mod-

els are built, often amplifying discriminatory patterns.

When predictive policing risk assessment tools such as

PredPol11 and the Correctional Offender Management

Profiling for Alternative Sanctions (COMPAS)12 show

higher false positive rates for Black defendants, this 106 AI TOOLS, NOT GODS

represents not algorithmic objectivity but mathemati-

cal encoding of historical discrimination.13 Algorithmic

objectivity mischaracterizes justice as an optimization

problem rather than an ongoing process of democratic

deliberation about competing values. Criminal justice

is much more than predicting recidivism. It involves

balancing public safety against individual liberty, re-

habilitation against deterrence, and efficiency against

due process. An algorithm cannot resolve value con-

flicts because they represent disagreements about social

priorities that require democratic resolution.

The doctrine extends to education, healthcare, employ-

ment, and social services, consistently promising that

algorithmic decision-making will improve outcomes by

eliminating human limitations. Educational AI will per-

sonalize learning more effectively than human teachers.

Healthcare AI will diagnose diseases more accurately

than human physicians. Employment AI will identify

qualified candidates more fairly than human recruiters.

Each application follows the same faith-driven pattern:

identify limitations in current human-centered process-

es, promise that algorithmic alternatives will eliminate

them, and frame resistance to automation as opposi-

tion to progress itself—pure heresy.

Digital Manifest Destiny

The salvation narrative sometimes also incorporates

elements of Manifest Destiny thinking,14 treating tech-

nological expansion as a natural right that justifies dis-

placing existing institutions and practices. Just as nine-

teenth-century belief in Manifest Destiny portrayed

westward expansion as the inevitable expression of

Myth #3 – Heaven 107

U.S. values, Digital Manifest Destiny treats technologi-

cal adoption as a natural evolution that serves a higher

purpose, regardless of the immediate costs.

Digital expansionism appears clearly in discussions of

AI governance, where Silicon Valley leaders consistent-

ly argue that U.S. technological leadership serves glob-

al interests because U.S. values of innovation and en-

trepreneurship will guide AI development in beneficial

directions.15 Schmidt’s National Security Commission

on Artificial Intelligence exemplified this thinking by

framing AI competition with China as a struggle be-

tween democratic and authoritarian values.16 While

legitimate concerns exist about authoritarian applica-

tions of AI surveillance and control, Schmidt’s framing

suggested that U.S. technological dominance naturally

serves democratic purposes, regardless of how AI tech-

nologies are actually deployed.

The narrative suggests that concentrating AI develop-

ment in U.S. companies represents not corporate cap-

ture of public technology, but a natural expression of

democratic values that will benefit humanity universal-

ly. It conveniently obscures how U.S. companies have

sometimes pursued projects that enable authoritarian

practices when doing so aligns with political or com-

mercial interests. Google’s Project Dragonfly, which

aimed to create a censored search engine for the Chi-

nese market, and Amazon’s marketing of its facial-rec-

ognition system, Rekognition, to law enforcement,

both reveal how moral narratives can bend to expe-

diency. Similarly, Microsoft’s partnerships with Immi-

gration and Customs Enforcement (ICE) demonstrate 108 AI TOOLS, NOT GODS

how AI tools can reinforce surveillance at home, not

just abroad.

These examples underscore a long-standing tension be-

tween the rhetoric of technological salvation and the

realities of power. The U.S. has often cast its techno-

logical expansion as a civilizing or liberating force, yet

such narratives have historically served national and

corporate interests rather than universal democratic

ideals. The nineteenth-century zeal of Manifest Des-

tiny found new expression in the twenty-first-century

Silicon Valley’s proclaimed belief in progress through

innovation. But as with its predecessors, the question

remains: who is being “saved,” and at whose expense?

The Altar of AGI

These salvation doctrines all converge on a single, ul-

timate promise (though some would see it more as a

threat): the arrival of AGI. This is the promised land, the

point on the horizon where today’s narrow, task-spe-

cific systems evolve into something with human-like

cognitive abilities across the board. AGI is the machine

that can reason, plan, learn, and create with the same

breadth and flexibility as a person. It’s the stuff of sci-

ence fiction, the digital equivalent of alchemy, and it

is the central pillar supporting the salvation narrative.

However, the AGI described by its prophets does not

exist. It is a speculative future, not a present reality or

even a foreseeable one. What exists today—for all their

impressive capabilities—are highly specialized systems

that excel at specific tasks. They are excellent mimics,

not independent minds. LLMs are sophisticated par-

rots with a PhD in probability, brilliant at predicting

Myth #3 – Heaven 109

the next word in a sequence based on patterns ingest-

ed from vast datasets. They are not thinking; they are

calculating probabilities.17 As OpenAI CEO Altman

admitted in 2023, “AI tools are “very good at doing

tasks” but terrible at doing whole jobs.”18 The key is

to see the tool for what it is, not what the hype claims

it will be.

The leap from the narrow AI of today to a hypothetical

AGI isn’t a simple matter of more data or faster chips.

It represents a qualitative jump for which we currently

have no established scientific theory or clear engineer-

ing path.19 There is no known roadmap from building

a better autocomplete to building a conscious entity. To

suggest otherwise is not a technical projection; it is an

article of faith.

Hence, the recent pronouncements from tech leaders

are all the more revealing. When Mark Zuckerberg

claims that “developing superintelligence is now in

sight,”20 the assertion should be seen less as a scientif-

ic assessment and more as a strategic maneuver. Such

statements are often inflated for competitive reasons,

part of a predictable pattern where CEOs issue grand

manifestos during moments of existential business cri-

sis. It carries the same “breathless, reality-distorting

tone” that characterized his metaverse pivot, another

moonshot announced to shift a negative narrative.21

This pattern of grand, salvation-oriented pivots is not

limited to AGI or the metaverse. In November 2025,

the Chan Zuckerberg Initiative announced a new, $1

billion Biohub project with a goal that is a pure distil-

lation of the Heaven myth: to “cure, prevent or man-110 AI TOOLS, NOT GODS

age all diseases” by building AI models of cells. This

initiative, rooted in the faith that AI can “simulate the

potential impact of any drug . . .on any cell,” recasts the

fundamental human challenge of mortality as an engi-

neering problem to justify massive, speculative invest-

ment.22

In some cases, the AGI story is not just a defensive

play. For companies such as OpenAI, the AGI narrative

forms the core of their business proposition—a story

that some consider designed to inflate valuations and

frame their work as a world-historical mission rather

than mere product development.23

The obsession with a speculative future could come at

a steep, immediate price. While Silicon Valley drifts out

of touch, fixated on the holy grail of AGI,24 its primary

geopolitical competitor, China, remains ruthlessly prag-

matic. Beijing is not chasing technological ghosts; it is

focused on deploying current AI technologies across its

economy and society. Where Silicon Valley evangelizes

AGI, Beijing’s national strategy prioritizes the integra-

tion of narrow AI into specific, vertical applications—

from industrial robotics and smart city infrastructure

to financial services and autonomous logistics.25 This

creates a dangerous paradox: by worshipping a future

that is not here, the West risks falling behind in the race

that is happening right now.

AGI is convenient for its evangelists because it achieves

two critical goals. First, it justifies the enormous con-

centration of capital and resources required to pursue

it, framing investments not as speculative bets but as a

moral imperative for the future of humanity. Second, it

Myth #3 – Heaven 111

deflects attention from the real, immediate, and often

mundane harms of current AI systems.26 It is far more

exciting to debate the existential risk of a hypothetical

superintelligence than it is to grapple with the discrim-

inatory outcomes of a biased hiring algorithm or the

privacy implications of a new surveillance tool.

Oracles of Silicon Valley

Technological faith doesn’t spread on its own; it needs

prophets who do not simply make predictions but cre-

ate interpretive frameworks that make their version

of the future seem inevitable, positioning any other

path as misguided or dangerous. The salvation narra-

tive thus gains its power through charismatic leaders

who assume these prophetic roles, promising that their

particular vision of AI will deliver humanity from its

struggles. Instead of corporate executives, they profile

themselves as visionaries.

The Musk Paradox: Salvation Through Struggle

Entrepreneur Elon Musk offers a masterclass in this

model through his dual role as AI alarmist and AI de-

veloper. His warnings about AI as humanity’s “biggest

existential threat” paradoxically enhance his authority

when proposing AI solutions through companies such

as Neuralink and xAI.27 The apparent contradiction

resolves within the logic of salvation: only those who

understand AI’s true power can guide humanity safely

toward technological transcendence. Musk’s authority

operates through “productive paranoia:”28 the creation

of a fear that positions the prophet as a necessary solu-

tion to threats he simultaneously identifies and claims 112 AI TOOLS, NOT GODS

a unique ability to address.29 When Musk warns that

AGI could destroy humanity, he establishes his cred-

ibility as someone who understands existential risks

that others ignore or underestimate. When he then pro-

poses brain-computer interfaces as a means of protect-

ing against AI supremacy, his earlier warnings serve as

the foundation for his technological solutions.

His Neuralink project illustrates how this authority

transforms a speculative technology into a moral im-

perative. Musk’s endeavor to create brain-computer in-

terfaces that will enhance human cognition enough to

remain competitive with AI assumes both that such en-

hancement is technically feasible and that competition

with AI represents the primary challenge facing human

societies.30 Neither assumption has strong evidentiary

support, yet prophetic framing makes questioning them

seem either technically naive or morally irresponsible.

More subtly, the Neuralink narrative embodies tech-

no-theodicy by reframing present suffering as a nec-

essary motivation for future transcendence. Paralysis,

depression, cognitive limitation, and other neurological

conditions are no longer just medical challenges requir-

ing treatment, but evidence of a fundamental human

inadequacy that only technology can overcome. The

timeline for these enhancements remains perpetually

deferred, while current interventions require immediate

faith and financial support from investors and regula-

tors who trust in the vision, rather than the demon-

strated results.

This model creates a policy environment in which any

criticism of a specific technological approach is con-

Myth #3 – Heaven 113

flated with backward-looking opposition to human en-

hancement itself. When researchers question the safety

or feasibility of brain-computer interfaces, salvation

narratives reframe these concerns as Luddite resistance

to inevitable progress rather than legitimate scientific

skepticism about unproven technologies.

The Altman Revelation: Benevolent Artificial General Intelligence

Altman’s leadership of OpenAI represents perhaps the

most sophisticated example of technological proph-

ecy in contemporary AI discourse. His positioning of

AGI as humanity’s final invention creates apocalyptic

urgency around AI development while simultaneously

promising that proper stewardship will deliver unprec-

edented benefits.31 His company’s mission statement is

dripping with salvation rhetoric: “to ensure that arti-

ficial general intelligence benefits all of humanity.”32

This framing transforms corporate product develop-

ment into a cosmic mission that transcends ordinary

business considerations.

This duality is literally baked into its complex corpo-

rate structure, which was formalized in October 2025.33

The new arrangement divides the entity into a for-prof-

it public benefit corporation, known as the “OpenAI

Group,” and a nonprofit organization, referred to as

the “OpenAI Foundation.” This structure is strategi-

cally convenient: the Foundation, which holds a 26%

stake and appoints the board, allows the company

to maintain its appearance of pursuing an altruistic,

world-serving mission.34 Simultaneously, the for-prof-

it Group—which was necessary to secure massive in-114 AI TOOLS, NOT GODS

vestments and is 74% owned by Microsoft, employees,

and other investors—is free to pursue the commercial

returns that prioritize shareholder value.

Much like Musk, Altman’s prophetic authority oper-

ates through a carefully managed balance of promise

and peril, positioning OpenAI as uniquely qualified to

navigate toward a beneficial AGI. His warnings about

AI alignment challenges and existential risks establish

credibility as someone who takes seriously the dangers

that others might ignore. His simultaneous promises

about AGI’s potential to solve climate change, elimi-

nate poverty, and extend human lifespan create stakes

high enough to justify giving some leeway to his com-

pany’s development timeline and safety protocols.

The release of ChatGPT showcased this strategy in ac-

tion. It was presented not as an incremental improve-

ment in large language modeling, but as a significant

leap toward AGI that would transform our relationship

with creativity and information.35 The system’s impres-

sive capabilities in certain domains provided apparent

validation of these prophetic claims, deflecting atten-

tion from the limitations and risks that only became

apparent after widespread use.

More concerning is how this strategy can be used to

co-opt democratic oversight. By creating internal bod-

ies such as a “Preparedness Framework” and a “Safety

Advisory Board,” the company projects an image of

responsible self-governance and maintains corporate

control over its safety evaluation processes.36 By posi-

tioning itself as more committed to safety than external

regulators, some critics have claimed that OpenAI dis-

Myth #3 – Heaven 115

courages democratic oversight while maintaining pub-

lic trust through efforts they label “security theater.”37

The Quiet Emperor: The Scientific Prophecy of Demis Hassabis

If Musk is the prophet of doom and Altman the beguil-

ing shepherd, Demis Hassabis of Google DeepMind

offers another flavor of prophecy: the quiet emperor.

Where Musk and Altman engage in public theatrics,

Hassabis projects an aura of serene inevitability. His

vision of AGI is less a dramatic revelation and more a

steady, inexorable march of scientific progress, backed

by the near-limitless resources of the Google empire.

His persona was so compelling that it drove Musk

to cast Hassabis as a “supervillain who needed to be

stopped,”38 framing the creation of OpenAI itself as a

moral crusade against DeepMind’s perceived evil.

Hassabis’s prophetic power lies in his very quietness. By

framing the pursuit of AGI as a matter of pure scientific

destiny, he subtly positions it as a domain for experts

within powerful, well-resourced institutions, not a sub-

ject for messy public debate. And this prophecy comes

with proof. When his AlphaFold program solved the

50-year-old grand challenge of protein folding, it was

not just a corporate win; it was a Nobel Prize-winning

triumph that cemented his status as a genuine oracle.39

The message is devastatingly effective: if his methods

can solve the riddles of biology, who can argue when

he claims they will soon solve intelligence itself?

The message to policymakers is implicit but powerful:

AGI is coming, and its development is best left in the

hands of those who, like him, are already building the 116 AI TOOLS, NOT GODS

future. It is a form of authority that trades the mis-

sionary’s zeal for the emperor’s quiet confidence, yet

it serves the same function of making democratic gov-

ernance seem irrelevant in the face of history’s grand

scientific sweep.

The Anthropic Constitution: Technical Salvation Through Moral Machines

Anthropic’s position offers a more subtle message.

Through its emphasis on “AI safety” and “constitu-

tional AI”, it presents commercial development as a

moral mission.40 By framing profit-driven research as

an altruistic service to humanity’s future, Anthropic

transforms business competition into a higher purpose

that serves the greater good beyond market success.

A “constitutional AI” approach promises to embed hu-

man values directly into AI systems via training pro-

cesses that teach models to identify and avoid harmful

outputs.41 This technical approach to value alignment

appeals to an engineering mindset that prefers algorith-

mic solutions to moral problems. Nevertheless, it makes

a profound theological claim: that human values can be

formalized mathematically and implemented through

optimization processes. It treats deep-seated value con-

flicts over issues such as freedom of speech or priva-

cy as engineering problems to be solved rather than as

ongoing processes of democratic deliberation. When

different groups disagree over appropriate content pol-

icies, hate-speech boundaries, or privacy protections,

constitutional approaches seek technical solutions that

eliminate the need for continuous political negotiation.

The promise of solving value alignment reflects a belief

Myth #3 – Heaven 117

that moral questions have optimal answers, discover-

able with sufficient technical sophistication.

Anthropic’s prophetic authority operates through a

studied contrast with more aggressive AI developers

who seem less concerned with safety considerations. By

positioning themselves as the responsible alternative to

reckless AGI development, Anthropic gains regulatory

sympathy and public trust.42 Its safety-focused messag-

ing provides cover for rapid capability advancement

that might otherwise trigger more skeptical regulatory

responses.

The Academic Prophet: Yoshua Bengio and Redemptive Research

The prophetic model is not limited to the corporate

world. It extends into academic research through fig-

ures including Yoshua Bengio, whose evolution from

technical researcher to AI safety advocate illustrates

how scientific authority can transform into moral guid-

ance.43 Bengio’s warnings about existential risks from

AGI carry particular weight precisely because they

come from someone widely recognized as a founda-

tional contributor to deep learning research.

His authority operates through a narrative of apparent

reluctant wisdom as the technical expert who discov-

ers moral obligations that transcend narrow research

interests. His calls for AI moratoriums and interna-

tional governance frameworks position him as a scien-

tist-prophet who recognizes dangers that others in his

field either ignore or underestimate.44 The implication

is that deep technical expertise naturally leads to su-118 AI TOOLS, NOT GODS

perior moral insight about how technology should be

governed.

Yet this positioning conceals how his policy recommen-

dations can serve particular institutional and commer-

cial interests within the AI research ecosystem. Calls

for development moratoriums benefit researchers at

established institutions who have already achieved

significant capabilities while potentially disadvantag-

ing newer entrants who lack comparable resources. At

the same time, national and international governance

frameworks led by academic experts could institution-

alize the authority of current AI research elites. When

Bengio testifies before government committees on AI

risks and benefits, his scientific credentials lend support

to policy recommendations on social priorities and val-

ues, matters that also require public deliberation. The

academic prophet model, as a result, can create poli-

cy environments where technical expertise is conflated

with moral authority, subtly undermining democratic

governance.

How Institutions Amplify the Prophets

AI prophets do not preach in a vacuum. They are

well-funded and are amplified by powerful institu-

tions in society. Understanding technological prophecy

requires looking beyond individuals to the structures

that elevate their voices. Political leaders consistently

feature tech executives as primary witnesses in hear-

ings on AI regulation rather than shining the spotlight

on representatives of affected communities or indepen-

dent researchers without commercial interests.45 When

Altman testified before the U.S. Congress, lawmakers

Myth #3 – Heaven 119

treated him less as a corporate representative with a

vested interest than as a neutral expert whose technical

knowledge qualified him to design governance frame-

works for his own industry.46

This deference to the prophetic class is not unique

to the United States; it is a core feature of the global

“Policy Machine.” A stark example emerged in No-

vember 2025, when over 150 leading scientists and AI

researchers, including members of the UN’s AI adviso-

ry body, published an open letter calling on European

Commission President Ursula von der Leyen to retract

“unscientific and inaccurate” statements about AI.47

In a major speech, President von der Leyen repeated

industry talking points in May 2025, claiming that AI

would “approach human reasoning already next year,”

a feat previously forecast for 2050.48 A freedom of in-

formation request revealed that her source was not sci-

entific literature, but rather the “marketing statements”

of tech CEOs, including Sam Altman, Dario Amodei,

and Jensen Huang.

The scientists’ letter directly attacked this amplifica-

tion, labeling her claims “baseless AI hype” and “mar-

keting statements driven by profit-motive and ideology

rather than empirical evidence.” They warned that this

“policy-making by hype”—a perfect illustration of a

policy hallucination—distracts from “real, existing

harms” and risks “wasting public funds” while fueling

a “significant speculative bubble.” This incident is the

Heaven myth in action: a powerful political leader ab-

dicating democratic diligence to amplify the salvation

narratives of tech’s high prophets, uncritically.

120 AI TOOLS, NOT GODS

Media coverage reinforces prophetic authority by

treating technology industry predictions as newswor-

thy events, rather than corporate marketing, which re-

quires careful analysis. When tech leaders make prom-

ises about AI’s future capabilities or warn of existential

risks, journalists often report these claims as major de-

velopments, not always counterbalancing the spin with

alternative perspectives from independent experts.

Academic institutions contribute to prophetic author-

ity through research funding structures that heavily

depend on industry support, creating incentives for

researchers to align their work with corporate priori-

ties rather than developing independent analyses that

might challenge industry narratives.49 When university

AI research relies on funding from tech companies, the

resulting scholarship often supports rather than ques-

tions the salvation narratives that justify continued cor-

porate development.

The venture capital ecosystem amplifies prophetic

authority by treating successful technology entrepre-

neurs as visionaries whose insights extend beyond nar-

row business domains to encompass broader social

and political issues. When venture capitalists such as

Marc Andreessen publish essays about AI policy or

Reid Hoffman speaks about democracy and technolo-

gy, their commercial success lends credibility to claims

about public policy that have little to do with their ac-

tual expertise.50

Myth #3 – Heaven 121

The Liturgy of Infinite Progress

Faith in a technology is not sustained by belief alone.

Like any faith, it requires rituals that encompass reg-

ular, performative acts that reinforce its core tenets,

foster a sense of community, and marginalize skepti-

cal voices. These liturgical practices are where the sal-

vation narrative moves from abstract promise to lived

experience, deflecting attention from present harms by

focusing the congregation’s eyes on a glorious, algo-

rithmically redeemed future.

The Sacred Conferences

Technology conferences have become modern-day litur-

gical gatherings, where salvation narratives receive rit-

ualistic reinforcement through carefully choreographed

presentations that blend technical demonstration with

moral inspiration. TED Talks are a great example, cre-

ating a sacred space where technology leaders can deliv-

er prophetic messages about humanity’s digital future

without the inconvenience of skeptical questioning.

The format embodies key elements of liturgical design

and promotes the idea that complex social problems

have simple, tech-based solutions, suitable for neat

18-minute-long presentations. The darkened audito-

rium creates an atmosphere of reverence, the elevated

stage positions speakers as prophetic authorities, and

the prohibition on questions replaces democratic dia-

logue with one-way revelation. The iconic red dot and

carefully managed lighting reinforce the sacred charac-

ter of the space, while the global livestream transforms

a local event into a universal congregation, all partici-

pating in the same technological epiphany.51 122 AI TOOLS, NOT GODS

Other technology conferences follow similar patterns.

The Consumer Electronics Show (CES) in Las Vegas

is an annual pilgrimage where technology companies

unveil new products through presentations that asso-

ciate product marketing with enthusiastic messaging.52

Apple’s product launch events established the template

for such rituals, where new devices receive a reverential

unboxing that treats incremental improvements as rev-

olutionary breakthroughs.

Developer conferences such as Google I/O and Micro-

soft Build serve as denominational gatherings, where

tech giants promote their visions of the digital future

while reinforcing a shared, unshakable faith in techno-

logical progress.

The Ritual of Disruption

Silicon Valley culture has elevated disruption from

a business strategy to a sacred ritual. While Clayton

Christensen’s academic work on “disruptive innova-

tion” provided the intellectual framework, the tech

industry turned it into a liturgical practice and a cele-

bration of institutional destruction as a necessary step

toward digital salvation.53

The disruption ritual operates through a predictable

narrative arc: identify inefficiencies in existing institu-

tions, promise that technological alternatives will elim-

inate them, celebrate the displacement of traditional

approaches as evidence of progress, and dismiss resis-

tance as irrational attachment to obsolete systems. This

liturgical pattern transforms corporate competition

into a cosmic struggle between the future and the past.

Myth #3 – Heaven 123

Take Uber’s approach to taxi regulation. Rather than

engaging with the democratic processes that creat-

ed taxi licensing systems to address legitimate public

concerns about passenger safety, vehicle maintenance,

driver accountability, pricing, and service equity, Uber

framed its regulatory violations as a heroic act of re-

sistance against bureaucratic obstacles.54 The compa-

ny’s messaging consistently portrayed taxi regulations

as the corrupt protection of inefficient incumbents, not

as an attempt to balance competing public interests.

This narrative enabled Uber to mobilize user support

against regulatory enforcement by framing government

oversight as an attack on innovation itself.

Airbnb followed a similar liturgy, positioning short-

term rental regulations—such as fire-safety require-

ments, insurance obligations, and zoning rules—as

obstacles to sharing-economy innovation, rather than

as safeguards aimed at striking a balance between tour-

ism, safety, housing affordability, and neighborhood

character.55

The disruption ritual creates policy environments in

which existing institutions are forced to bear the burden

of proof for their continued existence, while technolog-

ical alternatives are presumed to be benevolent. Tra-

ditional taxi companies must justify their value, while

ride-sharing platforms receive some leeway based on

promises of future efficiency gains. Similarly, hotel reg-

ulations face scrutiny as obstacles to innovation, while

short-term rental platforms operate with less oversight

despite experts’ concerns about potential negative com-

munity impacts.

124 AI TOOLS, NOT GODS

Data as Sacred Truth

The technological salvation narrative treats data collec-

tion and algorithmic analysis as forms of revelation that

provide access to a truth unavailable through tradition-

al human observation or democratic deliberation. This

“data liturgy” confers sacred status on quantified mea-

surement, marginalizing qualitative knowledge that re-

sists algorithmic capture.

Google’s approach to search optimization offers a blue-

print. The company’s algorithmic ranking systems treat

user behavior metrics as if click-through rates and dwell

time are the authoritative source of truth; its ranking

systems dismiss editorial judgment or community stan-

dards as subjective bias that interferes with data-driven

optimization.56

Facebook’s engagement metrics function as a similar rit-

ual, treating user interaction data as a revelation about

human social preferences and needs.57 The platform’s

algorithmic systems optimize for engagement indica-

tors, treating other values, such as accuracy, civility,

and democratic discourse, as secondary considerations

that matter only to the extent they affect measurable

user behavior.

The data liturgy establishes a clear hierarchy of knowl-

edge that privileges quantified measurement over ex-

periential understanding, statistical correlation over

causal analysis, and algorithmic optimization over

democratic deliberation. When educational institutions

adopt AI-driven assessment systems, they typically pri-

oritize metrics that algorithms can measure and down-

grade aspects of learning that resist quantification.

Myth #3 – Heaven 125

In healthcare, the promise of “data-driven medicine”

treats quantified information as more reliable than a

physician’s clinical judgment or a patient’s own nar-

rative, despite evidence that effective medical practice

requires contextual understanding that current AI sys-

tems cannot capture.58 The liturgy extends even to crim-

inal justice, where risk-assessment tools treat statistical

prediction as more objective than judicial discretion

or community input about appropriate sentences and

supervision levels.59 These systems embody faith that

algorithmic analysis of historical data provides a bet-

ter foundation for criminal justice decisions than dem-

ocratic deliberation about punishment, rehabilitation,

and public safety.

The Sacrament of Optimization

This liturgy of optimization confers sacred status on

mathematical improvement, while treating resistance

to efficiency gains as a moral failure. Amazon’s ap-

proach to warehouse automation illustrates this sacra-

ment perfectly by treating worker productivity metrics

as objective measures of operational effectiveness.60

The algorithmic management systems monitor em-

ployee performance through detailed measurements

of movement, break time, and task completion rates,

while treating worker autonomy and job satisfaction

as secondary considerations that matter only insofar as

they affect productivity metrics.

The liturgy extends to urban planning through smart

city initiatives that promise to improve traffic flow, en-

ergy consumption, and service delivery through com-

prehensive algorithmic management.61 These systems 126 AI TOOLS, NOT GODS

treat quantifi ed improvements in measurable outcomes

as evidence of success while disregarding values such as

privacy, community autonomy, or democratic partici-

pation that resist algorithmic optimization.

In education, algorithmic personalization promises to

improve learning outcomes by tailoring instruction to

student performance data.62 Measured academic prog-

ress is seen as the primary goal, while social develop-

ment, critical thinking, or civic engagement are brushed

aside.

The optimization sacrament creates policy environ-

ments where effi ciency gains justify other costs, includ-

ing job displacement, privacy erosion, and reduced

democratic accountability. When algorithmic systems

demonstrate measurable performance gains, these im-

provements receive a sacred status that makes ques-

tioning their broader social impacts appear irrational,

if not heretical.

Thus, the Heaven myth constructs a powerful narrative

of technological salvation, complete with prophets, rit-

uals, and a promised land of algorithmic perfection. It

encourages society to place its faith in inevitable prog-

ress guided by a technocratic elite, dismissing present

harms as mere bumps on the road to utopia.

Myth #4 – Sin 127

CHAPTER 6

MYTH #4 – SIN

WHY WE BLAME MACHINES FOR HUMAN CHOICES

F aith in algorithmic salvation is brittle, for it makes promises no tool can keep. When the prophecies of Heaven fail, when the AI doesn’t cure all dis-ease, when “benevolent automation” is revealed to be

biased, and when “infi nite progress” looks a lot like

wealth concentration, that faith shatters. Betrayal and

disillusionment set in. Blame must fi nd a target.

Enter the myth of Sin, the fi ction that casts technolo-

gy as the villain rather than a mirror of human choic-

es. This chapter examines how societies create digital

scapegoats to absorb collective guilt, thereby conve-

niently absolving their own institutions of their failings.

The Seduction of a Simple Villain The Seduction of a Simple Villain

According to the Bible, in ancient rituals, a goat was

symbolically burdened with the sins of the community

and cast into the wilderness to atone for the people’s

failings.1 The ritual is alive and well, forming the fourth 128 AI TOOLS, NOT GODS

and final mythology that governs our technological age:

the myth of Sin. Just replace goats with algorithms.

Where the myth of Heaven provides a savior, the myth

of Sin provides a villain. It’s a story that casts technol-

ogy as the primary source of our modern anxieties, a

narrative of damnation to counter the promises of dig-

ital salvation. Both extremes serve the same function

of redirecting our attention from the messy, systemic

problems of our own making toward a simple, non-hu-

man culprit, thereby absolving human institutions of

responsibility.

The seductive power of the concept of technological

blame lies in its elegant simplicity. Is unemployment

hollowing out communities? It must be the job-killing

robots, not four decades of economic policies that fa-

vored capital over labor.2 Are we lonelier than ever?

Blame addictive algorithms, not the urban planning

that atomized communities, or the economic pressures

that fractured families.3 Is democracy fraying at the

seams? Scapegoat the polarizing platforms, and avoid

the difficult conversation about campaign finance and

institutional decay.4

Scapegoating follows a playbook that transforms com-

plex social histories into simple technological fables.

First, identify a deeply rooted social issue. Second, find

a recent technological development that aligns with our

growing awareness of the issue. Third, assert a causal

link, often based on little more than correlation. Fi-

nally, propose a narrow, tech-focused solution that

conveniently sidesteps any need for difficult structural

reform.

Myth #4 – Sin 129

Such logic operates on two scales. At its most spectacu-

lar, it presents an AI Overlord narrative, which portrays

AI as an existential threat to humanity. This apocalyp-

tic vision, championed by some prophets who promise

a technological heaven, serves as the ultimate trump

card. It transforms policy debates into species-level sur-

vival questions, demanding urgent action.5

More common is the Blame it on the Bots logic, which

focuses on present harms rather than future catastro-

phes. It treats algorithmic systems as primary causes

of social problems ranging from employment displace-

ment to democratic erosion, instilling moral panic that

demands swift regulatory responses without careful

consideration of alternative explanations or unintend-

ed consequences.

Both narratives are powered by technological deter-

minism, portraying technology as an autonomous force

that shapes society, rather than a collection of tools

reflecting the values, priorities, and power structures

of the institutions that build and deploy them. This

deterministic framing is politically advantageous, as

it makes technological change appear inevitable while

omitting the human choices that steer tech applications

toward particular social outcomes.

The perfect scapegoat must be powerful enough to

be blamed for our biggest problems, yet seem foreign

enough that we do not recognize our own spawn. Tech-

nology fits the role perfectly. It appears to operate with

its own arcane logic, allowing the human institutions

that profit from it to claim innocence when its applica-

tions turn harmful.

130 AI TOOLS, NOT GODS

A Litany of Sins: The “Killer” Narratives

The scapegoat rhetorical strategy transforms complex

social changes into simple, emotionally charged stories

of technological contamination that is destroying our

institutions, relationships, and capabilities. Technolo-

gy, we are told, is not just changing things: it is killing

them, delivering a fatal blow to our institutions, rela-

tionships, or capabilities.6

AI as Job Killer

“The robots are coming for your job” is a headline that

regularly pops up, introducing a story of an impending

automation apocalypse where human labor is rendered

economically obsolete. The narrative gained significant

traction with studies, including Carl Benedikt Frey and

Michael A. Osborne’s analysis of the U.S. labor market

in 2013, which suggested that 47% of jobs were at high

risk of automation within two decades.7 The study’s

methodology, however, involved asking machine learn-

ing experts to evaluate whether specific occupational

tasks could be automated using current or foreseeable

technology, then extrapolating the assessments to en-

tire job categories.

This brand of speculative forecasting often rests on

surprisingly shaky foundations. For example, the wide-

ly cited Goldman Sachs 2023 report “The Potential-

ly Large Effects of Artificial Intelligence on Economic

Growth”8 based its dramatic predictions of job dis-

placement on a simplistic rating of task difficulty. The

analysis assumed that if a task scored low enough on

its scale, it could be automated away. The problem

was that tasks such as “Check to see if baking bread is

Myth #4 – Sin 131

done” and “Complete tax forms for a small business”

were among those deemed ripe for replacement. This

methodology not only reveals a profound misunder-

standing of the contextual judgment such jobs require,9

but it also produces the headline-grabbing figures that

fuel the automation apocalypse narrative.

The political utility of this story lies in shifting our fo-

cus from decades of specific policy choices regarding

trade, corporate governance, and labor rights toward a

vague, unstoppable force of technological determinism

that renders unemployment appear inevitable rather

than the result of deliberate choices. The conversation

conveniently narrows to retraining programs and safe-

ty nets, steering clear of any uncomfortable questions

about how the economic gains from that innovation

are distributed between workers and capital owners.

Yet, evidence tells a far more nuanced story. History

shows that technological revolutions have always de-

stroyed some jobs while creating others, with net em-

ployment effects depending on institutional arrange-

ments rather than technological capabilities alone.10

The Industrial Revolution displaced agricultural em-

ployment while creating factory jobs, and the comput-

er revolution transformed clerical work, while creat-

ing information technology jobs. Current AI develop-

ment may follow similar patterns if appropriate policy

frameworks guide the transition process.

Economic research reveals that automation typically

affects specific tasks within occupations rather than

eliminating entire job categories.11 The classic exam-

ple is the ATM. Its introduction did not eliminate bank 132 AI TOOLS, NOT GODS

tellers. Instead, automating cash handling freed them

up for more complex customer service roles, and the

number of tellers increased for a time as banks opened

more branches.

This logic persists even among those building the tech-

nology. Amazon Web Services CEO Matt Garman

called the idea of using AI to replace entry-level staff

the “dumbest thing I’ve ever heard.”12 His reasoning

cuts directly against the grain of the automation nar-

rative, highlighting a simple, practical truth: if you

eliminate junior positions, you destroy the pipeline for

senior talent. How can anyone become a seasoned ex-

pert, he argues, without first doing the foundational

work that builds expertise? This perspective reframes

the discussion from one of replacement to one of evo-

lution, where AI acts as a tool that changes workflows

but cannot substitute for the human process of learning

and development.

Ultimately, the job-killer narrative ignores a fundamen-

tal truth: employment is about power, not just tech-

nology. The impact of automation on workers depends

on institutional arrangements, such as union represen-

tation and social safety nets, which determine wheth-

er productivity gains lead to higher wages and shorter

working days for workers or simply higher profits for

shareholders.13 A focus on technological displacement

is a clever misdirection, deflecting attention from the

political choices that could ensure automation ben-

efits everyone. It allows us to sidestep difficult ques-

tions, such as what if automation could lead to reduced

working hours rather than just unemployment? What

Myth #4 – Sin 133

if productivity gains were shared through profit-shar-

ing arrangements or stronger social safety nets? These

possibilities are pushed to the margins because public

discourse treats technological unemployment as an in-

evitability, not a political choice. This focus on choice

over inevitability is now being demonstrated by ma-

jor U.S. labor unions. Instead of simply accepting the

“job-killer” narrative, the AFL-CIO, representing 15

million workers, argues for a future where AI makes

work better, but only if workers have a “real voice” in

its deployment. The 2023 Hollywood strikes success-

fully secured new contract provisions governing AI,

while the Teamsters are actively opposing autonomous

trucks, not because they are against technology, but be-

cause they demand that the prosperity from productiv-

ity gains be shared with workers. Further challenging

the simplistic killer narrative, other unions, such as the

International Brotherhood of Electrical Workers, view

AI as a “historic opportunity to grow,” driven by the

massive new infrastructure demands for data centers

and energy.14

AI as Culture-Killer

Next on the terminal list is culture itself, with AI cast

as a digital Thanos, threatening to snap its fingers and

wipe out human creativity. The panic was stoked by

headlines about AI-generated art winning competitions

and by fears that algorithmic tools would make jour-

nalists, writers, and artists obsolete.15

Such a narrative is built on a romanticized ideal of cre-

ativity as a purely human, non-technological act, posi-

tioning AI as an alien intruder that corrupts authentic 134 AI TOOLS, NOT GODS

cultural production. This ideal ignores the history of

art. Creativity has always been a dance with technol-

ogy, from the invention of the printing press and oil

paints to the camera and the synthesizer, with each

new technology initially facing similar accusations of

threatening authentic human expression.16

When photography was invented, critics argued that

mechanical image reproduction would destroy painting

by eliminating the need for human artistic skill. Instead,

photography freed painters from purely representation-

al work while helping spark movements like Impres-

sionism that expanded rather than contracted creative

possibilities.17 Each new tool was met with suspicion

before it was integrated into the creative toolkit, ex-

panding what was possible. In many cases, integration

did not diminish the appeal of traditional art forms and

even increased their value.

The panic over AI-generated content follows an iden-

tical pattern by treating algorithmic tools as different

from previous artistic technologies rather than rec-

ognizing them as extensions of existing technological

mediation in creative work. When artists use AI image

generators, they typically employ these tools within

broader creative processes that involve human con-

ceptualization, curation, and refinement rather than

simply outputting algorithmic results without human

intervention.

Much of the panic stems from misunderstandings or

misrepresentations about how AI systems function.

The prevailing assumption treats AI models as digital

hoarders that store copyrighted works in their entire-

Myth #4 – Sin 135

ty, waiting to retrieve them at a prompt’s notice. AI

training is represented by some as a form of original

sin, with media stakeholders characterizing the de-

velopment of LLMs as built on a foundational copy-

right theft.18 This characterization, while unfounded in

both technical and legal analysis,19 gains amplification

through media coverage that often reflects the interests

of established entertainment industry and media stake-

holders rather than a dispassionate examination of the

underlying technology and law. The reality is that the

focus on copyright infringement during AI training

mischaracterizes both the technical process and the le-

gal landscape.20

In reality, AI systems break down information into

patterns and statistical relationships using tiny data

fragments called tokens. Imagine a creative work is an

intricate Lego model. An AI will break this intricate

construction down into millions of individual Lego

bricks and mix them with bricks from countless other

models. It then learns the statistical patterns of how

those bricks fit together to build something new, which

may not resemble any of the original models. Chatbots

are trained to be masters of plausibility, to generate

text that sounds convincing, not to reproduce a specific

source. This is why the focus on inputs can be mislead-

ing. Copyright enforcement should therefore focus on

the outputs. When a generative system produces some-

thing that infringes on a protected work, our existing

copyright laws are already equipped to handle it.21

In 2025, the U.S. judicial decisions in Bartz v. Anthrop-

ic and Kadrey v. Meta confirm that courts recognize 136 AI TOOLS, NOT GODS

training data uses as having “transformative purposes”

since developers use book contents “by statistically an-

alyzing the books’ contents to train foundation mod-

els” rather than to “educate and/or entertain” readers

like the original authors intended.22 The decisive factor

remains whether final outputs unlawfully reproduce

protected content, not the learning process itself. The

learning process resembles how scientists read hundreds

of research papers over a career, internalize patterns in

experimental results, and apply that understanding to

generate new hypotheses, albeit without the human in-

telligence the researchers have.23

The culture-killer narrative also obscures the fact that

contemporary threats to artistic livelihoods stem pri-

marily from economic arrangements rather than tech-

nological capabilities. The concentration of cultural

markets, the elimination of public-arts funding, and the

prevalence of unpaid creative labor reflect policy choic-

es that undervalue artistic work rather than inevitable

consequences of technological development.24

The traditional copyright model relies on a simple for-

mula: create work, license it, make a profit. But this

model depends on mechanisms already under threat.

Digital platforms have altered how creative work gen-

erates revenue, with streaming models often produc-

ing less money from royalties than traditional product

sales. The result is that power is concentrated at the

top, and less money trickles down to creators who

make copyrighted works.25

Independent journalism faces economic pressure not

because AI can write articles but because digital adver-

Myth #4 – Sin 137

tising markets concentrate revenue among technology

platforms while news organizations struggle with un-

sustainable business models. Musicians cannot earn a

living wage not because algorithms compose songs, but

because streaming platforms distribute revenue primar-

ily to record labels and platform owners rather than to

performing artists.26

The AI scale problem makes traditional licensing mod-

els even more inadequate for addressing AI’s impact.

For language models requiring billions of tokens, the

sheer volume means individual work values become

negligible in any licensing market. Even if licensing

markets for AI training data emerged, as judicial anal-

ysis suggests, the transaction costs and uncertain valu-

ations make such arrangements infeasible, given that

developers would need to “engage in millions of license

transactions” without clear methods for assessing how

much each book contributed to the models’ value.27

This creates compensation scenarios comparable to

current streaming models but with exponentially small-

er payments to creators.28

Chasing the technological scapegoat allows us to avoid

confronting the need for systemic economic reforms.

Solutions lie not in constraining technological tools

but in addressing systemic economic inequities through

policy reforms: establishing creator grants, appropri-

ately taxing technology developers, and redistributing

resources to creators rather than assuming traditional

remuneration systems can survive.

The policy debate is further distorted by an artificial-

ly narrow focus on entertainment industries. Policy 138 AI TOOLS, NOT GODS

discussions typically interpret “creative industries” as

shorthand for music, film, and performing arts, when

broader definitions include scientific research, software

development, and technical innovation.29 Such a nar-

row framing creates problematic dynamics in AI policy.

Where entertainment industry stakeholders frame AI as

a threat to human creativity and livelihoods, science

and research sectors view it as a powerful discovery

partner. Many research institutions see AI as enabling

breakthroughs by spotting patterns humans cannot de-

tect, automating data processing, and generating hy-

potheses at scales not feasible by manual means.

Yet the same copyright laws that govern Hollywood

also govern scientific endeavors. And while generative

AI can produce a mediocre pop song of little cultural

value, it can equally identify a novel protein folding

pattern that could revolutionize medicine. However,

copyright frameworks generally do not distinguish

between these vastly different social utilities, treating

both as equivalent creative outputs subject to identical

legal constraints.

AI as Internet Killer

Another victim in our litany of AI sins is perhaps the

most sweeping: the internet itself is already dead.

This isn’t a fringe joke; the dead internet theory holds

that the authentic, human-driven internet has already

ceased to exist, replaced by an artificial ecosystem in

which bots create much of the content for other bots.30

It transforms concerns about digital authenticity into

an existential claim that the internet, as a space for gen-

Myth #4 – Sin 139

uine human expression and connection, has been sys-

tematically eliminated by automated systems and AI.

The theory emerged from underground forums around

2021, initially suggesting that the internet died some-

time around 2016 (pre-AI emergence in the public

space), when algorithmic curation began dominating

content distribution.31 The theory asserts that content

on the internet, including on social media platforms,

is predominantly being produced by AI and bots.32 It

maintains that what users experience as the internet is a

carefully constructed illusion maintained by automated

systems that generate content, interactions, and even

engagement patterns without meaningful human par-

ticipation.

Supporting evidence for the narrative includes cyberse-

curity research indicating that nearly half of all internet

traffic in 2022 was generated by bots, alongside ob-

servable phenomena such as the proliferation of bizarre

AI-generated content that nonetheless attracts substan-

tial engagement. The infamous “Shrimp Jesus” images

circulating on Facebook exemplify this pattern, where

AI-generated crustaceans melded in various forms with

a stereotypical image of Jesus Christ receive thousands

of likes despite their obviously artificial origins and

lack of coherent context.33

The internet killer narrative operates through several

interconnected claims. First, it argues that genuine hu-

man content has been systematically suppressed by al-

gorithmic systems that prioritize engagement-optimized

artificial content over authentic human expression. Sec-

ond, it suggests that not only is the content artificially 140 AI TOOLS, NOT GODS

generated, but the apparent audience for this content

consists largely of automated systems rather than real

people, creating a vicious cycle of artificial engagement.

The theory’s more conspiratorial variants propose that

this transformation serves deliberate political purposes,

with the U.S. government engaging in an AI-powered

gaslighting of the world. This version treats the dead

internet as an intentional project of social control rath-

er than an emergent consequence of technological and

economic developments.

The technology blame game sidesteps the economic

and institutional factors that shape digital experiences.

When researchers examine the mechanics of bot prolif-

eration, they find that the systems typically serve mun-

dane commercial purposes rather than sophisticated

manipulation schemes. Social media engagement drives

advertising revenue, creating a direct financial incentive

for automated systems to generate content optimized

for platform algorithms, regardless of human authen-

ticity or meaningful communication.34 Content farms

deploy AI-generation tools to create articles, social

media posts, and engagement bait because automated

production is cheaper than hiring humans and often

more effective at gaming the algorithms that determine

visibility.35 It is not a sign of the death of the internet; it

is sadly industrial automation applied to culture.

More problematically, the internet killer narrative is a

political dead end. It channels legitimate frustrations

with platform governance into a kind of fatalism, mak-

ing reform seem futile. Platform business models that

prioritize engagement metrics over content quality cre-

Myth #4 – Sin 141

ate systematic incentives for the production of artificial

content, while concentrated market power among tech-

nology companies limits users’ choice over algorith-

mic curation systems.36 But why demand algorithmic

transparency or challenge platform monopolies if the

patient is already dead? By asserting that the system

is irrevocably broken, it ensures no one tries to fix it,

leaving the platform monopolies that profit from the

chaos firmly in place.

AI as Humanity-Killer

The ultimate technological sin is the AI that will kill

us. The humanity-killer narrative operates through two

distinct but related claims: the immediate risk of algo-

rithmic authoritarianism that eliminates human auton-

omy and free will, and the longer-term possibility of

AGI that displaces or destroys human civilization.37

The immediate apocalypse narrative centers on algo-

rithmic decision-making systems that allegedly reduce

humans to mere appendages of the machine by elimi-

nating human agency from consequential decisions re-

garding employment, criminal justice, healthcare, and

social services. It treats current AI applications as steps

toward comprehensive algorithmic control that will

eliminate meaningful human choice and democratic

governance.38 It projects current AI capabilities toward

a hypothetical AGI that could surpass human cognitive

abilities in all domains, potentially leading to human

extinction through either deliberate AI action or indi-

rect consequences of superhuman optimization pro-

cesses that ignore human values. Prominent thinkers,

including Bostrom and Eliezer Yudkowsky, have devel-142 AI TOOLS, NOT GODS

oped elaborate scenarios for how advanced AI systems

might eliminate human civilization despite being pro-

grammed with apparently benevolent objectives.39

Both versions of the humanity-killer narrative trans-

form technology policy debates into questions of spe-

cies-level survival, justifying extraordinary measures.

When AI development is framed as an existential risk,

criticism of specific regulatory proposals appears ir-

responsible or naive, rather than representing a legit-

imate disagreement about appropriate policy respons-

es.40 The apocalypse narrative facilitates the expansion

of government surveillance and corporate control by

framing invasive oversight as a protection against AI

domination, while calls for AI development moratori-

ums41 often serve the interests of existing AI leaders by

creating barriers to entry for potential competitors.

More fundamentally, the humanity-killer narrative ex-

emplifies the fallacy of treating AI systems as autono-

mous agents. When Bostrom warns about misaligned

AI pursuing goals incompatible with human survival,

he treats algorithmic optimization as a natural force,

dismissing the fact that AI systems reflect the values

and priorities embedded by human organizations in

their design, training, and deployment.42

These real-world examples reveal the “killer robot”

for what it truly is: a useful villain, a perfect scapegoat

that keeps us from examining the human decisions that

shape our economic fates. Thus, the Sin myth com-

pletes the cycle. Like the other myths, it functions by

distorting reality, encouraging us to blame the tool

rather than the hand that wields it. This consistent mis-

Myth #4 – Sin 143

diagnosis—seeing magic instead of mechanics, frenzy

instead of process, prophets instead of company ex-

ecutives, and sin instead of systems—leads directly to

governance detached from reality. And that has a sig-

nificant cost.

144 AI TOOLS, NOT GODS

Myth #4 – Sin 145

PAR T III

THE COST

OF POLICY

HALLUCINATIONS

Myths are not harmless fi ctions; they are the source

code of fl awed governance. When policies are built on

fantasy, they become “policy hallucinations”—laws

that govern shadows while real problems grow. This

part takes stock of the real-world damage: the billions

wasted on policy phantoms, the governance whiplash

from hype and panic, and the democratic abdication

as we cede control to unelected prophets and digital

scapegoats.

It’s time to examine the cost of our illusions.

146 AI TOOLS, NOT GODS

The Wreckage of Myth-Based Governance 147

CHAPTER 7

THE WRECKAGE OF

MYTH-BASED GOVERNANCE

HOW IMAGINARY PROBLEMS PRODUCE REAL DAMAGE

T he wreckage of myth-based governance manifests in three concrete forms: policy distractions that chase sci-fi fantasies, governance whiplash result-ing from panic and hype, and the democratic abdica-

tion that leaves us unprepared.

Chasing Policy Phantoms, Wasting Billions Chasing Policy Phantoms, Wasting Billions

Legislating for Merlin

The fi rst major cost is policy distraction: myths cause

us to focus on the wrong problems. The Magic myth,

by portraying AI as a human-like autonomous entity

possessing agency comparable to that of human mor-

al actors, raises complex legal and philosophical ques-

tions that actively misframe the challenges we face.1

Policymakers become fi xated on hypothetical questions

about AI rights, consciousness, and moral status while

overlooking the immediate, tangible harms created by 148 AI TOOLS, NOT GODS

current systems.2 Put more bluntly, when policymakers

start legislating for Merlin instead of Microsoft, the re-

sult is frameworks that regulate fantasy, not function.

This misdirection was exemplified in 2016, when the

European Parliament proposed considering “electron-

ic personhood” for advanced autonomous robots.3

While largely symbolic and met with skepticism from

legal scholars, such discussions stem directly from the

misperception of AI as autonomous entities capable of

bearing rights and responsibilities.4

The proposal, drafted by Member of European Parlia-

ment Mady Delvaux, suggested creating a specific legal

status for robots in the long run, so that at least the

most sophisticated autonomous robots could be estab-

lished as having the status of electronic persons respon-

sible for making good any damage they may cause.5

The framework would have required robot insurance

and established legal liability mechanisms treating AI

systems as quasi-legal entities.

The focus on theoretical personhood diverts attention

from urgent, practical concerns about algorithmic ac-

countability. While policymakers debate the hypotheti-

cal rights of non-existent sentient machines, real AI sys-

tems make consequential decisions about employment,

criminal justice, financial services, and healthcare with

limited transparency or recourse mechanisms. Algorith-

mic bias in hiring systems, opaque scoring mechanisms

in lending, and unexplainable outcomes in criminal

sentencing do not require consciousness to cause harm.

For example, algorithmic hiring systems now screen

millions of job applications annually and are known, in

The Wreckage of Myth-Based Governance 149

some instances, to exhibit racial and gender6 bias, sys-

tematically disadvantaging qualified candidates based

on protected characteristics.7

Financial services provide another compelling example.

Credit scoring algorithms make decisions that affect

millions of consumers’ access to housing, transporta-

tion, and economic opportunities. These systems often

incorporate proxy variables that effectively discriminate

against protected classes,8 yet regulatory frameworks

are only slowly shifting their attention from traditional

lending practices to the already pervasive algorithmic

decision-making processes.

This mystification also hobbles international regulato-

ry coordination. When different jurisdictions operate

under different mental and cultural models of what AI

systems are, achieving a shared understanding of what

exactly is being regulated becomes extremely difficult.

The EU’s early drafts of the AI Act, China’s focus on

controlling AI “behavior,” and U.S. congressional hear-

ings, which often focused on sci-fi scenarios, all reflect

this deep confusion. This fragmented framing prevents

coordinated responses to global challenges, as nations

struggle to agree on whether they are regulating tools,

agents, or something entirely new.

Misallocating Resources

The misallocation of attention is matched by a mas-

sive misallocation of resources, driven by the Heaven

myth’s promise of a “silver bullet.” The embrace of

techno-solutionism leads to flawed policies, wasted re-

sources, and ultimately disappointment. When technol-

ogy is sold as a panacea, policymakers pour money into 150 AI TOOLS, NOT GODS

shiny new tools while putting aside the harder, messier

work of tackling underlying social, economic, or polit-

ical causes. The result is resource displacement: shiny

tools take the stage while structural causes remain

off-script. The resulting policies often fail not simply

because the technology was oversold, but because the

problems were misunderstood as technical rather than

political and societal challenges.

This approach is especially attractive when real solu-

tions demand difficult political conversations about

resource allocation, power distribution, or systemic

reform.9 Consider how technology is often promoted

as a solution to educational inequality. Rather than

addressing disparities in school funding, teacher qual-

ity, or socioeconomic conditions that affect learning,

policymakers invest in digital devices and software

that promise to personalize learning and improve out-

comes.10

As we have seen in Chapter 3, while educational tech-

nology can certainly provide value, it cannot overcome

the inequalities that drive educational disparities. Stu-

dents in under-resourced schools may receive tablets

and learning software, but they still face challenges

such as food insecurity, inadequate facilities, and in-

experienced teachers that technology cannot address.

The focus on technological solutions thus allows poli-

cymakers to appear responsive to educational challeng-

es while avoiding more costly and contentious reforms

to educational funding and social policy.

The pattern repeats across policy domains. In health-

care, electronic health records and AI diagnostic tools

The Wreckage of Myth-Based Governance 151

promise to improve care quality and reduce costs, but

they do not address underlying issues such as provider

shortages, insurance coverage gaps, or social determi-

nants of health. In criminal justice, predictive polic-

ing and risk assessment algorithms promise to reduce

crime and improve fairness without addressing pover-

ty, inequality, or systemic racism that drive criminal

behavior.

Environmental policy also reveals this pattern. Geo-

engineering garners headlines with visions of plane-

tary-scale interventions, ranging from carbon capture

to solar radiation management and atmospheric mod-

ification, that would enable societies to continue their

current patterns of fossil fuel consumption while mit-

igating their environmental effects. Yet many of these

proposals function more as delaying tactics than solu-

tions, postponing hard choices about carbon reduction.

These tech fixes are seductive not only because they

appear bold but because they deflect blame. It is eas-

ier to believe in algorithmic answers than to confront

decades of unjust policies and institutional failure. The

focus on technological solutions also creates path de-

pendency, making it more difficult to pursue alternative

approaches when technological interventions prove in-

adequate. The displacement effect creates a self-rein-

forcing cycle, where vendors, administrators, and tech-

nical experts with vested interests in these new tools

continue to promote them, justifying their expansion

rather than questioning their effectiveness.11 152 AI TOOLS, NOT GODS

Feeding Hype, Starving Reality

This leads to another devastating cost of hype: system-

atic resource misallocation that undermines long-term

innovation and social benefit. When madness takes

hold, common sense is the first casualty, swiftly fol-

lowed by a substantial amount of money and a loss of

democratic composure. Money flows toward anything

with the right buzzwords, regardless of its actual utility

or viability.

The AI investment boom since 2023is an example

of this fever. Venture-capital funding for AI startups

reached record levels, but much of the investment

went to companies with questionable business models

or marginal technological advantages.12 Startups with

minimal revenue but AI-powered pitches secured val-

uations that would have been unimaginable just a few

years earlier. For example, in 2021, Character.AI raised

$150 million, primarily based on the popularity of its

AI chatbot platform.13 The company’s technology was

interesting, but its business model relied primarily on

subscription revenues from users who wanted to chat

with AI personalities. When the novelty wore off and

user engagement declined, the company struggled to

justify its $1 billion valuation. The eventual outcome

was a licensing deal with Google in 2024 that left in-

vestors with little to show for their enthusiasm.14

More troubling is the opportunity cost. The investment

frenzy around AI crowded out funding for less glam-

orous but potentially more vital fields. While AI com-

panies raised billions, research into renewable energy

The Wreckage of Myth-Based Governance 153

storage, sustainable agriculture, and public health tech-

nologies struggled for attention.

Anthropic’s $4 billion funding round from Amazon in

2024 illustrates the scale of this misallocation.15 While

the company’s research into AI safety was valuable, the

investment amount exceeded the total annual research

budgets of many universities and public research insti-

tutions. The concentration of resources in a single com-

pany, regardless of its merits, raises questions about

whether such investments are the best way to advance

human knowledge.

The misallocation extends to people. Tech giants com-

peted aggressively for AI researchers, offering enor-

mous salaries and equity packages that drew talent

away from academia and non-profit organizations. The

“brain drain” was especially damaging to fields that are

supposed to act as a check on corporate excess, such

as AI safety and ethics.16 This dynamic was highlight-

ed when leading pioneers from institutions, including

the University of Toronto, a key hub of AI innovation,

departed for lucrative positions at companies such as

Google and Uber, leaving academic labs struggling to

retain senior talent.17

The Great Distraction: Picking Your Apocalypse

This massive distraction and misallocation are often

justified by a “false choice” narrative, born from the

Madness myth. We are told, in essence, to pick our

apocalypse: die by a thousand biased cuts today, or be

wiped out by a rogue superintelligence tomorrow. This

is a dangerous misreading. Worrying about long-term

risks and near-term harms is not a zero-sum game.18 In 154 AI TOOLS, NOT GODS

fact, the path to catastrophe is paved with the unad-

dressed failures of the present. The seemingly “small”

problems—like algorithmic bias or AI generated misin-

formation—are not distractions from a “big” problem

like AGI; they are the training grounds for it. Every

time a company is not held accountable for a biased al-

gorithm, it normalizes the idea that automated systems

are exempt from human oversight. Every time misin-

formation is allowed to poison democratic discourse, it

weakens the institutions needed to govern a more pow-

erful technology.

Think of it like climate change. The immediate harms

of pollution, like smog in our cities and plastic in our

oceans, are not separate from the long-term threat

of a collapsing biosphere. They are symptoms of the

same systemic failure, two ends of a single, catastroph-

ic spectrum. Ignoring the smog today doesn’t provide

more resources to fight global warming tomorrow; it

just ensures the air is unbreathable long before the seas

rise. So it is with AI.

The false choice serves the architects of madness per-

fectly. For the techno-optimists, it allows them to dis-

miss present-day harms as trivial bumps on the road

to a glorious, inevitable future. For the doomsayers, it

allows them to ignore the patient work of fixing today’s

broken systems in favor of a more dramatic crusade

against a hypothetical apocalypse.

Mature governance requires us to reject this false

choice. It demands we see the connection between the

algorithm that denies someone a loan today and the

hypothetical superintelligence that could destabilize the

The Wreckage of Myth-Based Governance 155

world tomorrow. They are not two separate monsters.

They are the same beast, simply at different stages of its

life. To tame it, we must start now, by addressing the

harms we can see, not by waiting for the ones we can

only imagine.

Governance Whiplash: The Cost of Panic

The second significant cost of myth-based policy is gov-

ernance whiplash. Driven by the Madness myth, our

institutions swing wildly between the fever of hype and

the chill of panic. This irrationality is the enemy of good

governance, short-circuiting the careful and often slow

processes of democratic deliberation. When a technol-

ogy is framed as an immediate existential threat, the

pressure to “do something”—anything—overwhelms

the need to do the right thing.

This panic-driven process is highly visible in practice.

As seen in Chapter 4, the legislative rush to address

deepfakes is a good example. California’s sixty-day elec-

tion window ban and Texas’s criminalization measures

were crafted and passed without systematic hearings,

expert testimony, or empirical assessment of the actual

threat level. In Washington, D.C., congressional treat-

ment of deepfakes as equivalent to weapons of mass

destruction created an atmosphere where questioning

the response became politically toxic, no matter what

the evidence said. The same emergency logic played out

with school bans on AI. New York City’s prohibition

of ChatGPT was a snap decision, made without any

meaningful consultation with the teachers and students

it would affect. The speed of implementation—in some 156 AI TOOLS, NOT GODS

cases, within weeks of the tool’s release—prevented the

kind of thoughtful assessment that policy requires, typ-

ically.

The Global Contagion

Panic is highly contagious. In today’s interconnected

world, emotional responses in one jurisdiction quickly

spread, creating global patterns of suboptimal gover-

nance.

Political scientists call this policy diffusion through

panic, a process in which bad ideas spread across bor-

ders because the political cost of appearing unprepared

is higher than the cost of a bad decision.19

The spillover effect is particularly damaging because it

eliminates the opportunity for policy experimentation.

When every country rushes to respond to the same in-

flated promise or exaggerated threat, the world loses

the chance to learn from diverse approaches. The result

is a form of global policy madness, where international

coordination is based on shared delusions rather than

shared evidence.

The Nirvana Fallacy

This whiplash between hype and panic leads to the

Nirvana fallacy,20 also known as the perfect solu-

tion fallacy, which pushes forward an unrealistic yet

perfect-sounding solution instead of considering re-

al-world solutions. The approach, born of either uto-

pian promises or dystopian fears, demands perfection

rather than improvement.

In a hype cycle, the fallacy rests on inflated promises.

When AI companies claim to solve poverty, cure dis-

The Wreckage of Myth-Based Governance 157

ease, or eliminate bias, they set expectations that no

technology could ever meet. The inevitable disappoint-

ment leads to backlash that can kill funding and sup-

port for genuinely beneficial applications. The debate

over AI education is the classic case. By demanding that

AI tools pose no risk to academic integrity, officials

overlooked the reality that concerns about “cheating”

have accompanied every educational technology, from

calculators21 to computers.22 Students have always

found ways to cheat, with or without technology.

The pursuit of impossible standards doesn’t just stifle

innovation, it leads to rules disconnected from reality,

built around exaggerated fears and imaginary futures.

Patterns of Flawed Regulation

When moral panic grips the policy machine, the result

is not thoughtful governance but a stampede of reac-

tive, ill-conceived rules. These frameworks satisfy the

emotional demand to “do something,” but they fail to

address the root causes of problems and often create

new harms. This panic-driven response typically falls

into four predictable patterns.

#1. The Militarized Framing

When a technological challenge is framed as a nation-

al security threat, the policy response becomes milita-

rized. Complex social and economic issues are flattened

into us-versus-them conflicts, demanding decisive ac-

tion, not democratic deliberation.23 The cybersecurity

debate is an example of this militarized thinking. When

government officials invoke terms such as “cyber Pearl

Harbor,” they create a policy environment that justifies 158 AI TOOLS, NOT GODS

extraordinary measures while marginalizing democrat-

ic oversight and the protection of civil liberties.24

The U.S. Department of Homeland Security’s Cyberse-

curity and Infrastructure Security Agency, for instance,

operates under authorities that would be controversial

if applied to traditional law enforcement but become

acceptable when framed as essential protection against

foreign adversaries.25 More generally, militarized fram-

ing enables the expansion of surveillance capabilities,

the restriction of encryption technologies, and increased

government control over digital infrastructure. Histo-

ry also shows that it inevitably leads to mission creep,

where tools designed to counter foreign adversaries are

turned inward, threatening civil liberties and political

dissent.26 This war-machine approach is often counter-

productive. It sparks escalations with adversaries27 and

diverts resources from the defensive work of building

secure systems, such as user education and institutional

resilience, in favor of offensive capabilities that serve

military functions.28

#2. The Binary Prohibition

The second pattern, born of pure panic, is regulation by

prohibition. This approach is built on the deterministic

fallacy that technology is inherently evil, ignoring the

reality that its impact depends largely on how it is gov-

erned.29 It treats technology as a binary choice between

total prohibition and utter catastrophe, bypassing any

nuanced discussion of risks and benefits.30 The initial

proposal for a temporary EU ban on facial recognition

in public spaces, which was reported in January 2020,31

illustrates this thinking. The idea was quickly dropped,

The Wreckage of Myth-Based Governance 159

however,32 and the AI Act opted for a tiered approach

that bans specific harmful uses, such as real-time public

biometric surveillance, while permitting other applica-

tions under strict safeguards.33

Similarly, calls for AI development moratoriums re-

spond to existential risk concerns by proposing to halt

all advanced AI research until safety issues can be com-

pletely resolved.34 This response often backfires. When

governments ban specific technologies rather than reg-

ulating their applications, they hinder the development

of beneficial applications while doing little to deter ma-

licious actors, who simply relocate to jurisdictions with

weaker oversight.35

Prohibition also creates practical challenges. Enforc-

ing such bans requires invasive monitoring, ironically

expanding the surveillance capabilities the panic was

meant to prevent.36

#3. The Symptom Regulation

The third pattern is the technocratic targeted approach.

Here, a complex social problem attributed to an algo-

rithm is addressed with a narrow technical fix, leaving

the broader system intact. The algorithm is treated as a

discrete entity that can be regulated independently, not

as a component of larger arrangements.37

This is the logic behind relying heavily on automated

content moderation to fight misinformation and harm-

ful content, or on algorithmic audits to address dis-

crimination. It does not address the underlying sourc-

es of misinformation production, nor does it improve

media literacy education.38 Similarly, while algorithmic 160 AI TOOLS, NOT GODS

bias auditing requirements in hiring, lending, and crim-

inal justice can help identify problematic patterns, they

typically focus on algorithmic outputs rather than ad-

dressing underlying data biases or institutional discrim-

ination that algorithmic systems reflect and amplify.39

Such interventions primarily target the symptom, rath-

er than the disease. And they create collateral damage.

Automated systems can censor legitimate speech while

missing sophisticated propaganda.40 Bias audits can be

gamed by companies to create a veneer of compliance,

without addressing substantive problems. Neither ap-

proach tackles the business models that incentivize the

production of harmful content and discriminatory tar-

geting practices.41

Worse, by creating an appearance of oversight (e.g.,

“this hiring algorithm has been audited”), this ap-

proach can legitimize the underlying problem, making

it harder to challenge the injustice of the automated

system.42

#4. The Moral Crusade

The final pattern is regulation as purification. This ap-

proach sees technology as a vector of moral contam-

ination and seeks to purify the digital world through

censorship and restriction, treating online content as

inherently dangerous and citizens as perpetually vul-

nerable. Instead of empowering people with education

that enables critical media consumption, the moral cru-

sade opts for broad speech restrictions and age-gating

requirements.43

The Wreckage of Myth-Based Governance 161

Age-verification requirements for social media, for ex-

ample, treat online interaction as inherently harmful to

minors, ignoring the valuable social connections and

educational resources these platforms can provide. A

focus on restriction avoids looking into the more diffi-

cult work of addressing the underlying causes of youth

mental health challenges, such as investing in better

mental health resources, improving educational fund-

ing, or strengthening community programs.44 Such

systems raise significant concerns for the privacy and

civil liberties of all users by requiring extensive identi-

ty documentation, yet they can be easily bypassed by

tech-savvy youth.45 The result is a system that compro-

mises adult privacy while failing to protect its intend-

ed subjects, who may simply migrate to less regulated

spaces.46

The moral crusade impulse also drives attempts to elim-

inate platform liability protections such as Section 230

of the U.S. Communications Decency Act, which shields

online platforms from liability for most user-generated

content and allows for good-faith content moderation.

The argument is that making platforms legally respon-

sible for user-generated content will compel them to

remove harmful material. The more likely outcome,

however, is a surge in defensive over-censorship that

silences legitimate speech47 and perversely provides a

pretext for authoritarian censorship regimes.48

Ultimately, these flawed regulations, born of panic and

hype, are often counterproductive. Content modera-

tion requirements may increase censorship without re-

ducing misinformation, algorithmic auditing may legit-162 AI TOOLS, NOT GODS

imize discrimination while creating compliance theater,

and technology bans may prevent beneficial applica-

tions while failing to eliminate harmful uses.49

Evaded Accountability & Democratic

Abdication

The final and perhaps greatest cost of myth-based gov-

ernance is the erosion of democratic control. This hap-

pens in two distinct ways.

First, the Magic and Sin myths combine to create a

sense of evaded accountability. By mystifying AI as an

incomprehensible “black box” or scapegoating it as a

“digital villain,” we create an “accountability vacuum”

where human actors can sidestep responsibility for the

harms their systems cause.

Second, the Heaven myth creates democratic abdica-

tion. We are encouraged to defer to a priesthood of

prophets—the tech CEOs and venture capitalists—who

position themselves as visionaries qualified to steer our

future. We abdicate our collective power out of faith in

their promise of salvation.

The Accountability Vacuum: Blaming the Bots

This accountability vacuum arises when responsibili-

ty for algorithmic decisions becomes so diffuse that no

one bears clear liability for the harmful outcomes.50

The Magic myth contributes by treating AI systems

as agent-like entities. When AI is portrayed as pos-

sessing agency, it becomes easier to attribute negative

outcomes to the code rather than to the human actors

who designed, deployed, and profited from it. The Sin

The Wreckage of Myth-Based Governance 163

myth completes the trick: the algorithm becomes a dig-

ital scapegoat, absorbing blame that should be direct-

ed at human choices about training data, optimization

objectives, and deployment contexts. When AI systems

cause harm, it’s convenient to point to the algorithm or

the bot as if they acted independently. This linguistic

sleight of hand complicates efforts to hold developers,

deployers, and decision-makers accountable for fail-

ures and biases.

The case of predictive policing algorithms illustrates

this dynamic perfectly. When systems such as PredPol51

and COMPAS52 produce biased recommendations that

perpetuate racial disparities, discussions often focus on

“algorithmic bias” as if the bias emerged spontaneous-

ly from the technology. Such framing obscures the hu-

man decisions that created the outcomes: the choice to

train systems on historically biased arrest data, the de-

cision to prioritize arrest prediction over public safety,

and the determination to deploy these systems despite

their known limitations.

This diffusion of responsibility plagues other sectors.

Healthcare AI is another example of accountability dif-

fusion. When diagnostic algorithms produce incorrect

recommendations that harm patients, responsibility be-

comes distributed among algorithm developers, health-

care institutions, and individual practitioners. The

systems’ complexity makes it difficult to trace specific

decisions to specific actors, creating legal and ethical

gray zones where patients have limited recourse against

algorithmic harms. On social media, the problem ex-

tends to automated content moderation, where AI sys-164 AI TOOLS, NOT GODS

tems make millions of decisions daily about what con-

stitutes acceptable speech. When these systems make

controversial moderation decisions, platforms often

describe the choices as algorithmic rather than what

they are: policy decisions embedded in code that reflect

specific human values and commercial interests.

This is not just a legal problem; it is a moral one. A

society that deflects blame onto its tools abdicates its

ethical responsibilities. To attribute harm to AI itself is

to let its creators off the hook.

The Great Abdication: Deferring to Prophets

While the accountability vacuum allows creators to

evade blame for past harms, democratic abdication

enables them to preempt scrutiny of future ones. This

occurs when our own democratic institutions amplify

the prophets of the Heaven myth, treating corporate

leaders as neutral arbiters of the public good.

In politics, we see this when lawmakers feature tech ex-

ecutives as star witnesses in AI hearings. In the media,

this abdication occurs when journalists report indus-

try predictions as objective news, amplifying corporate

marketing without critical analysis. In academia, it’s

fueled by research funding structures that are heavily

dependent on industry support and incentivize academ-

ic attitudes that support rather than question corporate

salvation narratives.

This democratic abdication is perhaps the highest cost

of all. It prevents the development of the institutional

wisdom needed to govern, leaving us trapped in a cycle

The Wreckage of Myth-Based Governance 165

of panic and reaction, unable to manage the very tech-

nologies we are told will save us.

A Critic’s Toolkit: How to Spot the Myth Red Flags

Use these red flags and critical questions to challenge

“policy hallucinations” and identify the four myths

in headlines, product demos, and political speeches.

1. The MAGIC Myth

Red Flag: Hearing phrases like “It thinks . . .” “It

understands . . .” “It feels . . .” “It’s sentient . . .” or

“It’s a ‘black box’ that’s too complex to explain.”

The Distortion: This language is designed to hide

human accountability by making a simple tool

seem like a conscious, magical being.

The Reality Check: What is the specific function,

not the feeling? Is it “thinking,” or is it “statisti-

cally predicting the next word”? Who built it, and

what were their goals?

2. The MADNESS Myth

Red Flag (Hype): “This is an arms race . . .” “We’re

falling behind . . .” “This will revolutionize every-

thing . . .” “Trillion-dollar opportunity . . .”

Red Flag (Panic): “This is an existential risk . . .”

“It will end humanity . . .” “We must ban it imme-

diately . . .”

The Distortion: This emotional language (FOMO

or fear) short-circuits democratic deliberation and

forces a knee-jerk reaction.

166 AI TOOLS, NOT GODS

The Reality Check: What is the evidence, not

the emotion? Is this claim based on a demon-

strated capability or a speculative future?

Who benefi ts from me feeling this urgency?

3. The HEAVEN Myth 3. The HEAVEN Myth

Red Flag: “This will solve . . .” (climate change,

poverty, cancer). “This is inevitable progress . . .”

“We’re on a mission to benefi t all humanity.”

The Distortion: This positions tech leaders as

prophets and asks for our faith, encouraging us to

abdicate democratic oversight in favor of their “vi-

sion.”

The Reality Check: What human problem, value,

or trade-off is this “solution” ignoring? Who gets

to defi ne “progress”? And who isn’t in the room to

discuss the consequences?

4. The SIN Myth 4. The SIN Myth

Red Flag: “Job-killing robots . . .” “Addictive algo-

rithms . . .” “Polarizing platforms . . .” (Any phrase

that blames the tool for a human problem).

The Distortion: This creates a digital scapegoat

to absorb blame for complex, systemic problems,

letting human decision-makers and fl awed policies

off the hook.

The Reality Check: What human choice or busi-

ness model is this narrative hiding? Is the prob-

lem the “job-killing robot,” or is it a corporate

policy of not re-investing in its workforce? Is

the problem the “addictive algorithm,” or is it a

business model that profi ts from engagement?

The Wreckage of Myth-Based Governance 167

PART IV

A BLUEPRINT FOR

AI GOVERNANCE

Having traversed the cycles of myth and charted their

costs, we arrive at the antidote. If the old maps are

fl awed, we must draw a new one. This part unbuilds

what doesn’t serve us, moving beyond the graveyard

of obsolete, technology-specifi c laws. In its place, it

sets out a practical, technology-neutral blueprint for

mature governance, built on three core principles: Ac-

countability, Proportionality, and Durability.

This is how we build a framework that lasts.

168 AI TOOLS, NOT GODS

The Foundation: From Graveyard to Blueprint 169

CHAPTER 8

THE FOUNDATION: FROM

GRAVEYARD TO BLUEPRINT

CLEARING THE RUINS OF FAILED TECH REGULATION

B ef ore we can build a durable framework, we must fi rst clear the rubble. This chapter visits the “graveyard of technology-specifi c laws” to under-stand why regulating the artifact always fails. From

this wreckage, we salvage the core principle that makes

governance last: technology-neutrality. This is the cru-

cial idea that laws should regulate an activity or out-

come—like fraud or discrimination—regardless of the

specifi c tool used, and that regulators should set objec-

tives rather than prescribe or favor a particular tech-

nology. It is the foundation for the blueprint to come.

The Policy Hallucination Syndrome The Policy Hallucination Syndrome

A policy hallucination is what happens when gover-

nance is built on myths. It is a politics where laws are

written not to address the concrete functions of a tech-

nology, but to appease phantoms of our own making. 170 AI TOOLS, NOT GODS

Throughout this book, we have seen this hallucination

manifest in four distinct ways, each driven by one of

the myths we have deconstructed.

The Magic myth leads to policy distraction. We debate

the theoretical rights of electronic persons and worry

about the impact of conscious robots, while the tan-

gible harms of biased hiring and loan algorithms take

a back seat. The Madness myth produces governance

whiplash. One moment, gripped by a FOMO-driven

gold rush, governments enact competitive deregulation

in a race to the policy bottom, fueled by the same in-

vestor frenzy that led to the AI market correction of

2025.1 Next, seized by a moral panic, they impose re-

actionary prohibitions on everything from deepfakes to

AI in schools, acting on fear instead of evidence. The

Heaven myth fosters a dangerous deference to a tech-

nocratic priesthood, urging an abdication of democrat-

ic oversight in favor of prophetic promises. Finally, the

Sin myth provides us with the perfect digital scapegoat.

We blame job-killing robots for decades of flawed eco-

nomic policy and polarizing platforms for the erosion

of democratic norms, producing regulations that target

technological symptoms while the human culprits walk

free.

The result of this collective hallucination is a policy

landscape built on sand: unstable, incoherent, unable

to protect the public interests it claims to safeguard,

and fundamentally misaligned with the technology it

purports to govern.

The Foundation: From Graveyard to Blueprint 171

The Graveyard of Technology-Specific Laws

The allure of mythological thinking is its promise of

a simple, immediate answer to a complex new reality.

This impulse haunts our legislative chambers, tempting

policymakers to write laws that target the specific tech-

nology of the moment. History, however, offers a clear

and unforgiving verdict on this approach. The road

to sound policy is paved over a graveyard of obsolete,

technology-specific statutes. The pace of innovation en-

sures that today’s cutting-edge artifact is tomorrow’s

historical curiosity, leaving behind a law that is irrele-

vant and inert.

The relationship between copyright law and new tech-

nologies provides a history of this pattern.2 In the late

nineteenth century, copyright law was written for a

world of sheet music, protecting works intelligible

to the human eye. The invention of the player piano,

which reproduced music using machine-readable per-

forated rolls, threw the system into confusion. In a

landmark case of 1908, White-Smith Music Publish-

ing Co. v. Apollo Co., the U.S. Supreme Court ruled

that piano rolls did not infringe copyright because they

were not human-readable copies.3 The law, tied to the

technology of the printed page, was blind to this new

form of mechanical reproduction. Congress was forced

to intervene a year later with the Copyright Act of

1909, a technology-specific fix that explicitly extended

protection to “mechanical reproductions.”4 This cycle

of the law, breathlessly chasing innovation, has repeat-

ed itself, from radio and television to photocopiers and

VCRs.5

172 AI TOOLS, NOT GODS

The regulation of communications networks tells a

similar story. Early laws were written specifically for

the telegraph. The Mann-Elkins Act of 1910, for in-

stance, granted the Interstate Commerce Commission

oversight of telegraph rates.6 However, as the telephone

and radio emerged, the telegraph-specific statutes

proved inadequate. The legislative solution was not to

write new laws for each new device, but to create a

broader, more principles-based framework. The Com-

munications Act of 1934 wisely consolidated the regu-

lation of the telegraph into a broader regime governing

all “wire or radio communication,” a far more durable

and abstract category.7

The lesson for the AI era is unambiguous. Any law

passed today that attempts to regulate LLMs, gener-

ative AI, or foundation models by name is, in essence,

obsolete. The terminology is in constant flux, and the

technology is evolving even faster. A statute targeting

GPT-X will be as useless against the innovations of

2030 as a law for player pianos was against the advent

of digital streaming. The path to effective governance

does not lie in a frantic sprint to regulate the technolog-

ical frontier with ever more specific statutes. It begins

by rediscovering the wisdom of durable legal frame-

works that have weathered technological revolutions

before.

The Foundation: Technology-Neutrality

If the legislative landscape is littered with the tomb-

stones of obsolete laws, it also contains architectural

marvels: a class of statutes that have proven remark-

The Foundation: From Graveyard to Blueprint 173

ably durable. Their resilience stems from a shared

design principle: they regulate human behavior and

(commercial) activity, not the technological medium

through which that activity is conducted. They are

technology-neutral. By focusing on the act rather than

the artifact, they establish stable legal principles that

can adapt to unforeseen innovation.

A good example is the U.S. wire fraud statute, 18

U.S.C. § 1343.8 Enacted in an era of telegraphs and

telephones, the law makes it a federal crime to use in-

terstate “wire, radio, or television communication” to

execute a scheme to defraud.9 The statute’s power lies

in its elegant abstraction. It does not define the tech-

nology, it defines the forbidden act—fraud—and the

jurisdictional hook—the use of interstate electronic

communications. Its simple, technology-neutral con-

struction has allowed it to apply seamlessly to every

subsequent wave of communications technology, from

fax machines and pagers to email, websites, and so-

cial media. Today, it is a primary tool for prosecuting

a range of internet crimes, including phishing, busi-

ness email compromise, and online romance scams, all

without requiring a single amendment to name the new

channels.10 It endures because it governs a timeless hu-

man behavior, not a transient technology.

Perhaps the most powerful precedents for AI gover-

nance come from the foundational laws of the inter-

net era. In the United States and the EU, policymakers

grappling with the rise of the commercial internet inde-

pendently arrived at the same insight: the only durable 174 AI TOOLS, NOT GODS

way to regulate the new space was to focus on the spe-

cific, observable behaviors of the actors within it.

In the United States, the insight was codified in Section

230 of the Communications Decency Act.11 Critically,

Section 230 does not attempt to regulate “the inter-

net.” Instead, it provides a conditional liability shield

to a specific type of actor—a “provider or user of an

interactive computer service”—for a specific category

of content—”information provided by another infor-

mation content provider.”12 The law’s durability comes

from its precise, behavioral focus.13 It distinguishes be-

tween the platform’s role as a conduit for third-party

speech and its role as a “publisher or speaker” of its

own content. The distinction has allowed the law to

adapt from the era of dial-up bulletin boards and AOL

chat rooms to a landscape of blogs, social media giants,

and generative AI.

The EU’s Electronic Commerce Directive of 2000 fol-

lowed a similar logic.14 Its “safe harbor” provisions do

not grant blanket immunity to “internet companies.”

They create liability exemptions for specific, defined

activities: “mere conduit” (transmitting information),

“caching” (temporarily storing information for effi-

ciency), and “hosting” (storing user-provided informa-

tion).15 Liability under the directive hinges on a behav-

ioral test: is the platform’s role “passive” and “mere-

ly technical,” or is it “active” in a way that gives it

“knowledge of, or control over” the data?16 As a result,

it has been described as “one of the most successful

pieces of European regulation,” because its principles

The Foundation: From Graveyard to Blueprint 175

have adapted to two decades of constant technological

change.17

The convergence of thinking exhibited in these two

foundational legal frameworks is profound. Despite

stemming from different legal traditions—one rooted

in the U.S. First Amendment’s protection of free speech,

the other in the EU’s internal market principles—both

systems concluded that the key to durable governance

was to regulate actions, not artifacts. This provides a

powerful, bipartisan, and transatlantic precedent for

the governance of AI. It suggests that the path to inter-

national consensus lies not in a futile attempt to agree

on a universal definition of “AI,” but in a pragmatic

effort to identify the set of high-risk behaviors, applica-

tions, and commercial activities that require oversight,

no matter what technology enables them.

Three Imperatives for Immediate Action

#1. Reverse-Engineer Policy Language

Modern technology policy often fails because its archi-

tects are working from flawed blueprints. It relies on

metaphorical shortcuts and linguistic imprecision that

create systematic and costly implementation problems.

An analysis conducted in 2024 documented fifty-five

different analogies used for AI, each carrying distinct

policy implications that “strongly affect many key stag-

es of the world’s response to a new technology.”18 A

Cambridge meta-analysis of nearly 35,000 participants

found that, more generally, metaphorical frames are

significantly more persuasive than literal ones, with 176 AI TOOLS, NOT GODS

some conceptual metaphors having five times the im-

pact.19

The real-world consequences of this linguistic impreci-

sion are measurable, leading to costly court cases and

missed opportunities. The Center for Trustworthy Tech-

nology has shown how the “AI arms race” metaphor

drives zero-sum thinking that stifles cooperation, while

the “black box” frame obscures real opportunities for

transparency.20 The future viability of AI chatbots un-

der Section 230 may hinge entirely on whether they are

best assimilated to “search engines” or “information

content providers.”21 Copyright litigation over image

generators hinges on a similar choice of metaphor, with

courts compelled to decide whether these systems serve

as modern-day collage tools before they can make con-

sequential fair-use determinations.22

The chasm between the language of a policy and the

reality of the technology creates what computer scien-

tists call “semantic gaps,” arising from differences in

meaning between constructs formed within different

representation systems.23 It is not just a tech problem,

it is a universal failure of governance. Harvard research

has identified overoptimism as a systemic issue in ma-

jor government projects, often born from unclear goals

and imprecise language.24 The UK National Audit Of-

fice finds that vague language “frequently results in the

underestimation of the time, costs and risks to delivery

.”25 It is part of the reason why only 16% of the UN’s

Sustainable Development Goals are on track globally.26

The solution requires reverse-engineering public dis-

course language. There must be a move from intuitive,

The Foundation: From Graveyard to Blueprint 177

seductive analogies toward technically grounded, func-

tional frameworks by unpacking the metaphors.27 That

requires asking what a system actually does. Clarity of

language not only sharpens debates but also sets the

stage for enforceable and coherent policy. Without

it, laws risk codifying misunderstandings and chasing

myths that shift faster than any rules can be amended.

#2. Transcend the Analog–Digital Divide

The traditional distinctions our laws make between

analog and digital are not merely outdated; they are

now actively harmful to effective regulation. In 2019,

the U.S. Congressional Research Service identified that

“boundaries that once separated single-function tech-

nologies blend together,” making regulatory jurisdic-

tion unclear and creating situations where converged

technologies fall “unregulated, partially regulated, or

regulated under a newly developed framework.”28

The scale of obsolescence is not theoretical. Between

2021 and 2024, the U.S. Federal Communications

Commission began a kind of bureaucratic exorcism,

launching formal proceedings to “remove obsolete an-

alog TV rules” from its books. It was a quiet admission

that the legal architecture was no longer fit for pur-

pose.29

Legal frameworks designed for specific technologies fail

catastrophically when addressing convergent systems,

and lawmakers must stop anchoring rights and respon-

sibilities to obsolete formats. Studies show a chronic

gap between the emergence of a new technology and

any effective government response.30 It is the predict-178 AI TOOLS, NOT GODS

able outcome of what researchers call regulatory lag,

a systemic delay that creates “a vicious circle of inef-

ficiency, disinvestment, and reduced performance.”31

Policy, it seems, is always late to the party, arriving

long after the rules of the game have already been set

by the technology itself. This is why technology neu-

trality is key. If a rule prohibits harassment, fraud, or

surveillance, it should apply regardless of whether the

act is committed with a megaphone, a smartphone, or

a synthetic chatbot. The principle of functional equiv-

alence, which involves treating digitally and physically

mediated behaviors under the same legal standards, is

essential to future-proofing regulation.

#3. Regulate What AI Does, Not What It Is

The four myths that dominate the AI discourse trap us

in a philosophical rabbit hole. We are endlessly debat-

ing ontology—what AI is. Is it a mind? A demon? A

savior? A tool? These questions, while intriguing, are

a disastrous foundation for public policy. They lead to

the flawed governance we have documented: regulat-

ing fantasies of machine consciousness while real algo-

rithmic harms do not get the attention they deserve, or

scapegoating bots for problems rooted in human-driv-

en institutions.

The antidote to mythological thinking is a decisive shift

in regulatory focus, a turn from ontology to function.

The critical question for governance is not what AI is,

but what AI does, and what duties its creators and de-

ployers owe to the society in which their systems op-

erate. This pragmatic turn allows us to begin building

The Foundation: From Graveyard to Blueprint 179

governance frameworks grounded in tangible impacts

and shared responsibilities.

As the durable frameworks of the internet age have

already proven, technology-neutral laws that focus on

conduct consistently outperform their technology-spe-

cific cousins. History provides stark warnings against

the alternative. The UK’s infamous Red Flag Acts of the

nineteenth century, which required a person to walk

in front of early automobiles with a red flag, delayed

the country’s automotive development by decades.32 It

stands as a cautionary tale about the folly of writing

rules for the artifact instead of the action.

Functional regulation succeeds because it regulates out-

comes, not inputs. Technology neutrality principles em-

phasize outcome-based standards, non-discrimination

regardless of technology, and innovation neutrality

that avoids pushing markets toward particular techno-

logical structures.33 It is the only approach that allows

the law to keep pace with technology, creating a sta-

ble and predictable environment where innovation can

flourish responsibly.

180 AI TOOLS, NOT GODS

CHAPTER 9

PRINCIPLES FOR MATURE

AI GOVERNANCE

DESIGNING RULES FOR REALITY, NOT MYTH

H aving identifi ed the four myths and their costly misconceptions, we can now develop the antidote. The solution is not a single law but a new govern-ing philosophy, built on three core principles: Account-

ability, Proportionality, and Durability. This chapter

outlines each principle, synthesizing the antidotes to

our myths into a practical blueprint for mature gover-

nance.

Principle #1: Accountability as Antidote to Principle #1: Accountability as Antidote to

Magic and Sin Magic and Sin

The fi rst principle, accountability, is the direct anti-

dote to the myths of Magic and Sin. The Magic myth

creates an accountability vacuum by framing AI as an

unknowable “black box,” while the Sin myth uses the

algorithm as a “digital scapegoat” to absorb blame for

human failings. Accountability rejects both fi ctions. It

Principles for Mature AI Governance 181

embraces a democratic realism that holds individuals

and institutions accountable for the tools they create

and deploy.

The Mental Shift: AI Tools, Not Gods

The ritual of the scapegoat is, above all, a ritual of relief.

By casting our sins onto the algorithm, we absolve our-

selves of the messy business of accountability. But this

relief is a dangerous fi ction.1 The alternative is to reject

the technological determinism that fuels the myths and

insist on a simple truth: AI is a collection of tools built

by organizations that refl ect the values, priorities, and

power structures embedded in their design.2

Framing AI as a tool, not a being, provides a produc-

tive foundation for policy. It shifts the focus from fan-

tasy to functionality.3 It allows us to sidestep abstract

philosophical puzzles and ask straightforward, practi-

cal questions: What does this tool do? What are its lim-

itations and failure modes? Who controls it, and who

benefi ts from its use? What safeguards exist to prevent

misuse?

This “tool metaphor” is the necessary fi rst step. It im-

mediately clarifi es where responsibility lies, namely not

with the tool, but with its creators and deployers. This

mental shift requires abandoning the fairy tale of en-

chanted algorithms. As author Ted Chiang observed,

interacting with current AI systems resembles Tom

Hanks’s relationship with Wilson the Volleyball in Cast

Away: the interaction provides psychological comfort

through projection, but “there’s no one else in there.”4 182 AI TOOLS, NOT GODS

The tools metaphor also puts the AGI discussions in

perspective, as it is crucial that the philosophical puz-

zle about whether AI might eventually achieve genuine

consciousness5 does not paralyze present-day policy de-

velopment.6 The problem lies not in acknowledging un-

certainty about the future, but in projecting sentience

onto current systems that clearly lack it, thereby ob-

scuring the human choices that determine their impact.

The Legal Framework: AI as a Product

Once we see AI as a tool, the legal path becomes clear.

The most powerful principle for mature governance is

to treat AI systems as products. By viewing AI not as a

nascent consciousness but as a complex artifact sold in

a marketplace, we can apply the robust, centuries-old

body of product liability law.7 This move has a power-

ful historical precedent in the automobile. In the early

twentieth century, car accidents could have been framed

as unavoidable consequences of a mysterious new tech-

nology. Instead, the common law, through landmark

cases such as MacPherson v. Buick Motor Co.,8 evolved

to hold manufacturers accountable for the foreseeable

harm caused by their defective products.9 This princi-

ple did not stifle innovation; it channeled it, creating

market incentives for seatbelts, airbags, and anti-lock

brakes, which saved countless lives.

Product liability principles that apply to other indus-

tries should extend to systems that affect employment,

healthcare, criminal justice, and other consequential

domains.10 Applying a similar framework to AI demy-

stifies the technology and clarifies responsibility. It al-

Principles for Mature AI Governance 183

lows us to ask a series of practical, legally established

questions:

1. Was there a design defect? Was the system, even if

built to specification, designed with foreseeable risks

that a reasonable alternative could have avoided?

This could be a crucial question for systems trained

on biased data or given optimization goals that pre-

dictably lead to discriminatory or harmful outcomes.

2. Was there a manufacturing defect? Did a system de-

viate from its intended design in a way that made it

unreasonably dangerous? This could, for instance,

apply to a system corrupted during data processing

or deployment, leading to harmful outputs.11

3. Was there a failure to warn? Did the manufacturer

fail to provide adequate instructions about the prod-

uct’s limitations and foreseeable risks? For a power-

ful, general-purpose AI, this duty is immense. It re-

quires transparency about the system’s capabilities,

error rates, and the contexts in which it is unsafe to

use.

This framework is already being operationalized. The

EU’s AI Act, at its core, is a product safety regulation.

It identifies “high-risk AI systems,” many of which are

embedded in other products such as medical devices

or cars, and subjects them to the same rigorous con-

formity assessments and post-market monitoring that

the public expects for any other potentially dangerous

product.12 This approach grounds the abstract anxiet-

ies of the AI discourse in the concrete world of consum-

er protection and corporate responsibility.

184 AI TOOLS, NOT GODS

The Policy Toolkit

To move beyond reactive or oversimplified approaches

to AI regulation, we need a policy toolkit grounded in

democratic values and capable of handling complexi-

ty. That means rejecting overreach, paralysis, and false

certainty in favor of adaptive governance and demo-

cratic stewardship. It means accepting there is a need to

force fundamental changes in how companies design,

deploy, and profit from their systems.13

1. Targeted Regulation, Not Blanket Bans

Democratic governance must focus on addressing spe-

cific, demonstrable harms—not banning the underlying

tools. Technologies are tools, and any questions re-

garding their morality lie in their application and gov-

ernance frameworks.14 This means prohibiting harmful

uses, not entire technologies. For instance, laws can be

passed to ban the creation and distribution of non-con-

sensual deepfakes, without affecting the underlying

technology used in film, art, and education. Biometric

surveillance can be restricted in public spaces to pro-

tect civil liberties, while facial recognition for phone

unlocking is allowed.15 An algorithmic hiring tool may

be a functional tool for identifying certain skills, but it

can be disastrously biased for others.16 Effective gov-

ernance is about making these context-aware distinc-

tions.

Regulatory frameworks can establish conditional per-

missions that enable beneficial technological applica-

tions while preventing harmful ones. Public procure-

ment offers a powerful tool for this kind of targeted

intervention. By establishing high standards for gov-

Principles for Mature AI Governance 185

ernment technology purchases, including requirements

for auditing and fairness in AI systems, public agencies

create strong market incentives for companies to devel-

op more responsible products for everyone.17

2. Building Ethics into the Innovation Pipeline

Accountability cannot be an afterthought; it must be

an architectural feature.18 It starts with mandatory risk

assessments that require companies and government

agencies to evaluate the potential social impacts of an

AI system before deployment.19 Assessments should

cover the effects across different communities, consider

alternative approaches, and include plans for ongoing

monitoring and adjustment based on empirical evi-

dence rather than theoretical projections.20

It continues with transparency-by-design. When com-

panies must document their design choices, training

data sources, and intended applications, they create

records that enable democratic evaluation while poten-

tially influencing development decisions toward more

responsible approaches.21 Transparency must be mean-

ingful rather than just technical, providing information

in accessible formats that enable third party under-

standing and democratic evaluation.22

In the public space, it must also include participatory

mechanisms, ensuring that communities affected by a

technology have a voice in shaping how it works and

what values it embodies, rather than having companies

or government agencies design systems based on their

own assumptions about user needs and preferences.23 186 AI TOOLS, NOT GODS

Once a system is in the world, the guardrails must in-

clude ongoing oversight through independent auditing

to monitor for discrimination, inaccuracy, and unin-

tended consequences while enabling adjustments that

improve performance over time.24

Finally, we need strong professional ethics for technol-

ogists, creating individual accountability that comple-

ments institutional rules and fosters a culture of social

responsibility. Just as medical professionals follow eth-

ical guidelines that prioritize patient welfare over com-

mercial interests, technology workers could be held

to standards that emphasize social responsibility and

democratic values.25

3. Enforcing Corporate Responsibility

Accountability must have teeth. This means moving be-

yond voluntary commitments to concrete mechanisms,

looking past the code and examining the business mod-

els that create incentives for harmful applications in the

first place. When a platform’s profits depend on max-

imizing engagement, it will inevitably develop systems

that promote viral content even when it includes mis-

information or hate speech. When surveillance technol-

ogy companies profit from data collection, they have

incentives to expand their monitoring capabilities, re-

gardless of the privacy implications.26

Antitrust enforcement provides crucial tools for cor-

porate accountability by preventing technology com-

panies from accumulating market power that enables

them to avoid responsibility for their social impact.27

Internal checks on corporate power must be strength-

Principles for Mature AI Governance 187

ened by protecting workers’ rights. Whistleblowers and

organized employees who can raise ethical concerns

without fear of retaliation are one of the most effective

lines of defense against irresponsible development and

can help align corporate practices with public interests

rather than just shareholder returns.28

Finally, accountability must be global. In our intercon-

nected world, companies cannot be allowed to evade

responsibility through regulatory arbitrage. When com-

panies can move operations to countries with weaker

oversight, they can escape accountability while con-

tinuing to operate in jurisdictions that demand higher

standards. Coordinated international frameworks can

establish minimum standards for corporate responsi-

bility while allowing democratic variation in imple-

mentation approaches.29

4. Recognizing the Limits of the AI as a Tool Analogy

However, the tool metaphor does have limitations that

must be acknowledged. Modern AI systems, particu-

larly LLMs and multimodal systems, exhibit emergent

behaviors that their creators didn’t explicitly program

and sometimes don’t fully understand. These systems

can generate novel outputs, combine concepts in unex-

pected ways, and exhibit capabilities that exceed their

training objectives. While such emergent properties

don’t constitute consciousness or agency, they do rep-

resent a form of complexity that challenges simple tool

metaphors.

Furthermore, AI systems increasingly operate in con-

texts where they shape human behavior and social 188 AI TOOLS, NOT GODS

dynamics in ways that traditional tools do not. Rec-

ommendation algorithms shape the content billions of

people consume, potentially influencing political opin-

ions, consumer choices, and social relationships. Chat-

bots increasingly shape how people access information

and understand the world. The systems function more

like social infrastructure than individual tools, requir-

ing governance approaches that account for their sys-

temic effects.

Principle #2: Proportionality as Antidote to

Madness

The second principle for mature governance is pro-

portionality, which reframes policy intervention from

a crisis response to tackling a public health challenge.

This approach is the direct antidote to the emotion-

al extremes of the Madness myth, which pushes pol-

icy between the fever of hype cycles and the chill of

moral panics. A public-health approach, by contrast, is

rooted in the sober, steady work of evidence-based risk

assessment, targeted interventions, and a long-term fo-

cus on building societal resilience. Instead of the all-or-

nothing logic of madness, it embraces proportionality,

recognizing that not all AI systems pose the same level

of risk, and that regulatory attention should be focused

where the potential for harm is most significant.

The Mental Shift: Evidence Over Emotion

Moving beyond techno-solutionism means insisting

on rigorous evidence before, during, and after deploy-

ing AI. It means treating technological solutions with

the same skepticism applied to other high-stake poli-

Principles for Mature AI Governance 189

cy interventions. If we are serious about abandoning

mythological thinking, we need better tools when ask-

ing questions such as “Does this work?” “For whom?”

“And at what cost?” Such an evidence-based approach

helps us distinguish genuine breakthroughs from over-

hyped solutions.

The medical field provides a useful model. We do not

deploy a new drug to millions based on a marketing

pitch. New medical technologies must demonstrate

safety and efficacy through controlled trials before re-

ceiving regulatory approval.30 In the U.S., medical AI

systems that receive approval from the Food and Drug

Administration (FDA) must demonstrate effectiveness

through controlled trials, leading to genuinely useful

applications such as diabetic retinopathy screening and

certain forms of medical imaging analysis.31 However,

many healthcare AI applications are deployed without

rigorous evaluation, particularly in areas like clinical

decision support and administrative automation.32

Patients deserve better. For example, a Google AI sys-

tem for retinal disease detection performed accurately

during development but exhibited poor performance

after deployment in Thai hospitals due to differences in

image quality between the training and practice data.33

Similar standards must apply to AI systems deployed in

other critical domains.

IBM Watson Health’s ultimate dissolution after bil-

lions in investment demonstrates how promises of sal-

vation can consume enormous resources while deliv-

ering minimal clinical benefit.34 The project failed not

due to a lack of technology, but rather from unrealistic 190 AI TOOLS, NOT GODS

promises that overlooked the complexities of medical

decision-making. In contrast, DeepMind’s AlphaFold

was a genuine breakthrough that solved a 50-year-old

challenge in protein folding, opening new frontiers in

medicine.35 It was technically sophisticated and prac-

tically valuable. This is a world away from most viral

chatbots, whose technical sophistication is impressive

but whose social value is often unclear. In fact, most

AI chatbots solve problems that are already adequate-

ly addressed by existing technologies, such as search

engines, customer service systems, and educational re-

sources; however, they also create problems related to

misinformation, privacy, and dependency.

Sometimes, the result can be mixed. When the

COVID-19 pandemic upended livelihoods in Togo, the

government launched the Novissi program. It set a spe-

cific goal to use satellite data and machine learning to

improve the targeting of emergency cash assistance.36

The system achieved a meaningful 17-point increase in

accuracy, while also being honest about its limitations,

including a 30% error rate in excluding truly poor in-

dividuals who did not match the algorithmic profile.37

This transparency enabled productive discussions

about how AI targeting could supplement, rather than

replace, traditional poverty assessment methods.

As a rule of thumb, the goal of smart policy should not

be to eliminate enthusiasm for technological innova-

tion, but to channel it toward applications that demon-

strably improve human welfare. It means fostering an

environment where the next AlphaFold is celebrated as

a greater achievement than the next viral chatbot.

Principles for Mature AI Governance 191

The Legal Framework: Adaptive and Risk-Proportional Governance

If moral panic is a disease of governance, then a sense

of proportion and timing is the cure. For any govern-

ment faced with a new technology, the hardest ques-

tion is “when to act?” Acting too soon on panic risks

stifling innovation; acting too late risks allowing harm

to take root.

The legislative rush to address deepfakes is a perfect les-

son. Initial panic-driven laws in the U.S. focused on the

hypothetical threat of video fakes in elections. When

the real threat emerged years later as targeted audio

manipulations, those early regulations were a poor fit.

The solution is not inaction, but “adaptive vigilance”:

designing laws that can evolve as our understanding of

a technology grows. This requires iterative processes,

for which we have decades of blueprints. We only need

to be wise enough to borrow them. The introduction

of automobiles, aviation,38 pharmaceuticals,39 and tele-

communications40 all involved periods of uncertainty

and regulatory experimentation before the right bal-

ance was found. Each transition offers valuable lessons

about striking a balance between innovation and public

safety, mitigating unintended consequences, and adapt-

ing governance to technological advancements.

For example, the pharmaceutical industry’s phased ap-

proach to drug development is a proven model, born

from past tragedies and designed to prevent future ones.

Phase I trials test basic safety, Phase II evaluates effica-

cy, and Phase III conducts large-scale comparisons, all

before a drug is approved for public use.41 In some in-192 AI TOOLS, NOT GODS

stances, AI deployment could easily follow similar stag-

es: limited testing in controlled environments, evalua-

tion of harms and benefits, and systematic comparison

against existing methods before any wide release.

Automotive safety testing offers another relevant frame-

work. Regulators do not just hope that cars are safe but

require manufacturers to prove it through standardized

tests. In the United States, the National Highway Traf-

fic Safety Administration’s Federal Motor Vehicle Safe-

ty Standards42 require manufacturers to demonstrate

compliance through standardized tests before vehicles

can be sold. The standards are not static but evolve

over time, based on real-world accident data and new

technological capabilities, providing a model for creat-

ing adaptive safety requirements for AI.

Canada’s Algorithmic Impact Assessment43 require-

ment offers another model for regulatory approaches

that resist hype.44 It forces government agencies to eval-

uate a system’s potential effects on accuracy, autono-

my, procedural fairness, and human dignity before they

launch, rather than simply measuring efficiency gains

or cost savings.45

Regulatory sandboxes offer an alternative avenue for

resisting salvation logic by enabling controlled exper-

imentation under democratic oversight. The UK Fi-

nancial Conduct Authority’s sandbox allows fintech

companies to test new products with real customers

while maintaining consumer protections and, crucially,

generating systematic evidence about their benefits and

risks.46 This model has been formally adopted by the

EU AI Act,47 whose Article 57 mandates the creation of

Principles for Mature AI Governance 193

AI regulatory sandboxes in member states. These are

defined as controlled environments where innovative

AI systems can be developed, tested, and validated—

sometimes in real-world conditions—for a limited time

under regulatory supervision before being placed on

the market. Educational AI systems could be piloted

in selected schools with a comprehensive assessment of

effects on student learning across different populations.

Healthcare AI could be tested in controlled clinical set-

tings with rigorous evaluation of diagnostic accuracy

and patient outcomes. The key is to ensure the sand-

boxes serve democratic learning, not just corporate

development, with public reporting of findings and a

commitment to adjusting the rules based on evidence,

not overoptimistic promises by vested interests.

What all these models share is a commitment to itera-

tion and evidence. They feature reviews, pilot studies,

and a willingness to adapt—a far cry from the reactive,

all-or-nothing prohibitions born of panic.

A focus on impact also requires a critical shift in what

we choose to measure. Much of the current regulatory

discussion, particularly in high-level international fo-

rums, has been captivated by the idea of using compu-

tational power, or “compute,” as a primary proxy for

risk. The logic is simple: the more computing power a

model requires, the more capable—and therefore po-

tentially more dangerous—it must be. This is the ap-

proach taken by the Responsible AI Safety and Edu-

cation Act (RAISE) in New York,48 Senate Bill 53 in

California,49 and the EU’s AI Act.

194 AI TOOLS, NOT GODS

However, this is another seductive but flawed simplifi-

cation. Risk is a product of context, not just capabil-

ity. A small, computationally inexpensive model used

to screen asylum applications or recommend medical

treatments can cause immense harm, while a massive,

high-compute model used to generate poetry is relative-

ly benign. Focusing on compute as the key regulatory

threshold is like judging a vehicle’s danger solely on its

horsepower, ignoring whether it’s being driven at 10

mph in a school zone or 90 mph on a wet road by

an inexperienced teenager.50 Effective regulation under-

stands that the context of deployment—the who, what,

and where of its use—is a far more reliable indicator

of risk than any technical specification. This approach

is also more durable, as it is not rendered obsolete by

inevitable advances in algorithmic efficiency that allow

for more capable models to be run with less and less

computational power.

The Policy Toolkit

Finally, the principle of proportionality is not just

about reacting correctly; it is about building a society

that is proactively resilient to the storms of hype and

panic. In this context, resilience is the direct antidote to

the “Madness” myth. It is a society’s structural capaci-

ty to absorb the shock of technological change without

breaking—that is, without succumbing to “governance

whiplash” or “policy FOMO”. A brittle system reacts

with knee-jerk bans or reckless deregulation; a resilient

system responds with a steady, evidence-based process.

This is achieved by intentionally creating “buffers,” or

institutional shock absorbers, at every level. We must

Principles for Mature AI Governance 195

build resilience into our governing bodies, markets, in-

ternational relations, and public sphere.

1. Institutional Resilience

Resilience requires independent, non-partisan bodies to

provide clear-eyed analysis.

The United States had a model for this in its Congres-

sional Office of Technology Assessment (OTA). From

1972 to 1995, it provided balanced analysis that re-

sisted both industry hype and activist alarmism.51 The

OTA covered a variety of emerging technologies, from

genetic engineering to computer security, helping legis-

lators understand complex issues without relying solely

on the input of interested parties.52 While the office was

ultimately defunded due to political pressures, its work

demonstrated how independent technical expertise can

support democratic decision-making about complex

technologies.

The U.S. Congressional Budget Office (CBO) offers

another model for independent analysis that provides

factual foundations for policy debates.53 CBO’s eco-

nomic projections and policy analysis, while not al-

ways accurate, create common ground for legislative

discussions by establishing shared factual baselines. A

modern equivalent, staffed with independent experts,

could provide a trusted diagnosis of AI risks, free from

the distortions of hype.

2. Market Resilience

Resilience implies aligning market incentives with pub-

lic values. This means designing systems that reward

companies for responsible behavior.

196 AI TOOLS, NOT GODS

The FDA’s “breakthrough device” designation provides

a model for incentivizing transparency and responsible

innovation.54 Companies that demonstrate their devic-

es address unmet medical needs and provide evidence

of potential benefits receive expedited review and on-

going communication with FDA staff.

Environmental disclosure requirements offer anoth-

er precedent for transparency incentives. The United

States Environmental Protection Agency’s Toxic Re-

lease Inventory requires companies to publicly report

releases of toxic chemicals, creating market pressures

for pollution reduction without mandating specific

technologies.55

Financial services also demonstrate how transparency

requirements can drive industry-wide improvements.

The Basel III capital requirements require banks to pub-

licly disclose their risk management practices, creating

market incentives for better governance while enabling

regulatory oversight.56

Similarly, for AI, transparency should be a competitive

advantage, not a regulatory burden. Regulators can of-

fer faster approvals for firms that open their models

to rigorous testing and monitoring, while subjecting

opaque players to heightened scrutiny. AI disclosure

requirements could create competitive advantages for

companies with better algorithmic governance practic-

es while exposing those with inadequate practices.

3. Global Resilience

We must move beyond the “arms race” mentality (a key

part of the Madness myth) and embrace international

Principles for Mature AI Governance 197

cooperation. Challenges such as AI safety, ethics, and

economic disruption require coordinated responses.

The Montreal Protocol is an inspiring example of suc-

cessful international technology cooperation.57 When

scientists identified the threat of ozone depletion from

chlorofluorocarbons (CFCs), countries didn’t compete

to develop the most effective anti-ozone-destroying

chemicals. Instead, they worked together to phase out

CFCs and develop alternatives. The result was a combi-

nation of environmental protection and technological

innovation that benefited all participants.

International cooperation is also essential in AI gov-

ernance, especially in safety research, technical stan-

dards, and the management of economic disruption.

The Global Partnership on AI represents early efforts

to facilitate collaboration, though more structured ap-

proaches may be needed as AI technologies become

increasingly pervasive.58 Learning from successful in-

ternational frameworks, such as aviation safety coordi-

nation and pharmaceutical regulatory harmonization,

could inform more effective global approaches to AI

governance and promote shared standards.

Yet even as the benefits of international cooperation

become apparent, a counter-narrative has emerged that

threatens to undermine collaborative approaches: the

rhetoric of digital sovereignty. The EU has been partic-

ularly vocal in promoting this concept, framing techno-

logical independence as essential for political autonomy

and economic security.59 The move is a response to a

legitimate fear of becoming a digital colony, dependent 198 AI TOOLS, NOT GODS

on U.S. and Chinese platforms for critical infrastruc-

ture and data processing.60

The digital sovereignty agenda reflects legitimate con-

cerns about technological dependence, but the rheto-

ric often slips into zero-sum thinking that mirrors the

arms-race mentality it claims to reject.61 When Ursula

von der Leyen, President of the European Commission,

pledged in 2014 that Europe must achieve “techno-

logical sovereignty” in critical areas such as AI,62 she

framed international cooperation as a luxury that Eu-

rope can ill afford rather than a necessity for addressing

global challenges.

Such rhetoric creates its own competitive dynamics. If

Europe seeks technological autonomy, other regions

inevitably respond with their own sovereignty initia-

tives, fragmenting global technology ecosystems in

ways that benefit no one. The result risks delivering not

genuine independence but an expensive duplication of

effort and a reduced collective capacity to address glob-

al challenges.

4. Public Resilience

Public resilience requires a commitment to critical

thinking education, empowering citizens to evaluate

information independently, understand how techno-

logical systems function, and participate in democratic

processes that shape the governance of technology.63

The most effective long-term strategy implies a sus-

tained, society-wide investment in robust digital and AI

literacy, enabling society to navigate a complex world

with wisdom and agency.64 It means investing in insti-

Principles for Mature AI Governance 199

tutional strength, ensuring schools, healthcare systems,

and community groups are well-funded and account-

able, so they can adapt to technological developments

while maintaining their core purposes.65

It also depends on fostering diverse media ecosystems

by supporting public media and local journalism. Such

diversity contributes to resilience by ensuring that com-

munities have access to multiple information sources

rather than relying on a single platform or algorithm

for news and communication.66 More importantly, it

provides an alternative to algorithmically optimized

outrage and empowers citizens to distinguish between

legitimate excitement and manipulative hype, building

a collective immunity to madness.

Finally, resilience is sustained by developing the social

capital and democratic participation infrastructure

that allows communities to organize effectively around

shared interests rather than remaining isolated and

vulnerable to technological manipulation. It, in turn,

enables communities to participate in finding adequate

solutions to their problems, rather than waiting for a

technological savior or fearing a digital demon.67

200 AI TOOLS, NOT GODS

Table 1—The Guardrails Framework

1. CORE PRINCIPLE

Categorize Systems by Demonstrated Impact, Not Hypothetical Fantasy

RATIONALE & CORRECTIVE FUNCTION Illustration: EU AI Act model that differentiates the obligations of, for example, high-risk medical devices vs. low-risk entertainment recommendations.68

Advantage: Forces policymakers to address verifiable, real-world harms, not sci-fi scenarios. Focuses oversight where it is most needed.

2. CORE PRINCIPLE

Require Progressively Rigorous Testing Based on Potential Harm

RATIONALE & CORRECTIVE FUNCTION Illustration: The FDA’s approval process for medical devices, which requires progressively rigorous testing based on the severity of potential patient harm.69

Advantage: Acts as a crucial brake on reckless hype cycles, providing a rational alternative to moral panics.

3. CORE PRINCIPLE

Mandate Systematic Hazard Identification & Human Accountability

RATIONALE & CORRECTIVE FUNCTION Illustration: The FAA’s Safety Management System in the aviation sector, which requires airlines to systematically identify hazards, assess risks, and implement controls proportional to an accident’s potential severity and likelihood.70

Advantage: Restores clear lines of responsibility, making it impossible to blame the algorithm for failures.

Principles for Mature AI Governance 201

4. CORE PRINCIPLE

Incentivize Safety by Making Risk a Material Cost

RATIONALE & CORRECTIVE FUNCTION Illustration: Financial regulation models, such as the Basel framework, require banks to hold capital proportional to their assessed risks.

Advantage: Replaces the need for blind faith in corporate benevolence with verifiable, economic incentives, forcing companies to internalize the costs of the risks their systems create.

Principle#3: Durability as Antidote to Heaven

The final principle for mature governance is durability.

This approach conceives of AI regulation not as a se-

ries of reactive, technology-specific laws, but as a set of

foundational principles. It is the direct antidote to the

opposing temptations of the Heaven myth, which urg-

es a dangerous deference to tech prophets, and the Sin

myth, which demands reactionary prohibitions.

The Mental Shift: From Reactive Laws to Durable Principles

Instead of writing piecemeal legislation for each new

technological marvel or abdicating governance entirely,

this framework establishes a stable and future-proof set

of high-level principles to guide all algorithmic systems.

Durability requires a shift in our long-term thinking.

While the tool metaphor is useful for accountability,

the infrastructure analogy is better for understanding

AI’s systemic influence. Like electricity or transporta-

tion networks, AI is becoming a foundational layer of

society that requires robust governance frameworks, 202 AI TOOLS, NOT GODS

clear technical standards, equitable access provisions,

and comprehensive safeguards against abuse.71 This

framing forces us to consider public-interest principles

such as universal access, reliability, and interoperabil-

ity.

Additionally, the infrastructure approach alters our

perspective on the concentration of the AI market.

Rather than treating AI capabilities as commercial

products subject only to market forces, infrastructure

thinking acknowledges that access to AI tools is in-

creasingly influencing opportunities for education, em-

ployment, and civic participation. Just as telecommuni-

cations and transportation infrastructure are subject to

common carrier regulations and antitrust oversight, AI

infrastructure might require similar governance mech-

anisms to prevent monopolization and ensure compet-

itive markets.

Resilience and reliability become increasingly crucial as

AI systems become more deeply integrated into critical

social systems. Infrastructure frameworks emphasize

the need for redundancy, fail-safe mechanisms, and di-

saster recovery planning. Such concepts translate nat-

urally to AI governance through requirements for al-

gorithmic auditing, backup systems, and transparency

mechanisms that enable oversight and accountability.

Finally, a durable framework recognizes that techni-

cal fixes alone are insufficient. Many issues framed as

“AI problems” are actually systemic, human problems

amplified by new tools. A holistic approach requires

embracing “complexity thinking”: seeing social chal-

lenges not as bugs to be fixed but as outcomes of tan-

Principles for Mature AI Governance 203

gled systems, histories, and power dynamics.72 Pover-

ty, for instance, is not a data distribution issue; it is a

convergence of policy, labor, education, and structur-

al injustice. Climate change is not just a technologi-

cal problem; it requires coordinated changes in energy

systems, transportation, and international cooperation.

Technology can play a supporting role, but it cannot

substitute for political will or public buy-in.

Complexity thinking looks beyond the algorithm to

the broader social, economic, and political systems.

Achieving this requires cross-sector collaboration,

bringing together technologists, social scientists, edu-

cators, and community organizations to develop com-

prehensive strategies.73

• When we blame automation for unemployment, we

must also look at economic policy, job training, and

stronger safety nets.74

• When we blame platforms for loneliness, we must

also support community development, public spac-

es, civic organizations, and programs that foster the

face-to-face connections that no technology can re-

place.

• When we blame policy failures on inscrutable algo-

rithms, we must invest in the cross-disciplinary re-

search needed to build a comprehensive picture of

their impacts, moving beyond the isolated perspec-

tives of computer science or social science.

The Legal Framework: Principles-Based Regulation

The model for durability draws inspiration from the

success of Principles-Based Regulation (PBR) in other

complex, fast-moving domains. In fields such as finance 204 AI TOOLS, NOT GODS

and environmental protection, experience reveals that

attempting to write thousands of prescriptive rules is

not a good idea, as they are likely to become obsolete

quickly.75 PBR, by contrast, sets broad, outcome-fo-

cused standards. For example, the UK FCA imposes

a duty on firms to “act to deliver good outcomes for

retail customers.”76 This approach endures in the face

of innovation, forcing firms to engage with the spirit of

the law and reducing the potential for creative compli-

ance.

The Uniform Commercial Code (UCC) in the United

States stands as a testament to this thinking. For over

seventy years, the UCC has provided a stable and uni-

form legal foundation for commercial transactions,

successfully adapting to the shift from an industrial

economy to the era of e-commerce.77 It achieves this

durability not through detailed technological specifica-

tions but through clear, high-level principles governing

contracts, sales, and secured transactions, successful-

ly adapting from an industrial economy to the age of

cryptocurrency.

An AI PBR would similarly establish a set of founda-

tional, technology-neutral tenets:

• Human agency and oversight: A default requirement

for meaningful human control over consequential

decisions.

• Accountability and responsibility: A clear allocation

of liability to the human actors who design, deploy,

and profit from AI systems.

Principles for Mature AI Governance 205

• Transparency and explainability: A right for individ-

uals to understand the basis of algorithmic decisions

that affect them.

• Fairness and non-discrimination: A prohibition on

algorithmic systems that produce or perpetuate un-

lawful discrimination.

• Safety and reliability: A duty to ensure that AI sys-

tems operate safely and reliably in their intended

contexts.

The Policy Toolkit: Reclaiming Democratic Control

After detailing the pervasive capture of our democratic

institutions, is it not naive to propose democratic real-

ism as the solution? This is not a contradiction; it is the

core of the challenge. The fact that our institutions are

under strain is not a reason to abandon them for tech-

nocratic prophets. On the contrary, it is the reason we

must work to reclaim and strengthen them.

1. Tools for Democratic Participation

We already have powerful models for structured public

engagement. As noted in Chapter 4, Ireland’s Citizens’

Assembly demonstrates that a representative group of

everyday people, given time and access to independent

expertise, can produce nuanced policy recommenda-

tions on deeply divisive issues such as abortion and cli-

mate change.78 This same approach can be applied to

AI governance, replacing the polarized debate of salva-

tion versus damnation with evidence-based discussion.

Imagine citizen assemblies systematically tackling the

questions that the salvation narrative ignores. Should

public schools use AI tutoring systems, and under what

conditions? How should healthcare AI be designed to 206 AI TOOLS, NOT GODS

preserve patient privacy? What role should algorithms

play in the justice system, and what human oversight is

essential? These are precisely the kinds of messy, val-

ue-laden debates that the salvation narrative seeks to

override but that democracy is designed to navigate.

The Danish Board of Technology’s consensus confer-

ences have equally provided a model for public engage-

ment on technological issues.79 These conferences bring

together diverse citizens to examine emerging technol-

ogies, hear from independent experts, and develop rec-

ommendations that have successfully influenced demo-

cratic policy.80

We even have examples of how digital tools can sup-

port this process, such as the vTaiwan platform men-

tioned in Chapter 3, which has enabled large-scale pub-

lic consultation on complex issues such as ride-sharing

and cryptocurrency regulation, using technology to en-

hance, not replace, democratic deliberation.81

2. Tools for Transparency and Agency

For democratic participation to be meaningful, it must

be informed. It requires tools that empower citizens

and embed human agency into the design of technol-

ogy itself.

First, transparency must be designed for public un-

derstanding, not just for bureaucratic box-checking.

Public registries of automated decision-making sys-

tems, such as those in Amsterdam82 and Helsinki, are

a crucial step.83 They require government agencies to

disclose how and when they use algorithmic systems

in clear, accessible language, not technical jargon. The

Principles for Mature AI Governance 207

Amsterdam registry details each system’s purpose, de-

cision-making logic, data sources, accuracy metrics,

and human oversight procedures, allowing citizens to

understand and evaluate the automated decisions that

affect their lives and communities. The Helsinki regis-

try goes a step further by requiring public consultation

before deploying AI systems that could significantly af-

fect citizen rights.84 This consultation process creates

opportunities for community input from the outset on

whether algorithmic applications serve public purpos-

es, while also enabling ongoing oversight of system per-

formance and social impacts.

Second, transparency frameworks must also create a

legal right to an explanation for citizens affected by au-

tomated decisions. France’s algorithmic transparency

law, for instance, allows citizens affected by automat-

ed government decisions about benefits, taxes, or other

government services to demand an accessible explana-

tion of how those decisions were reached, establishing

a framework for accountability that goes beyond mere

technical disclosure.85 Corporate transparency require-

ments could follow similar principles by requiring com-

panies to disclose the impacts of AI systems on workers,

consumers, and communities in a meaningful manner.

The EU’s AI Act includes transparency requirements

for certain AI applications, but these could be expand-

ed to cover broader social and economic impacts that

affect public welfare.86

Third, beyond transparency, we need community-con-

trolled technology. Barcelona’s Decidim offers an ex-

ample of participatory democracy software designed 208 AI TOOLS, NOT GODS

by and for civic organizations, rather than by a tech

company optimizing for engagement.87 The platform

enables neighborhood assemblies, municipal councils,

and civic organizations to organize democratic deliber-

ation. Its governance is democratic, and its success in

supporting participatory budgeting and neighborhood

planning demonstrates how technology can serve pub-

lic purposes. Cooperative ownership models, such as

platform cooperatives, offer a similar path, enabling

communities to collectively own and govern the sys-

tems that impact their lives. Platform cooperatives

such as Stocksy United and Resonate demonstrate how

worker and user ownership can align technological de-

velopment with community interests, moving beyond a

singular focus on profit maximization.88 Public banking

provides a related model for funding such initiatives,

allowing community-controlled financial institutions

to support local, democratic technology development,

free from the commercial pressures that fuel the salva-

tion myth.89

Fourth, we must build widespread technological lit-

eracy. This means rejecting the narrative of helpless-

ness. Reclaiming agency begins with education that

frames AI as a human creation that can be shaped

through democratic engagement. Finland, for example,

launched a program in 2020 to teach at least 1% of its

citizens—and, by extension, civil servants—the basics

of AI.90 The campaign quickly exceeded its targets and

offered a model for broader digital education.91 The

idea is simple: if lawmakers, judges, city council mem-

bers, and voters understand what AI can and cannot

Principles for Mature AI Governance 209

do, they are better equipped to ask tough questions.92

Similarly, when workers can see the logic behind al-

gorithmic management, they are better equipped to

organize for fair treatment and oversight.93 This liter-

acy must extend to computer science education, which

should include the analysis of algorithmic bias and eth-

ical design, not just technical implementation.94

Finally, we must design systems that preserve human

agency. This requires creating interfaces that allow us-

ers to understand and modify algorithmic decisions,

providing options for human oversight, and maintain-

ing human-centered workflows that utilize technology

to augment, rather than replace, human judgment.95

3. Tools for Power-Balancing

More generally, a durable democracy must build struc-

tural resistance to myth-based capture. This means re-

balancing power.

Media: Restructure the Pulpit

Technology journalism plays a crucial role in shaping

public and policy understanding of AI, through edi-

torial choices about coverage, sourcing, and framing

that influence whether industry narratives are accept-

ed uncritically or subjected to rigorous evaluation. It

often amplifies industry perspectives rather than pro-

viding independent analysis. As the book AI Snake Oil

notes, “If researchers and companies kindle the sparks

of hype, the media fans the flames.”96

This amplification isn’t limited to industry hype; it

extends to bizarre political theater. In October 2025,

when Albania’s Prime Minister announced his AI “min-210 AI TOOLS, NOT GODS

ister” was “pregnant” with 83 digital assistants,97 he

was framing a simple software rollout as a magical

birth. Instead of ignoring this bizarre take, the media

picked it up as a journalist at a European Commission

briefing asked for a reaction to the happy news,” and

the Commission’s spokesperson, rather than correcting

the absurd anthropomorphism, played along, offering

official “congratulations.”98 This is a perfect example

of the media and policy institutions choosing to ampli-

fy a narrative rather than ignoring it or providing the

critical analysis needed to hold power to account. Fos-

tering a media ecosystem capable of more independent,

critical analysis is therefore essential. Equally import-

ant in today’s diverse media environment is investing

in public media literacy, enabling citizens to critically

assess the technological narratives presented to them.

Fostering a resilient media ecosystem, such as by sup-

porting public media or initiatives like the BBC’s Verify

service,99 is crucial for countering misinformation and

myths.

Research: Open the Black Box

AI research and development are often conducted in

the laboratories of a few corporate giants, shielded

from democratic oversight despite being fueled by pub-

lic investment in the form of tax incentives, research

grants, and infrastructure support. If democracy is to

have any say in our technological future, it must have

a seat at the table where that future is being designed.

Further, public investment must be used to create alter-

natives to corporate-controlled technology. When gov-

ernments fund AI research, they can attach strings that

Principles for Mature AI Governance 211

require public benefit, open development, and demo-

cratic accountability, creating a technology ecosystem

that serves more than commercial advantage.100

Finally, academic institutions require structural reforms

that reduce dependence on industry funding. They must

be bastions of independent thought, not extensions

of corporate R&D. Even though it is currently under

pressure due to massive budget cuts from the Trump

administration,101 the National Science Foundation’s

historic support for fundamental research provides a

model for public investment that generates knowledge

serving broad social interests rather than narrow com-

mercial applications.102 The goal is not to eliminate

industry collaboration but to ensure that academic re-

search maintains the independence necessary for genu-

ine evaluation of technological benefits and risks.

International cooperation, guided by frameworks such

as the Montreal Declaration for Responsible AI,103 can

further align research priorities with shared democratic

values, resisting the pull of a competitive race to the

bottom in favor of a collaborative pursuit of the public

good. Democratic countries could collaborate on re-

search priorities that advance shared values of human

rights, democratic governance, and social justice, in-

stead of leaving AI governance to competition between

national technology champions.

Market: Address Concentration

The extreme concentration of wealth and market power

in the hands of a few tech companies warrants scrutiny.

Robust antitrust enforcement is crucial, as it addresses

the market concentration that enables these companies 212 AI TOOLS, NOT GODS

to shape public discourse and eliminate competitive

alternatives. When a few companies control the core

infrastructure of AI development, their leaders natu-

rally become like prophets, regardless of the merit of

their visions. Addressing the potential negative impact

of such concentration is a prerequisite for breaking the

spell of the Heaven myth.

A Coherent System for Governance

The three frameworks of Accountability, Proportionali-

ty, and Durability are complementary layers of a coher-

ent system for mature governance. The AI as Product

framework provides the necessary sword, establishing

clear liability for the platform’s own actions and de-

signs. The Public Health model creates the expert agen-

cies needed to assess risk and inform both regulators

and the public. Finally, the Principle-Based Regulation

approach provides the overarching values that guide

the system. It is a system of checks and balances for the

algorithmic age, one that can simultaneously protect

expression and hold powerful actors accountable for

the harms their systems directly cause.

Ultimately, while the catalyst for this analysis has been

the rise of AI, the principles of accountability, propor-

tionality, and durability outlined are not unique to AI.

They are the essential components of wise governance.

The next transformative force may emerge from bio-

technology, quantum computing, or a field yet to be

imagined. If we learn the lessons of the AI era, we can

build a resilient democratic framework ready for that

Principles for Mature AI Governance 213

future: one that governs based on evidence and shared

values, not on myth and fear.

Table 2—Flawed Mythologies vs.

the Mature Governance Frameworks

MAGIC MADNESS

(Mystification) (Hype/Panic)

RESULTING POLICY RESULTING POLICY FAILURE FAILURE

Policy Distraction & Emergency Governance &

Techno-Solutionism Policy Whiplash

MATURE GOVERNANCE MATURE GOVERNANCE PRINCIPLE PRINCIPLE

Accountability Proportionality

CORRESPONDING CORRESPONDING FRAMEWORK FRAMEWORK

AI as a Product: Demystifies AI Governance as Public

AI by applying product liability Health: Replaces emotional law, focusing on concrete extremes with evidence-

harms and manufacturer based risk assessment and

responsibility. targeted interventions.

HEAVEN SIN

(Salvation) (Scapegoating)

RESULTING POLICY RESULTING POLICY FAILURE FAILURE

Prophetic Deference & Blame Deflection &

Democratic Abdication Flawed Regulation

MATURE GOVERNANCE MATURE GOVERNANCE PRINCIPLE PRINCIPLE

Durability Shared Responsibility

CORRESPONDING CORRESPONDING FRAMEWORK FRAMEWORK

AI Regulation as PBR: Integrated System: Holds Establishes stable, human actors accountable

high-level principles to (via liability) instead of

prevent capture by utopian blaming bots, while protecting

promises. platforms as forums. 214 AI TOOLS, NOT GODS

Epilogue 215

EPILOGUE

A CALL TO ACTION FOR THE AGE OF UNENCHANTMENT

T he show is over. The spell is broken. We have seen how the myths of Magic, Madness, Heaven, and Sin create “policy hallucinations” that cost us bil-lions, distract us from real problems, and lead to an

abdication of our democratic responsibilities.

The central illusion uniting all four myths is technolog-

ical determinism: the fatalistic belief that technology is

an autonomous force and that we are powerless to do

anything but react to its inevitable trajectory.

This premise is a lie.

Technology is not destiny; it is a mirror. It refl ects our

choices, our values, and our structures of power. The

future of AI will not be discovered in the code; it will

be built by our laws, our institutions, and our collective

will.

Rejecting the myths means accepting a profound re-

sponsibility. This book is not just an analysis; it is a 216 AI TOOLS, NOT GODS

call to action. The principles of Accountability, Propor-

tionality, and Durability are not abstract theories; they

are a blueprint for the patient, necessary, and essential

work of democratic governance.

This work belongs to all of us.

To the Policymaker

Your desk is littered with proposals that either ban AI

in a panic or deregulate it in a fever. You are being

pushed and pulled by the myths of Madness and Sin.

Resist them. Your task is not to regulate “AI” as a

magical, monolithic entity. Your task is to govern spe-

cific, high-risk activities enabled by these tools. Em-

brace technology-neutrality. Instead of writing a law

for “ChatGPT,” strengthen the laws against fraud, dis-

crimination, and non-consensual image abuse, no mat-

ter what tool is used.

Stop legislating for Merlin. Adopt the “AI as Product”

framework. Hold companies liable for their defective

designs, just as you would an automaker. Adopt the

“Public Health” model. Focus regulatory energy pro-

portionally on high-risk applications, such as algo-

rithms used in medicine and law, rather than on low-

risk tools, like spam detection.

Do not abdicate your duty to the prophets. The future

is not their vision to bestow; it is your responsibility to

build.

To the Journalist

You are the first line of defense against the myths. Yet,

the “Policy Machine” depends on media to amplify

hype and panic.

Epilogue 217

Reject the script. When a tech CEO makes a grand

promise (the Heaven myth) or a dire warning (the Sin

myth), do not report it as a weather forecast. Treat it as

what it is: a sophisticated press release.

Ask the hard questions. Move past the “magic” of

the demo and ask about the mechanics. Ask about the

training data, the labor conditions of the annotators,

and the documented error rates. Ask for evidence, not

just evangelism. When you hear the words “inevita-

ble,” “sentient,” or “superintelligence,” your skepti-

cism should be at its peak.

Your job is not to transcribe the prophecies; it is to in-

vestigate the prophets.

To the Citizen, the Educator, and the Leader

You are told that this technology is too complex, too

fast, and too powerful for you to understand. This is

the Magic myth at its most disempowering.

Reclaim your agency. You do not need to be a data sci-

entist to have a legitimate voice in this debate. You are

an expert in what it means to be human, and that is the

only expertise that matters in a democracy.

Demand a seat at the table. Organize. Join citizens’ as-

semblies. Ask your local school board, your city coun-

cil, and your employer how they are using automat-

ed systems. Demand transparency, not in code, but in

plain language. Ask: “What is this tool for?” “How

does it make decisions?” “Who is responsible when it

is wrong?”

The challenge of AI is not a technical problem for en-

gineers to solve. It is a political and social challenge 218 AI TOOLS, NOT GODS

that calls for the best of our democratic traditions. This

epilogue is a call to end the age of enchantment and

embrace the necessary work of building the future we

want, not the one we’ve been sold.

The burden is heavy, but the promise is a future that

serves human fl ourishing, not the other way around.

That is the burden. That is the promise. Now, let’s get

to work.

Endnotes 219

ENDNOTES

Introduction

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Chapter 2

1 Clothilde Goujard, “Italian Privacy Regulator Bans ChatGPT,”

Politico, March 31, 2023, https://www.politico.eu/article/italian-

privacy-regulator-bans-chatgpt/

224 AI TOOLS, NOT GODS

2 K.C. Halm, John D. Seiver and Patrick J. Austin, “Italy’s Data

Protection Agency Lifts Ban on ChatGPT,” Davis Wright

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2023, https://www.dwt.com/blogs/artificial-intelligence-law-

advisor/2023/05/ai-chatgpt-italy-ban-lifted

3 Natasha Lomas, “ChatGPT Resumes Service in Italy after Adding

Privacy Disclosures and Controls,” TechCrunch, April 28, 2023,

https://techcrunch.com/2023/04/28/chatgpt-resumes-in-italy/ 4 Frank R. Baumgartner and Bryan D. Jones, Agendas and Instability in American Politics (Chicago: University of Chicago

Press, 1993), https://archive.org/details/agendasinstabili0000baum 5 Peter A. Hall, “Policy Paradigms, Social Learning, and the State: The Case of Economic Policymaking in Britain,”

Comparative Politics 25, no. 3 (April 1993): 275–96, https://doi.

org/10.2307/422246

6 Steven Shapin, and Simon Schaffer, Leviathan and the Air-Pump:

Hobbes, Boyle, and the Experimental Life (Princeton: Princeton

University Press, 1985), https://openlibrary.org/books/

OL21623774M/Leviathan_and_the_air-pump 7 Julie Thompson Klein, Interdisciplinarity: History, Theory, and Practice (Detroit: Wayne State University Press, 1990), https://

archive.org/details/interdisciplinar00kleirich

8 Nur Ahmed, Muntasir Wahed, and Neil C. Thompson, “The

Growing Influence of Industry in AI Research,” Science 379, no.

6635 (March 3 2023): 884-886, https://doi.org/10.1126/science.

ade2420

9 Karen Hao, Empire of AI (Allen Lane, 2025), https://

www.penguin.co.uk/books/460331/empire-of-ai-by-hao-

karen/9780241678923

10 Maja Schyns, “Why and how is the power of Big Tech increasing

in the policy process? The case of generative AI,” Policy and

Society 44, no. 1 (2025): 52-73, https://academic.oup.com/

policyandsociety/article/44/1/52/7636223

11 Diana Crane, Invisible Colleges: Diffusion of Knowledge in

Scientific Communities (Chicago: University of Chicago Press,

1972), https://archive.org/details/invisiblecollege0000cran 12 Andrew Rich, Think Tanks, Public Policy, and the Politics of Expertise (Cambridge & New York: Cambridge University Press,

2004), https://archive.org/details/thinktankspublic00andr 13 Murray J. Edelman, Constructing the Political Spectacle (Chicago: University of Chicago Press, 1988), https://archive.org/details/

constructingpoli00edel

Endnotes 225

14 Natasha Dow Schüll, “Data for Life: Wearable Technology and

the Design of Self-Care,” BioSocieties 11, no. 3 (October 2016):

317–33, https://doi.org/10.1057/biosoc.2015.47 15 Bobby Allyn, “Stung By Media Coverage, Silicon Valley Starts Its Own Publications,” NPR, June 25, 2021, https://www.npr.

org/2021/06/25/1010066447/stung-by-media-coverage-silicon-

valley-starts-its-own-publications

16 Wikipedia, s.v. “Churnalism,” https://en.wikipedia.org/wiki/

Churnalism

17 Matthew Hindman, The Internet Trap: How the Digital Economy

Builds Monopolies and Undermines Democracy (Princeton:

Princeton University Press, 2018), https://doi.org/10.23943/

princeton/9780691159263.001.0001

18 Herbert J. Gans, Deciding What’s News: A Study of CBS Evening

News, NBC Nightly News, Newsweek, and Time (Evanston, IL:

Northwestern University Press, 2004), https://openlibrary.org/

books/OL3312029M/Deciding_what%27s_news 19 Madhumita Murgia, Cristina Criddle, and Melissa Heikkilä, “AI pioneers claim human-level general intelligence is already

here,” Financial Times, November 6, 2025, https://www.ft.com/

content/5f2f411c-3600-483b-bee8-4f06473ecdc0 20 Ferenc Huszár, Sofia Ira Ktena, Conor O’Brien, Luca Belli, Ashton Schlaikjer, and Moritz Hardt, “Algorithmic Amplification

of Politics on Twitter,” Proceedings of the National Academy

of Sciences of the United States of America 119, no. 1 (2022):

e2025334119, https://doi.org/10.1073/pnas.2025334119 21 Benedict R. O’G. Anderson, Imagined Communities: Reflections on the Origin and Spread of Nationalism, rev. ed. (London

& New York: Verso, 2006), https://archive.org/details/

imaginedcommunit00ande_0

22 James Lewis, “AI and Arms Races,” Center for European Policy

Analysis, June 3, 2025, https://cepa.org/article/ai-and-arms-races/ 23 John W. Kingdon, Agendas, Alternatives, and Public Policies, 2nd ed. (New York: HarperCollins College Publishers, 1995), https://

archive.org/details/agendasalternati00king

24 Alvin Carpio, “The rise of the political entrepreneur and why

we need more of them,” World Economic Forum, November 23,

2017, https://www.weforum.org/stories/2017/11/the-rise-of-the-

political-entrepreneur-and-why-we-need-more-of-them/ 25 Deborah Stone, Policy Paradox: The Art of Political Decision Making, 3rd ed. (New York: W.W. Norton & Company, 2012),

https://archive.org/details/policyparadoxart0000ston 226 AI TOOLS, NOT GODS

26 Paul Pierson, “Increasing Returns, Path Dependence, and the

Study of Politics,” American Political Science Review 94, no. 2

(June 2000): 251–67, https://www.jstor.org/stable/2586011 27 Theda Skocpol, Diminished Democracy: From Membership to Management in American Civic Life (Norman, OK:

University of Oklahoma Press, 2003), https://archive.org/details/

diminisheddemocr0000skoc

28 John W. Kingdon, Agendas, Alternatives, and Public Policies, 2nd

ed. (New York: HarperCollins College Publishers, 1995), https://

archive.org/details/agendasalternati00king

29 Ivan Jureta, “Policy Windows: What They Are And When They

Occur,” Ivan Jureta blog, December 2024, https://ivanjureta.com/

policy-windows-what-they-are-and-when-they-occur/ 30 Michael Lipsky, Street-Level Bureaucracy: Dilemmas of the Individual in Public Services (New York: Russell Sage Foundation,

1980), https://archive.org/details/streetlevelburea0000lips 31 James Q. Wilson, Bureaucracy: What Government Agencies Do and Why They Do It (New York: Basic Books, 1989), https://

archive.org/details/bureaucracywhatg00wils

32 Cary Coglianese and David Lehr, “Regulating by Robot:

Administrative Decision Making in the Machine-Learning Era,”

Georgetown Law Journal 105, no. 5 (2017): 1147–1223, https://

scholarship.law.upenn.edu/faculty_scholarship/1734 33 James Q. Wilson, Bureaucracy: What Government Agencies Do and Why They Do It (New York: Basic Books, 1989), https://

archive.org/details/bureaucracywhatg00wils

34 Cornelius M. Kerwin and Scott R. Furlong, Rulemaking: How

Government Agencies Write Law and Make Policy, 4th ed.

(Washington, DC: CQ Press, 2010), https://archive.org/details/

rulemakinghowgov0000kerw_4edi

35 Ronald M. Dworkin, Law’s Empire (Cambridge, MA: Belknap

Press of Harvard University Press, 1986), https://archive.org/

details/lawsempire0000dwor

36 Edward H. Levi, An Introduction to Legal Reasoning (Chicago:

University of Chicago Press, 1949)

37 Jerome N. Frank, Courts on Trial: Myth and Reality in American

Justice (Princeton, NJ: Princeton University Press, 1949), https://

archive.org/details/courtsontrialmyt0000fran

38 Paul Pierson, “Increasing Returns, Path Dependence, and the

Study of Politics,” American Political Science Review 94, no. 2

(June 2000): 251–67, https://www.jstor.org/stable/2586011

Endnotes 227

39 Anu Bradford, The Brussels Effect: How the European Union

Rules the World (Oxford & New York: Oxford University Press,

2020), https://doi.org/10.1093/oso/9780190088583.001.0001 40 Theodore J. Lowi, “American Business, Public Policy, Case-Studies, and Political Theory,” World Politics 16, no. 4 (July

1964): 677–715, https://doi.org/10.2307/2009452 41 Robert A. Dahl, Democracy and Its Critics (New Haven, CT: Yale University Press, 1989), https://archive.org/details/

democracyitscrit00dahl_0

Chapter 3

1 Leon Furze, “Myths, Magic, and Metaphors: The Language of

Generative AI,” LeonFurze, October 18, 2023, https://leonfurze.

com/2023/10/18/myths-magic-and-metaphors-the-language-of-

generative-ai/

2 OpenAI, “Concepts,” OpenAI Platform, https://platform.openai.

com/docs/concepts

3 Wikipedia, s.v. “Clarke’s Three Laws,”https://en.wikipedia.org/

wiki/Clarke%27s_three_laws

4 “What Is Google Bard? How the AI Chatbot Works, How to Use

It and Why It’s Controversial,” Fox News, June 29, 2023, https://

www.foxnews.com/tech/what-google-bard-how-the-ai-chatbot-

works-how-use-it-why-its-controversial

5 Yusuf Mehdi, “Announcing Microsoft Copilot, Your Everyday

AI Companion,” The Official Microsoft Blog, September 21,

2023, https://blogs.microsoft.com/blog/2023/09/21/announcing-

microsoft-copilot-your-everyday-ai-companion/ 6 Andreu Belsunces Gonçalves and Jascha Bareis, “Expanding Hype Literacy to Protect Democracy,” Tech Policy Press, August 21,

2025, https://www.techpolicy.press/expanding-hype-literacy-to-

protect-democracy/

7 Dario Amodei, “The Urgency of Interpretability,” darioamodei.

com, April 2025, https://www.darioamodei.com/post/the-urgency-

of-interpretability

8 Dario Amodei, “The Urgency of Interpretability,” darioamodei.

com, April 2025, https://www.darioamodei.com/post/the-urgency-

of-interpretability

9 The Editorial Board, “AI Should Not Be a Black Box,” Financial

Times, May 30, 2024, https://www.ft.com/content/9378339f-

a0aa-434a-a687-5dd9a13df5fe

10 Matthew Hutson, “AI Researchers Allege That Machine Learning

Is Alchemy,” Science, May 3, 2018, https://www.science.org/

content/article/ai-researchers-allege-machine-learning-alchemy 228 AI TOOLS, NOT GODS

11 Eryk Salvaggio, “The Black Box Myth: What the Industry

Pretends Not to Know About AI,” Tech Policy Press, June 17,

2025, https://www.techpolicy.press/the-black-box-myth-what-the-

industry-pretends-not-to-know-about-ai/

12 Newley Purnell and Parmy Olson, “AI Startup Boom Raises

Questions of Exaggerated Tech Savvy,” The Wall Street Journal,

August 15, 2019, https://www.wsj.com/articles/ai-startup-boom-

raises-questions-of-exaggerated-tech-savvy-11565775004 13 Alexei Alexis, “AI Startup Reaped Millions Using Bogus Claims, FTC Suit Says,” CFO Dive, August 26, 2025, https://www.

cfodive.com/news/ai-startup-reaped-millions-using-bogus-claims-

ftc-suit/758587/.

14 Emma Woolacott, “What is “AI washing” and why is it a

problem?,” BBC, June 27, 2024, https://www.bbc.com/news/

articles/c9xx8122893o

15 Emily M. Bender, “We’re going to need journalists to stop talking

about synthetic text extruding machines...,” LinkedIn, https://

www.linkedin.com/posts/ebender_were-going-to-need-journalists-

to-stop-talking-activity-7359229969345978369-3gnI/ 16 Luciano Floridi and Anna C. Nobre, “Anthropomorphising Machines and Computerising Minds: The Crosswiring of

Languages between Artificial Intelligence and Brain & Cognitive

Sciences,” Minds and Machines 34, no. 1 (April 25, 2024): 1–9,

https://doi.org/10.2139/ssrn.4738331

17 Tania Lombrozo, “How AI Is Learning to Think on Its Own Like

Humans,” SciTechDaily, September 18, 2024, https://scitechdaily.

com/how-ai-is-learning-to-think-on-its-own-like-humans/ 18 Jason Koebler, “Is Google’s AI Actually Discovering ‘Millions of New Materials?,’” 404 Media, April 11, 2024, https://

www.404media.co/google-says-it-discovered-millions-of-new-

materials-with-ai-human-researchers/

19 Brigitte Nerlich, “Hunting for AI Metaphors,” Making Science

Public (University of Nottingham blog), April 12, 2024, https://

blogs.nottingham.ac.uk/makingsciencepublic/2024/04/12/hunting-

for-ai-metaphors/

20 FLeon Furze, “AI Metaphors We Live By: The Language of

Artificial Intelligence,” Leon Furze (blog), July 19, 2024, https://

leonfurze.com/2024/07/19/ai-metaphors-we-live-by-the-language-

of-artificial-intelligence/

21 Karen Hao, Empire of AI (Allen Lane, 2025), https://

www.penguin.co.uk/books/460331/empire-of-ai-by-hao-

karen/9780241678923

Endnotes 229

22 Derya Soydaner, “Attention Mechanism in Neural Networks:

Where it Comes and Where it Goes,” Neural Computing and

Applications 34 (2022): 15391–403, https://doi.org/10.1007/

s00521-022-07366-3

23 Matthew Shardlow and Piotr Przybyła, “Deanthropomorphising

NLP: Can a Language Model Be Conscious?,” PLoS ONE 19,

no. 12 (2024): e0307521, https://doi.org/10.1371/journal.

pone.0307521

24 “What is computer vision?,” IBM, https://www.ibm.com/think/

topics/computer-vision

25 Bharath K, “Object Detection Algorithms and Libraries,”

Neptune.ai, April 15, 2024, https://neptune.ai/blog/object-

detection-algorithms-and-libraries

26 Tingting Qiao et al., “Learn, Imagine and Create: Text-to-Image

Generation from Prior Knowledge,” in Advances in Neural

Information Processing Systems 32, edited by H. Wallach et al.

(Curran Associates, Inc., 2019), https://papers.nips.cc/paper_fi les/

paper/2019/hash/d18f655c3fce66ca401d5f38b48c89af-Abstract.

html

27 Leon Furze, “AI Metaphors We Live By: The Language of

Artifi cial Intelligence,” Leon Furze (blog), July 19, 2024, https://

leonfurze.com/2024/07/19/ai-metaphors-we-live-by-the-language-

of-artifi cial-intelligence/

28 Melissa Heikkilä, “The ‘Hallucinations’ That Haunt AI: Why

Chatbots Struggle to Tell the Truth,” Financial Times, July 22,

2025, https://www.ft.com/content/7a4e7eae-f004-486a-987f-

4a2e4dbd34fb

29 Mike Onslow, “The Phantom Menace: AI Hallucinations and

Their Impact,” Antics, March 10, 2024, https://antics.tv/p/

understanding-ai-hallucination-insights-and-implications

30 Anna Mills and Nate Angell, “Are We Tripping? The Mirage of

AI Hallucinations,” SSRN, 2025, https://papers.ssrn.com/sol3/

papers.cfm?abstract_id=5127162

31 Wikipedia, s.v. “Hallucination (Artifi cial Intelligence),” https://

en.wikipedia.org/wiki/Hallucination_(artifi cial_intelligence

32 Emily M. Bender, Timnit Gebru, Angelina McMillan-Major,

and Shmargaret Shmitchell. 2021. On the Dangers of Stochastic

Parrots: Can Language Models Be Too Big? In Proceedings

of the 2021 ACM Conference on Fairness, Accountability,

and Transparency (FAccT ‘21). Association for Computing

Machinery, New York, NY, USA, 610--623. https://doi.

org/10.1145/3442188.3445922

230 AI TOOLS, NOT GODS

33 Nicholas Carlini et al., “Extracting Training Data from Large

Language Models,” arXiv, December 15, 2020, https://arxiv.org/

abs/2012.07805

34 Wikipedia, s.v. “Rote Learning,” https://en.wikipedia.org/wiki/

Rote_learning

35 Nicholas Carlini et al., “Quantifying Memorization Across

Neural Language Models,” arXiv:2202.07646v3 [cs.LG], March

6, 2023, https://doi.org/10.48550/arXiv.2202.07646 36 Leo Gao et al., “The Pile: An 800GB Dataset of Diverse Text for Language Modeling,” arXiv:2101.00027 [cs.CL], submitted

December 31, 2020, https://doi.org/10.48550/arXiv.2101.00027 37 Jared Kaplan et al., “Scaling Laws for Neural Language Models,”

arXiv:2001.08361v1 [cs.LG], submitted January 23, 2020,

https://arxiv.org/abs/2001.08361

38 Chris Olah et al., “Zoom In: An Introduction to Circuits,” Distill,

March 10, 2020, https://distill.pub/2020/circuits/zoom-in/ 39 Ivo Emanuilov and Thomas Margoni, “Memorisation in Generative Models and EU Copyright Law: An Interdisciplinary

View,” Kluwer Copyright Blog, March 26, 2024, https://

legalblogs.wolterskluwer.com/copyright-blog/memorisation-in-

generative-models-and-eu-copyright-law-an-interdisciplinary-

view/

40 Julio Carvalho, “The Stubborn Memory of Generative AI:

Overfitting, Fair Use, and the AI Act,” Kluwer Copyright Blog,

April 8, 2024, https://legalblogs.wolterskluwer.com/copyright-

blog/the-stubborn-memory-of-generative-ai-overfitting-fair-use-

and-the-ai-act/

41 Getty Images (US) Inc and others v Stability AI Ltd [2025] EWHC

2863 (Ch) (BAILII), https://www.judiciary.uk/judgments/getty-

images-v-stability-ai/

42 Andres Guadamuz, “Getty Images v Stability AI: A landmark

High Court ruling on AI, copyright, and trade marks,”

Technollama, November 6, 2025, https://www.technollama.co.uk/

getty-images-v-stability-ai-a-landmark-high-court-ruling-on-ai-

copyright-and-trade-marks

43 Landgericht München I, “Urheberrechtsverletzung durch Training

einer KI,” (Pressemitteilung 11/2025, November 11, 2025),

https://www.justiz.bayern.de/gerichte-und-behoerden/landgericht/

muenchen-1/presse/2025/11.php

44 Hamish Hector, Are you polite to ChatGPT? Here’s where you

rank among AI chatbot users, Techradar, https://www.techradar.

com/computing/artificial-intelligence/are-you-polite-to-chatgpt-

heres-where-you-rank-among-ai-chatbot-users

Endnotes 231

45 Becca Caddy, I stopped saying thanks to ChatGPT -- here’s what

happened, TechRadar, https://www.techradar.com/computing/

artificial-intelligence/i-stopped-saying-thanks-to-chatgpt-heres-

what-happened

46 Hamish Hector, Are you polite to ChatGPT? Here’s where you

rank among AI chatbot users, Techradar, https://www.techradar.

com/computing/artificial-intelligence/are-you-polite-to-chatgpt-

heres-where-you-rank-among-ai-chatbot-users 47 Wikipedia, s.v. “Roko’s basilisk”, https://en.wikipedia.org/wiki/

Roko’s_basilisk

48 “Funeral of speaking clock voice Brian Cobby in Brighton,” BBC,

November 16, 2012, https://www.bbc.com/news/uk-england-

sussex-20353154

49 Yin, Ziqi, et al. “Should we respect LLMs? A cross-lingual study

on the influence of prompt politeness on LLM performance.”

Proceedings of the Second Workshop on Social Influence

in Conversations (SICon 2024). 2024, https://arxiv.org/

abs/2402.14531

50 Placani, Adriana. (2024). Anthropomorphism in AI: hype

and fallacy. AI and Ethics. 4. 10.1007/s43681-024-00419-

4, https://www.researchgate.net/publication/377976318_

Anthropomorphism_in_AI_hype_and_fallacy 51 ARTICLE 19, “Algorithmic people-pleasers: Are AI chatbots telling you what you want to hear?,” May 20, 2025, https://

www.article19.org/resources/algorithmic-people-pleasers-are-ai-

chatbots-telling-you-what-you-want-to-hear/

52 Samantha Cole, “Users Say Replika AI Is ‘Sexually Harassing’

Them. The Company Says It’s ‘For Their Own Safety’,” Vice,

February 14, 2023, https://www.vice.com/en/article/my-ai-is-

sexually-harassing-me-replika-chatbot-nudes/

53 Simon Willison, “The surprise deprecation of GPT-4o for

ChatGPT consumers,” Simon Willison’s Weblog, August 8, 2025,

https://simonwillison.net/2025/Aug/8/surprise-deprecation-of-gpt-

4o/

54 “‘We will live as one in heaven’: Belgian man dies by suicide

after chatbot exchanges,” Belga News Agency, March 28, 2023,

https://www.belganewsagency.eu/we-will-live-as-one-in-heaven-

belgian-man-dies-of-suicide-following-chatbot-exchanges 55 Hill, Kashmir. “A Teen Was Suicidal. ChatGPT Was the Friend He Confided In.” The New York Times, August 27, 2025. https://

www.nytimes.com/2025/08/26/technology/chatgpt-openai-suicide.

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232 AI TOOLS, NOT GODS

56 Hill, Kashmir. “A Teen Was Suicidal. ChatGPT Was the Friend

He Confided In.” The New York Times, August 27, 2025. https://

www.nytimes.com/2025/08/26/technology/chatgpt-openai-suicide.

html

57 “Meta let chatbots engage in inappropriate conversations with

minors, Reuters reports,” Business & Human Rights Resource

Centre, August 14, 2025, https://www.business-humanrights.

org/en/latest-news/meta-let-chatbots-engage-in-inappropriate-

conversations-with-minors-reuters-reports/

58 Elizabeth Laird, Maddy Dwyer, and Hannah Quay-de la Vallee,

“Hand in Hand: Schools’ Embrace of AI Connected to Increased

Risks to Students,” Center for Democracy & Technology,

October 8, 2025, https://cdt.org/insights/hand-in-hand-schools-

embrace-of-ai-connected-to-increased-risks-to-students/ 59 Rebecca Bellan, “California becomes first state to regulate AI companion chatbots,” TechCrunch, October 13, 2025, https://

techcrunch.com/2025/10/13/california-becomes-first-state-to-

regulate-ai-companion-chatbots/

60 Anna Merod, “Character.AI to ban teens from chatting with its

AI companions,” K-12 Dive, October 29, 2025, https://www.

k12dive.com/news/characterai-to-ban-teens-from-chatting-with-

its-ai-companions/804199/

61 Sam Altman (@sama), “We made ChatGPT pretty restrictive

to make sure we were being careful with mental health

issues...” X, October 14, 2025, 6:02 p.m., https://x.com/sama/

status/1978129344598827128

62 Yutong Zhang et al., “The Rise of AI Companions: How Human-

Chatbot Relationships Influence Well-Being,” June 14, 2025,

https://arxiv.org/html/2506.12605v1#bib.bib100 63 Wikipedia, s.v. “ELIZA,” https://en.wikipedia.org/wiki/ELIZA. 64 Yaroslav Bulatov, New Navy Device Learns by Doing, Medium, https://yaroslavvb.medium.com/new-navy-device-learns-by-doing-

62f31d2dc7ba

65 Wikipedia, s.v. “Mycin,” https://en.wikipedia.org/wiki/Mycin 66 Avron Barr and Shirley Tessler, “Expert Systems: A Technology Before Its Time,” AI Expert, June 1995, http://www.ksl.stanford.

edu/people/eaf/cs226/AIExpert95.pdf

67 Paul N. Edwards, The Closed World: Computers and the Politics

of Discourse in Cold War America (Cambridge, MA: MIT Press,

1996), https://direct.mit.edu/books/monograph/4762/The-Closed-

WorldComputers-and-the-Politics-of

68 Wikipedia, s.v. “Backpropagation,” https://en.wikipedia.org/wiki/

Backpropagation

Endnotes 233

69 Wikipedia, s.v. “Gradient Descent,” https://en.wikipedia.org/wiki/

Gradient_descent

70 Andrew Griffin, Facebook’s artificial intelligence robots shut

down after they start talking to each other in their own language,

The Independent, https://www.independent.co.uk/life-style/

facebook-artificial-intelligence-ai-chatbot-new-language-research-

openai-google-a7869706.html

71 Wikipedia, s.v. “Reward Hacking,” https://en.wikipedia.org/wiki/

Reward_hacking

72 Mark Wilson, “AI Is Inventing Languages Humans Can’t

Understand. Should We Stop It?,” Fast Company, July 14, 2017,

https://www.fastcompany.com/90132632/ai-is-inventing-its-own-

perfect-languages-should-we-let-it

73 Wikipedia, s.v. “Sophia (robot),” https://en.wikipedia.org/wiki/

Sophia_(robot)

74 Cristina Maza, Saudi Arabia Gives Citizenship to a Non-Muslim,

English-Speaking Robot, Newsweek, https://web.archive.org/

web/20171108055113/http://www.newsweek.com/saudi-arabia-

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75 Richard Luscombe, Google engineer put on leave after saying

AI chatbot has become sentient, The Guardian, https://www.

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sentient-blake-lemoine

76 Notes from the conversation can be found here https://www.

documentcloud.org/documents/22058315-is-lamda-sentient-an-

interview/

77 The Google engineer who thinks its AI has come alive,

The Washington Post, https://www.washingtonpost.com/

technology/2022/06/11/google-ai-lamda-blake-lemoine/ 78 Jonathan Yerushalmy, “’I Want to Destroy Whatever I Want’: Bing’s AI Chatbot Unsettles US Reporter,” The

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79 Mustafa Suleyman, “We must build AI for people; not to be a

person,” Mustafa Suleyman, August 19, 2025, https://mustafa-

suleyman.ai/seemingly-conscious-ai-is-coming 80 Evgeny Morozov, To Save Everything, Click Here: Technology, Solutionism, and the Urge to Fix Problems That Don’t Exist

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81 SHenrik Skaug Sætra and Evan Selinger, “Technological

Remedies for Social Problems: Defining and Demarcating 234 AI TOOLS, NOT GODS

Techno-Fixes and Techno-Solutionism,” Science and Engineering

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s11948-024-00524-x; https://pmc.ncbi.nlm.nih.gov/articles/

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intelligence-solutionism-hype.html

83 Arvind Narayanan and Sayash Kapoor, AI Snake Oil:

What Artificial Intelligence Can Do, What It Can’t, and

How to Tell the Difference, Princeton University Press,

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RQEcsI4xXYAVOh5RPsUKb21R4HSlWqBCT70F_Itgl 84 Techdirt Nerd Harder Tagged Articles, https://www.techdirt.com/

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85 Cory Doctorow, “ “Privacy preserving age verification” is

bullshit,” Pluralistic, August 15, 2025, https://pluralistic.

net/2025/08/14/bellovin/#wont-someone-think-of-the-

cryptographers

86 Virginia Eubanks, Automating Inequality: How High-Tech Tools

Profile, Police, and Punish the Poor (New York: St. Martin’s Press,

2018), https://www.amazon.com/Automating-Inequality-High-

Tech-Profile-Police/dp/1250074312

87 Rob Kitchin, The Data Revolution: A Critical Analysis of Big

Data, Open Data and Data Infrastructures (Los Angeles: SAGE

Publications, 2014), https://www.amazon.com/Data-Revolution-

Critical-Analysis-Infrastructures/dp/1529733758 88 Safiya Umoja Noble, Algorithms of Oppression: How Search Engines Reinforce Racism (New York: New York University Press,

2018), https://doi.org/10.2307/j.ctt1pwt9w5

89 Frank Pasquale, The Black Box Society: The Secret Algorithms

That Control Money and Information (Cambridge, MA: Harvard

University Press, 2015), https://archive.org/details/the-black-

box-society-the-secret-algorithms-that-control-money-and-

information-frank-pasquale

90 Cathy O’Neil, Weapons of Math Destruction: How Big Data

Increases Inequality and Threatens Democracy (New York:

Crown, 2016), https://archive.org/details/weapons-of-math-

destruction_202411

91 Ruha Benjamin, Race After Technology: Abolitionist Tools for

the New Jim Code (Cambridge & New York: Polity Press, 2019),

https://openlibrary.org/books/OL28147808M/Race_After_

Technology

Endnotes 235

92 Wikipedia, s.v. “Robodebt scheme”, https://en.wikipedia.org/wiki/

Robodebt_scheme

93 Wikipedia, s.v. “Dutch childcare benefits scandal”, https://

en.wikipedia.org/wiki/Dutch_childcare_benefits_scandal 94 Frances Mao, “Robodebt: Illegal Australian welfare hunt drove people to despair,” BBC News, July 7, 2023, https://www.bbc.

com/news/world-australia-66130105

95 Melissa Heikkilä, “Dutch scandal serves as a warning for Europe

over risks of using algorithms,” POLITICO, March 29, 2022,

https://www.politico.eu/article/dutch-scandal-serves-as-a-warning-

for-europe-over-risks-of-using-algorithms/

96 Newsdesk, “UAE Explores AI Judges,” Gulf Insight 360, February

7, 2025, https://gulfinsight360.com/uae-explores-ai-judges-a-bold-

step-towards-judicial-innovation/?amp=1

97 Neil Selwyn, Lessons to Be Learnt? Education,

Technosolutionism, and Sustainable Development, DOI:

10.1201/9781003325086-6, https://researchmgt.monash.edu/ws/

portalfiles/portal/453512258/453453733_oa.pdf 98 Michael Polanyi, The Tacit Dimension (Garden City, NY: Doubleday, 1966), https://openlibrary.org/books/OL5990259M/

The_tacit_dimension

99 Eric J. Topol, Deep Medicine: How Artificial Intelligence Can

Make Healthcare Human Again (New York: Basic Books, 2019),

https://www.amazon.com/Deep-Medicine-Artificial-Intelligence-

Healthcare/dp/1541644638

100 Gary Marcus, “Deep Learning: A Critical Appraisal,”

CoRR abs/1801.00631 (January 2, 2018), https://arxiv.org/

abs/1801.00631

101 Chan Lawrence, “Beware General Claims about ‘Generalizable

Reasoning Capabilities’ (of Modern AI Systems),” Alignment

Forum, June 11, 2025, https://www.alignmentforum.org/

posts/5uw26uDdFbFQgKzih/beware-general-claims-about-

generalizable-reasoning

102 “vTaiwan: rethinking democracy,” vTaiwan, last updated

December 2023, https://info.vtaiwan.tw/

103 “Decidim is a digital platform for citizen participation,” Decidim,

https://decidim.org/

104 Tarmo Kalvet, “Innovation: A Factor Explaining E-Government

Success in Estonia,” Electronic Government, an International

Journal 9, no. 2 (2012): 142–57, https://doi.org/10.1504/

EG.2012.046266

105 National Academies of Sciences, Engineering, and Medicine,

Securing the Vote: Protecting American Democracy 236 AI TOOLS, NOT GODS

(Washington, DC: National Academies Press, 2023), https://

nap.nationalacademies.org/catalog/25120/securing-the-vote-

protecting-american-democracy

106 Bruce Schneier, Click Here to Kill Everybody: Security and

Survival in a Hyper-connected World (New York: W. W. Norton

& Company, 2018), https://www.schneier.com/books/click-here/ 107 Tarleton Gillespie, Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions That Shape Social Media

(New Haven, CT: Yale University Press, 2018), https://archive.org/

details/Custodians_of_the_Internet_by_Terleton_Gillespie 108 Sarah T. Roberts, Behind the Screen: Content Moderation in the Shadows of Social Media (New Haven, CT: Yale University Press,

2019), https://openlibrary.org/books/OL28709710M/Behind_the_

Screen

109 David Robertson, “Ten Reasons to Say No: A Primer on Sidewalk

Labs’ Plan for Toronto” (The Bullet, Socialist Project), https://

socialistproject.ca/2019/10/ten-reasons-to-say-no-sidewalk-lab-

toronto/

110 Wikipedia, s.v. “Sidewalk Labs,” https://en.wikipedia.org/wiki/

Sidewalk_Labs

111 Greig Charnock, Hug March, and Ramon Ribera-Fumaz, “From

Smart to Rebel City? Worlding, Provincialising and the Barcelona

Model,” Urban Studies 58, no. 3 (2021): 581–600, https://doi.

org/10.1177/0042098019872119

112 Maya Wang, “China’s Chilling ‘Social Credit’ Blacklist,” Wall

Street Journal, https://www.wsj.com/articles/chinas-chilling-social-

credit-blacklist-1513036054

113 Wikipedia, s.v. “Social Credit System”, https://en.wikipedia.org/

wiki/Social_Credit_System

Chapter 4

1 Wikipedia, s.v. “P(doom),” https://en.wikipedia.org/wiki/P(doom) 2 Future of Life Institute, “Pause Giant AI Experiments: An Open Letter,” March 22, 2023, https://futureoflife.org/open-letter/pause-

giant-ai-experiments/

3 Center for AI Safety, “Statement on AI Risk,” May 30, 2023,

https://www.safe.ai/statement-on-ai-risk

4 Wikipedia, s.v. “Machine Intelligence Research Institute,” https://

en.wikipedia.org/wiki/Machine_Intelligence_Research_Institute 5 Nirit Weiss-Blatt, “Why The ‘Doom Bible’ Left Many Reviewers Unconvinced,” AI Panic, November 1, 2025, https://www.aipanic.

news/p/why-the-doom-bible-left-many-reviewers

Endnotes 237

6 Sigal Samuel, “”AI will kill everyone” is not an argument. It’s a

worldview,” Vox, September 17, 2025, https://www.vox.com/

future-perfect/461680/if-anyone-builds-it-yudkowsky-soares-ai-

risk

7 Emily M. Bender and Alex Hanna, “The AI Con: How to Fight

Big Tech’s Hype and Create the Future We Want,” The Con,

https://thecon.ai/

8 Meredith Ringel Morris et al., “Levels of AGI for

Operationalizing Progress on the Path to AGI,”

arXiv:2311.02462 [cs.AI], submitted November 4, 2023, https://

doi.org/10.48550/arXiv.2311.02462.

9 Wikipedia, s.v. “Artificial general intelligence,” https://

en.wikipedia.org/wiki/Artificial_general_intelligence 10 Émile P. Torres, “The Dangerous Ideas of ‘Longtermism’ and ‘Existential Risk’,” Current Affairs, July 28, 2021, https://

www.currentaffairs.org/news/2021/07/the-dangerous-ideas-of-

longtermism-and-existential-risk

11 Octavio Kulesz, Artificial Intelligence and International Cultural

Relations: Challenges and Opportunities for Cross-Sector

Collaboration (Stuttgart: ifa, 2024), https://opus.bsz-bw.de/ifa/

frontdoor/deliver/index/docId/1203/file/ifa-2024_kulesz_ai-intl-

cultural-relations.pdf

12 Cory Doctorow, “Don’t Believe the Criti-Hype,” Pluralistic,

September 30, 2021, https://pluralistic.net/2021/09/30/dont-

believe-the-criti-hype/

13 Arvind Narayanan and Sayash Kapoor, AI Snake Oil: What

Artificial Intelligence Can Do, What It Can’t, and How to Tell

the Difference (Princeton, NJ: Princeton University Press, 2024),

https://press.princeton.edu/books/hardcover/9780691249131/ai-

snake-oil.

14 Wikipedia, s.v. “Availability heuristic,” https://en.wikipedia.org/

wiki/Availability_heuristic

15 Wikipedia, s.v. “Roy Amara,”,https://en.wikipedia.org/wiki/Roy_

Amara

16 James Farrell, “BuzzFeed’s stock price soars as company

announces AI content push,” siliconANGLE, https://siliconangle.

com/2023/01/26/buzzfeeds-stock-price-soars-company-announces-

ai-content-push/

17 Tabrez Syed, “The AI Gold Rush: Are We Mining or Just Selling

Shovels?,” BoxCars AI, https://blog.boxcars.ai/p/the-ai-gold-rush-

are-we-mining-or

18 Weston Blasi, “Nvidia is now worth more than the GDP of

every country except these 11,” MarketWatch, https://www.

238 AI TOOLS, NOT GODS

marketwatch.com/story/nvidia-is-now-worth-more-than-the-gdp-

of-every-country-except-these-few-d58a3508 19 Tyler Roush, “Nvidia Becomes First Company Worth $5

Trillion,” Forbes, October 29, 2025, https://www.forbes.com/

sites/tylerroush/2025/10/29/nvidia-becomes-first-company-worth-

5-trillion/

20 Karen Hao, Empire of AI (Allen Lane, 2025), https://

www.penguin.co.uk/books/460331/empire-of-ai-by-hao-

karen/9780241678923

21 “A New MIT Report Reveals That While Generative AI Holds

Promise, Most Enterprise Initiatives to Drive Rapid Revenue

Growth Are Falling Flat, With 95% of Pilots Failing,” Fortune,

August 18, 2025, https://fortune.com/2025/08/18/mit-report-95-

percent-generative-ai-pilots-at-companies-failing-cfo/ 22 “Is AI a Bubble? Market Crash Sees $1 Trillion Wiped From S&P

500 as OpenAI’s GPT-5 Release Flops and Sam Altman’s ‘Bubble’

Comments Bite,” Fortune, August 24, 2025, https://fortune.

com/2025/08/24/is-ai-a-bubble-market-crash-gary-marcus-openai-

gpt5/

23 Saanya Ojha, “The 95% Problem: AI Isn’t Overhyped,” Saanya

Ojha’s Newsletter, Substack, https://saanyaojha.substack.com/p/

the-95-problem-ai-isnt-overhyped

24 Alex Singla et al., “The state of AI in 2025: Agents, innovation,

and transformation,” McKinsey & Company, November 5,

2025, https://www.mckinsey.com/capabilities/quantumblack/our-

insights/the-state-of-ai

25 Wikipedia s.v. “Gartner Hype Cycle,” https://en.wikipedia.org/

wiki/Gartner_hype_cycle

26 Wikipedia, s.v. “AI winter”, https://en.wikipedia.org/wiki/AI_

winter

27 Arvind Narayanan and Sayash Kapoor, AI Snake Oil: What

Artificial Intelligence Can Do, What It Can’t, and How to Tell

the Difference (Princeton, NJ: Princeton University Press, 2024),

https://press.princeton.edu/books/hardcover/9780691249131/ai-

snake-oil.

28 Noah Smith, “America’s future could hinge on whether AI slightly

disappoints,” Noahpinion (blog), October 12, 2025, https://www.

noahpinion.blog/p/americas-future-could-hinge-on-whether 29 Nils Pratley, “The AI valuation bubble is now getting silly,”

The Guardian, October 8, 2025, https://www.theguardian.com/

technology/nils-pratley-on-finance/2025/oct/08/the-ai-valuation-

bubble-is-now-getting-silly

Endnotes 239

30 Nils Pratley, “The AI valuation bubble is now getting silly,”

The Guardian, October 8, 2025, https://www.theguardian.com/

technology/nils-pratley-on-finance/2025/oct/08/the-ai-valuation-

bubble-is-now-getting-silly

31 Jonathan Weil, “Is the Flurry of Circular AI Deals a Win-Win—or

Sign of a Bubble?,” The Wall Street Journal, October 22, 2025,

https://www.wsj.com/tech/ai/is-the-flurry-of-circular-ai-deals-a-

win-winor-sign-of-a-bubble-8a2d70c5

32 Joanna Partridge, “Global stock markets fall sharply over AI

bubble fears,” The Guardian, November 5, 2025, https://www.

theguardian.com/business/2025/nov/05/global-stock-markets-fall-

sharply-over-ai-bubble-fears

33 UK Government - Department for Science, Innovation &

Technology and Office for Artificial Intelligence, “White Paper --

AI regulation: A pro-innovation approach,” published March 29,

2023, updated August 3, 2023, https://www.gov.uk/government/

publications/ai-regulation-a-pro-innovation-approach 34 French Government, “Make France an AI powerhouse,”

https://www.elysee.fr/admin/upload/default/0001/17/

d9c1462e7337d353f918aac7d654b896b77c5349.pdf 35 Singapore Government, Smart Nation and Digital Government Office, “National Artificial Intelligence Strategy,” https://www.

smartnation.gov.sg/initiatives/national-ai-strategy 36 CNBC, “Putin: Leader in artificial intelligence will rule world,”

September 4, 2017, https://www.cnbc.com/2017/09/04/putin-

leader-in-artificial-intelligence-will-rule-world.html 37 Kai-Fu Lee, “China’s Sputnik Moment and the Sino American Battle for AI Supremacy,” Asia Society Magazine, https://

asiasociety.org/magazine/article/chinas-sputnik-moment-and-sino-

american-battle-ai-supremacy

38 Wikipedia, s.v. “Joint Artificial Intelligence Center,” https://

en.wikipedia.org/wiki/Joint_Artificial_Intelligence_Center 39 National Security Commission on Artificial Intelligence, “Final Report,” March 2021, https://reports.nscai.gov/final-report/ 40 Executive Office of the President, Office of Science and Technology Policy, America’s AI Action Plan (Washington, DC:

The White House, 2025), X, https://www.whitehouse.gov/wp-

content/uploads/2025/07/Americas-AI-Action-Plan.pdf 41 Shelly Palmer, “America’s AI Action Plan: What You Need to Know,” Shelly Palmer, July 24, 2025, https://shellypalmer.

com/2025/07/americas-ai-action-plan-what-you-need-to-know/

240 AI TOOLS, NOT GODS

42 Ministry of Foreign Affairs of the People’s Republic of China,

“Global AI Governance Action Plan,” July 26, 2025, https://www.

mfa.gov.cn/eng/xw/zyxw/202507/t20250729_11679232.html 43 Atlantic Council, “Reading between the lines of the dueling US

and Chinese AI action plans,” New Atlanticist (blog), August

7, 2025, https://www.atlanticcouncil.org/blogs/new-atlanticist/

reading-between-the-lines-of-the-dueling-us-and-chinese-ai-action-

plans/

44 Paul Scharre, Four Battlegrounds: Power in the Age of Artificial

Intelligence (New York: W. W. Norton & Company, 2023), 45,

https://wwnorton.com/books/9780393866865 45 Mary Ann Azevedo, “Microsoft to spend $80 billion in FY’25

on data centers for AI,” TechCrunch, https://techcrunch.

com/2025/01/03/microsoft-to-spend-80-billion-in-fy25-on-data-

centers-for-ai/

46 Emma Strubell, Ananya Ganesh, and Andrew McCallum,

“Energy and Policy Considerations for Deep Learning in NLP,” in

Proceedings of the 57th Annual Conference of the Association for

Computational Linguistics (Florence, Italy, July 2019), 3645–50,

https://doi.org/10.18653/v1/P19-1355

47 International Energy Agency (IEA), Energy and AI (Paris: IEA,

2025), https://www.iea.org/reports/energy-and-ai 48 Rebecca Leppert, “What we know about energy use at U.S. data centers amid the AI boom,” Pew Research Center, October 24,

2025, https://www.pewresearch.org/short-reads/2025/10/24/what-

we-know-about-energy-use-at-us-data-centers-amid-the-ai-boom/ 49 Reuters, “Most coal-fired power plants will delay retirement to feed AI boom, energy secretary says,” Mining Weekly, September

26, 2025, https://www.miningweekly.com/article/most-coal-

fired-power-plants-will-delay-retirement-to-feed-ai-boom-energy-

secretary-says-2025-09-26

50 Julia Musto, “Three Mile Island nuclear plant, site of

worst US reactor accident, to reopen for Microsoft,” The

Independent, September 20, 2024, https://www.independent.

co.uk/climate-change/microsoft-three-mile-island-nuclear-

pennsylvania-b2616314.html.

51 Eoin Burke-Kennedy, “Data centres now account for 21% of all

electricity consumption,” Irish Times, https://www.irishtimes.com/

business/2024/07/23/electricity-consumption-by-data-centres-

rises-to-21-eclipsing-urban-households/

52 Zolan Kanno-Youngs, Madeleine Ngo and Don Clark, “Intel

Receives $8.5 Billion in Grants to Build Chip Plants,” The New

Endnotes 241

York Times, https://www.nytimes.com/2024/03/20/us/politics/

chips-act-grant-intel.html

53 David Shepardson, “US finalizes $6.6 billion chips award for

TSMC ahead of Trump return,” Reuters, https://www.reuters.

com/technology/us-finalizes-66-billion-chips-award-tsmc-ahead-

trump-return-2024-11-15/

54 Habgood-Coote, J. Deepfakes and the epistemic apocalypse.

Synthese 201, 103 (2023). https://doi.org/10.1007/s11229-023-

04097-3

55 Robert Chesney and Danielle Keats Citron, “Deep Fakes: A

Looming Challenge for Privacy, Democracy, and National

Security,” California Law Review 107 (2019): 1753, https://ssrn.

com/abstract=3213954

56 Rob Toews, “Deepfakes Are Going to Wreak Havoc on Society.

We Are Not Prepared,” Forbes (May 25, 2020), https://www.

forbes.com/sites/robtoews/2020/05/25/deepfakes-are-going-to-

wreak-havoc-on-society-we-are-not-prepared/ 57 Lauren Goode, “Welcome to the age of paranoia as deepfakes and scams abound,” Wired.com, https://arstechnica.com/ai/2025/05/

welcome-to-the-age-of-paranoia-as-deepfakes-and-scams-abound/ 58 Matthew Leake, “Are fears about online misinformation in the US election overblown? The evidence suggests they might be,”

Reuters Institute, https://reutersinstitute.politics.ox.ac.uk/news/

are-fears-about-online-misinformation-us-election-overblown-

evidence-suggests-they-might-be

59 Henry Ajder et al., “The State of Deepfakes: Landscape, Threats,

and Impact,” Deeptrace, September 2019, https://docslib.org/

doc/12559428/the-state-of-deepfakes-landscape-threats-and-

impact-henry-ajder-giorgio-patrini-francesco-cavalli-and-laurence-

cullen-september-2019

60 California Legislature, Assembly Bill No. 730 (2019–2020

Reg. Sess.): Elections: deceptive audio or visual media, https://

leginfo.legislature.ca.gov/faces/billTextClient.xhtml?bill_

id=201920200AB730.

61 Texas Legislature, Senate Bill No. 751 (86th Leg., Regular

Session): Relating to the creation of a criminal offense for

fabricating a deceptive video with intent to influence the

outcome of an election, enacted June 14, 2019 (effective

September 1, 2019), https://capitol.texas.gov/BillLookup/History.

aspx?LegSess=86R&Bill=SB751

62 House Permanent Select Committee on Intelligence, “National

Security Challenges of Artificial Intelligence, Manipulated

Media, and ‘Deepfakes’,” hearing, 116th Cong. (June 13, 242 AI TOOLS, NOT GODS

2019), U.S. House of Representatives, 1100 Longworth House

Office Building, Washington, D.C., https://www.congress.gov/

event/116th-congress/house-event/109620

63 Defense Advanced Research Projects Agency, MediFor: Media

Forensics, DARPA, https://www.darpa.mil/research/programs/

media-forensics

64 Defense Advanced Research Projects Agency, SemaFor: Semantic

Forensics, DARPA, https://www.darpa.mil/research/programs/

semantic-forensics

65 Curt Devine, Donie O’Sullivan and Sean Lyngaas, “A fake

recording of a candidate saying he’d rigged the election went viral.

Experts say it’s only the beginning,” CNN, https://edition.cnn.

com/2024/02/01/politics/election-deepfake-threats-invs/index.html 66 James M. Lindsay, “Election 2024: The Deepfake Threat to the 2024 Election,” Council on Foreign Relations, https://www.cfr.

org/blog/election-2024-deepfake-threat-2024-election 67 Shannon Bond, “How AI deepfakes polluted elections in 2024,”

NPR, https://www.npr.org/2024/12/21/nx-s1-5220301/deepfakes-

memes-artificial-intelligence-elections

68 Anupriya Datta and Maximilian Henning, “Irish election

deepfake scandal spotlights slow enforcement on AI fakes,”

Euractiv, October 24, 2025, https://www.euractiv.com/news/

irish-election-deepfake-scandal-spotlights-slow-enforcement-on-ai-

fakes/

69 Mike Corder and Molly Quell, “Wilders, Timmermans are

among the leaders of the key parties in Dutch election,” 10tv.

com, October 28, 2025, https://www.10tv.com/article/syndication/

associatedpress/wilders-timmermans-are-among-the-leaders-of-

the-key-parties-in-dutch-election/616-5c83cae5-d1d0-4db6-9f08-

e6435ce1d6c8

70 Rebecca Schneid and Andrew R. Chow, “How Trump’s Use of AI

Videos Is Changing His Political Playbook,” Time, October 21,

2025, https://time.com/7327317/trump-ai-video-political-weapon/ 71 Brittany Shepherd and Monica Madden, “Gavin Newsom Is Gambling on New Rules: ‘Trump 2.0’,” ABC News, November 2,

2025, https://abcnews.go.com/US/gavin-newsom-gambling-new-

rules-trump-20/story?id=126900405

72 Matthew Berlin and Natasha Weis, “California Enacts

Comprehensive AI Safety Law: What AI Developers Need to

Know,” AFS Law, October 24, 2025, https://www.afslaw.com/

perspectives/ai-law-blog/california-enacts-comprehensive-ai-

safety-law-what-ai-developers-need-know

Endnotes 243

73 Loreben Tuquero, “Trump’s White House is testing the limits

of AI-driven political messaging,” Poynter, October 28, 2025,

https://www.poynter.org/fact-checking/2025/trump-white-house-

ai-political-messaging/

74 GovTech, “New York City Department of Education Bans

ChatGPT,” GovTech (January 10, 2023), https://www.govtech.

com/education/k-12/new-york-city-department-of-education-bans-

chatgpt

75 Taylor Soper, “Seattle Public Schools bans ChatGPT; district

‘requires original thought and work from students’,” GeekWire,

https://www.geekwire.com/2023/seattle-public-schools-bans-

chatgpt-district-requires-original-thought-and-work-from-

students/

76 Sumner Park, “School Districts Blocking ChatGPT Amid Fears of

Cheating, Educators Weigh In on AI,” Fox Business, January 12,

2023, https://www.foxbusiness.com/technology/school-districts-

blocking-chatgpt-amid-fears-of-cheating-educators-weigh-in-on-ai 77 Turnitin, “Turnitin turns on AI writing detection capabilities for educators and institutions,” https://www.turnitin.co.uk/press/

turnitin-turns-on-ai-writing-detection-capabilities-for-educators-

and-institutions

78 Jonathan Pearlman, “Australian states block ChatGPT in schools

even as critics say ban is futile,” The Straits Times, https://www.

straitstimes.com/asia/australianz/australian-states-block-chatgpt-

in-schools-even-as-critics-say-ban-is-futile

79 Gregory Kestin, Kelly Miller, Anna Klales, Timothy Milbourne,

Gregorio Ponti, “AI Tutoring Outperforms Active Learning,”

Stanford, https://doi.org/10.21203/rs.3.rs-4243877/v1 80 Plato, Phaedrus, trans. Benjamin Jowett (Project Gutenberg, October 20, 2016), https://www.gutenberg.org/files/1636/1636-

h/1636-h.htm

81 Audrey Watters, “A Brief History of Calculators in the

Classroom,” https://hackeducation.com/2015/03/12/calculators 82 U.S. Senate. Committee on Commerce, Science, and Transportation. Video Game Violence. C-SPAN, 1999. Video,

2:37:16. https://www.c-span.org/program/senate-committee/video-

game-violence/129470

Chapter 5

1 Sam Altman, “Moore’s Law for Everything,” March 16, 2021,

https://moores.samaltman.com/

244 AI TOOLS, NOT GODS

2 Eric Schmidt, “The AI revolution is underhyped,” TED Talk,

2024, https://www.ted.com/talks/eric_schmidt_the_ai_revolution_

is_underhyped.

3 Ray Kurzweil, The Singularity Is Near: When Humans Transcend

Biology (New York: Viking/Penguin Books, September 2005),

https://www.penguinrandomhouse.com/books/291221/the-

singularity-is-near-by-ray-kurzweil/

4 Brad Evans and Chantal Meza, “The Techno-Theodicy: How

technology became the new religion,” University of Bath’s

research portal, July 31, 2022, https://researchportal.bath.ac.uk/

en/publications/the-techno-theodicy-how-technology-became-the-

new-religion

5 Wikipedia, s.v. “Prosperity theology,” https://en.wikipedia.org/

wiki/Prosperity_theology

6 Klaus Schwab, The Fourth Industrial Revolution (Geneva: World

Economic Forum, 2016), https://www3.weforum.org/docs/WEF_

The_Fourth_Industrial_Revolution.pdf.

7 Satoshi Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash

System,” October 31, 2008, https://nakamotoinstitute.org/library/

bitcoin/

8 Giuseppe De Ruvo, “Algorithmic Objectivity as Ideology: Toward

a Critical Ethics of Digital Capitalism,” Topoi 44 (March 2025):

175–186, https://doi.org/10.1007/s11245-024-10117-9 9 Jon Henley. “Albania puts AI-created ‘minister’ in charge of public procurement.” The Guardian, September 11, 2025, https://

www.theguardian.com/world/2025/sep/11/albania-diella-ai-

minister-public-procurement

10 Jennifer L. Skeem and Christopher T. Lowenkamp, “Risk,

Race, and Recidivism: Predictive Bias and Disparate

Impact,” Criminology 54, no. 4 (2016): 680–712, https://doi.

org/10.1111/1745-9125.12123.

11 “Predpol,” Predictive Policing, Upturn, https://teamupturn.

gitbooks.io/predictive-policing/content/systems/predpol.html. 12 Wikipedia, s.v. “COMPAS (Software),” https://en.wikipedia.org/

wiki/COMPAS_(software)

13 Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner,

“Machine Bias: There’s Software Used Across the Country

to Predict Future Criminals. And It’s Biased against Blacks,”

ProPublica, May 23, 2016, https://www.propublica.org/article/

machine-bias-risk-assessments-in-criminal-sentencing. 14 Wikipedia, s.v. “Manifest Destiny,” https://en.wikipedia.org/wiki/

Manifest_destiny

Endnotes 245

15 Marc Andreessen, “Why AI Will Save the World,” Andreessen

Horowitz, June 6, 2023, https://a16z.com/ai-will-save-the-world/

16 National Security Commission on Artifi cial Intelligence, Final

Report, March 1, 2021, 752 pp., https://digital.library.unt.edu/

ark:/67531/metadc1851188/

17 Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and

Shmargaret Shmitchell, “On the Dangers of Stochastic Parrots:

Can Language Models Be Too Big? ” in Proceedings of the 2021

ACM Conference on Fairness, Accountability, and Transparency

(FAccT ‘21), March 3-10, 2021 (virtual), 610-623, https://doi.

org/10.1145/3442188.3445922

18 Steve Mollman, “OpenAI CEO Sam Altman says A.I. tools are

‘very good at doing tasks’ but terrible at doing whole jobs,”

Fortune, https://fortune.com/2023/06/08/openai-ceo-sam-altman-

ai-good-at-tasks-bad-at-jobs-for-now/

19 Melanie Mitchell, “Why AI is Harder Than We Think,” last

modifi ed April 28, 2021, arXiv:2104.12871, https://arxiv.org/

abs/2104.12871

20 Mark Zuckerberg, “Building AGI and Open Sourcing the

Journey,” Meta, January 18, 2024, https://www.meta.com/

superintelligence/

21 Om Malik, “Decoding Zuck’s Superintelligence Memo,” Om

Malik, July 30, 2025, https://om.co/2025/07/30/decoding-zucks-

superintelligence-memo/

22 Axios, “Zuckerberg, Chan Launch $1B Biohub to Build AI to

Cure All Diseases,” November 6, 2025, https://www.axios.

com/2025/11/06/zuckerberg-chan-biohub-ai-disease

23 Brian Merchant, “AI Generated Business: The Rise of AGI and

the Rush to Find a Working Revenue Model,” AI Now Institute,

December 2024, https://ainowinstitute.org/publications/ai-

generated-business

24 Eric Schmidt and Selina Xu, “Silicon Valley Is Drifting Out of

Touch With the Rest of America”, The New York Times, August

19, 2025, https://www.nytimes.com/2025/08/19/opinion/artifi cial-

general-intelligence-superintelligence.html

25 Michael Froman, “China, the United States, and the AI Race,”

Council on Foreign Relations, October 24, 2025, https://www.cfr.

org/article/china-united-states-and-ai-race

26 Alex Hanna and Emily M. Bender, “AI Causes Real Harm. Let’s

Focus on That over the End-of-Humanity Hype,” Scientifi c

American, May 31, 2023, https://www.scientifi camerican.com/

article/we-need-to-focus-on-ais-real-harms-not-imaginary-

existential-risks/

246 AI TOOLS, NOT GODS

27 Elon Musk, remarks at MIT Aeronautics and Astronautics

Centennial Symposium, October 24, 2014, Kresge Auditorium,

Massachusetts Institute of Technology, video and summary,

https://spacepolicyonline.com/meeting-summaries/elon-musk-

interviewed-at-mit-centennial-celebration-october-2014/ 28 Jim Collins, “Productive Paranoia,” Jim Collins, 2025, https://

www.jimcollins.com/concepts/productive-paranoia.html 29 Shoshana Zuboff, The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power (New York:

PublicAffairs, 2019), https://archive.org/details/zuboff-shoshana.-

the-age-of-surveillance-capitalism.-2019

30 Elon Musk and Neuralink, “An Integrated Brain-Machine

Interface Platform With Thousands of Channels,” Journal

of Medical Internet Research 21, no. 10 (October 31, 2019):

e16194, https://www.jmir.org/2019/10/e16194/ 31 Sam Altman, “Planning for AGI and beyond,” OpenAI, February 24, 2023, https://openai.com/index/planning-for-agi-and-beyond/ 32 OpenAI, “About,” OpenAI, https://openai.com/about/ 33 OpenAI, “Our structure,” OpenAI, October 28, 2025, https://

openai.com/our-structure/

34 Russell Brandom, “OpenAI completes its for-profit

recapitalization,” TechCrunch, October 28, 2025, https://

techcrunch.com/2025/10/28/openai-completes-its-for-profit-

recapitalization/

35 OpenAI, “Introducing ChatGPT,” OpenAI, November 30, 2022,

https://openai.com/index/chatgpt/

36 OpenAI, “Preparedness Framework,” OpenAI, December 18,

2023, https://openai.com/safety/preparedness. 37 Michael Feffer, Anusha Sinha, Wesley Hanwen Deng, Zachary C.

Lipton & Hoda Heidari, Red-Teaming for Generative AI: Silver

Bullet or Security Theater?, arXiv, Jan 29 2024, https://arxiv.org/

abs/2401.15897

38 Karen Hao, Empire of AI (Allen Lane, 2025), https://

www.penguin.co.uk/books/460331/empire-of-ai-by-hao-

karen/9780241678923

39 The Nobel Prize in Chemistry 2024, NobelPrize.org, https://www.

nobelprize.org/prizes/chemistry/2024/summary/ 40 Daniela Amodei (Anthropic), “Constitutional AI,” Stanford eCorner (video transcript), https://ecorner.stanford.edu/clips/

constitutional-ai/

41 Yuntao Bai et al., Constitutional AI: Harmlessness from AI

Feedback, Anthropic, December 15, 2022, arXiv preprint, https://

arxiv.org/abs/2212.08073

Endnotes 247

42 Dario Amodei, “Core Views on AI Safety: When, Why, What, and

How,” Anthropic, April 2023, https://www.anthropic.com/news/

core-views-on-ai-safety.

43 Yoshua Bengio, “AI for Social Good” (blog series/resource),

YoshuaBengio.org, 2021. https://yoshuabengio.org/category/ai-

for-social-good/

44 Future of Life Institute, “Pause Giant AI Experiments: An

Open Letter,” March 22, 2023, Future of Life Institute, https://

futureoflife.org/open-letter/pause-giant-ai-experiments/ 45 U.S. House Committee on Oversight and Accountability, “Oversight of A.I.: Rules for Artificial Intelligence” (hearing

before the Subcommittee on Privacy, Technology, and the Law,

Senate Judiciary Committee, May 16, 2023), Gibson Dunn

public policy alert (June 6, 2023), https://www.gibsondunn.com/

wp-content/uploads/2023/06/oversight-of-ai-rules-for-artificial-

intelligence-and-artificial-intelligence-in-government-hearings.pdf 46 Brendan Bordelon, “The law firm acting as OpenAI’s sherpa in Washington,” POLITICO Pro, September 12, 2023, https://

subscriber.politicopro.com/article/2023/09/the-law-firm-acting-as-

openais-sherpa-in-washington-00115205

47 Irish Council for Civil Liberties, “Scientists call on the President

of the European Commission to retract AI hype statement,”

*Irish Council for Civil Liberties*, November 10, 2025, https://

www.iccl.ie/press-release/scientists-call-on-the-president-of-the-

european-commission-to-retract-ai-hype-statement/ 48 Ursula von der Leyen, “Speech by President von der Leyen at the Annual EU Budget Conference 2025,” European Commission,

May 19, 2025, https://ec.europa.eu/commission/presscorner/

detail/en/speech_25_1284

49 Will Knight, “Many Top AI Researchers Get Financial Backing

From Big Tech,” WIRED, October 4 2020, https://www.wired.

com/story/top-ai-researchers-financial-backing-big-tech/ 50 Marc Andreessen, “Why AI Will Save the World,” Andreessen Horowitz, June 6, 2023, https://a16z.com/ai-will-save-the-world/ 51 Chris Anderson, TED Talks: The Official TED Guide to Public Speaking (Boston: Houghton Mifflin Harcourt, 2016), https://

archive.org/details/tedtalksofficial0000ande_f3k1 52 Gary Shapiro, The Comeback: How Innovation Will Restore the American Dream (New York: Beaufort Books, January 6,

2011), https://archive.org/details/comeback0000shap members.

iedconline.org+15

53 Clayton M. Christensen, The Innovator’s Dilemma: When

New Technologies Cause Great Firms to Fail (Boston: Harvard 248 AI TOOLS, NOT GODS

Business School Press, 1997), https://archive.org/details/

innovatorsdilem000chri

54 Brad Stone, The Upstarts: How Uber, Airbnb, and the Killer

Companies of the New Silicon Valley Are Changing the World

(New York: Little, Brown and Company, 2017), https://archive.

org/details/upstartshowubera0000ston

55 Leigh Gallagher, The Airbnb Story: How Three Ordinary Guys

Disrupted an Industry, Made Billions, and Created Plenty of

Controversy (Boston: Houghton Mifflin Harcourt, 2017), https://

archive.org/details/airbnbstoryhowth0000gall 56 Steven Levy, In the Plex: How Google Thinks, Works, and Shapes Our Lives (New York: Simon & Schuster, 2011), https://www.

mondotheque.be/wiki/images/2/2b/Levy_Steve_In_The_Plex.pdf. 57 Roger McNamee, Zucked: Waking Up to the Facebook Catastrophe (New York: Penguin Press, 2019), https://archive.

org/details/zucked-waking-up-to-the-facebook-catastrophe-2019-

roger-mc-namee

58 Eric Topol, Deep Medicine: How Artificial Intelligence Can Make

Healthcare Human Again (New York: Basic Books, 2019), https://

archive.org/details/theaspen-Deep_Medicine_-_How_Artificial_

Intelligence_Can_Make_Health_Care_Human_Again 59 Cathy O’Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (New York:

Crown Publishers, 2016), https://archive.org/details/weapons-of-

math-destruction_202411

60 Alec MacGillis, Fulfillment: Winning and Losing in One-Click

America (New York: Farrar, Straus and Giroux, 2021), https://

archive.org/details/fulfillmentwinni0000macg (archive.org) 61 Sarah Brayne, Predict and Surveil: Data, Discretion, and the Future of Policing (New York: Oxford University Press, 2021),

https://academic.oup.com/book/33466 (Oxford Academic) 62 Ben Williamson, Big Data in Education: The Digital Future of Learning, Policy and Practice (London: SAGE Publications,

2017), https://us.sagepub.com/en-us/nam/big-data-in-education/

book249086

Chapter 6

1 Wikipedia, s.v. “Scapegoat,” https://en.wikipedia.org/wiki/

Scapegoat

2 David H. Autor, “Why Are There Still So Many Jobs? The History

and Future of Workplace Automation,” Journal of Economic

Perspectives 29, no. 3 (2015): 3–30, https://www.aeaweb.org/

articles?id=10.1257/jep.29.3.3

Endnotes 249

3 Robert D. Putnam, Bowling Alone: The Collapse and Revival

of American Community (New York: Simon & Schuster, 2000),

https://www.simonandschuster.com/books/Bowling-Alone-

Revised-and-Updated/Robert-D-Putnam/9780743219037 4 Martin Gilens and Benjamin I. Page, “Testing Theories of American Politics: Elites, Interest Groups, and Average Citizens,”

Perspectives on Politics 12, no. 3 (2014): 564–581, https://www.

cambridge.org/core/journals/perspectives-on-politics/article/

testing-theories-of-american-politics-elites-interest-groups-and-

average-citizens/62327F513959D0A304D4893B382B992B 5 Nick Bostrom, Superintelligence: Paths, Dangers, Strategies (Oxford: Oxford University Press, 2014), https://global.oup.com/

academic/product/superintelligence-9780198739838 6 Stanley Cohen, Folk Devils and Moral Panics (London: MacGibbon and Kee, 1972), https://www.routledge.com/Folk-

Devils-and-Moral-Panics/Cohen/p/book/9780415610162 7 Carl Benedikt Frey and Michael A. Osborne, “The Future of Employment: How Susceptible Are Jobs to Computerisation?”

Technological Forecasting and Social Change 114 (2017): 254–

280, https://doi.org/10.1016/j.techfore.2016.08.019 8 Jack Kelly, “Goldman Sachs Predicts 300 Million Jobs Will Be Lost Or Degraded By Artificial Intelligence,” Forbes, March

31, 2023, https://www.forbes.com/sites/jackkelly/2023/03/31/

goldman-sachs-predicts-300-million-jobs-will-be-lost-or-degraded-

by-artificial-intelligence/

9 Emily M. Bender and Alex Hanna, “The AI Con: How to Fight

Big Tech’s Hype and Create the Future We Want,” The Con,

https://thecon.ai/

10 Joel Mokyr, Chris Vickers, and Nicolas L. Ziebarth, “The History

of Technological Anxiety and the Future of Economic Growth: Is

This Time Different?” Journal of Economic Perspectives 29, no.

3 (2015): 31–50, https://www.aeaweb.org/articles?id=10.1257/

jep.29.3.31

11 David H. Autor, Frank Levy, and Richard J. Murnane, “The

Skill Content of Recent Technological Change: An Empirical

Exploration,” Quarterly Journal of Economics 118, no. 4 (2003):

1279–1333, https://doi.org/10.1162/003355303322552801 12 Simon Sharwood, “AWS CEO says using AI to replace junior staff is ‘Dumbest thing I’ve ever heard’,” The Register, August 21,

2025, https://www.theregister.com/2025/08/21/aws_ceo_entry_

level_jobs_opinion/

250 AI TOOLS, NOT GODS

13 Daron Acemoglu and Pascual Restrepo, “Robots and Jobs:

Evidence from US Labor Markets,” Journal of Political Economy

128, no. 6 (2020): 2188–2244, https://doi.org/10.1086/705716 14 Chris Mills Rodrigo, “How Major Labor Unions are Positioning on AI,” Tech Policy Press, October 28, 2025, https://techpolicy.

press/how-major-labor-unions-are-positioning-on-ai 15 Kevin Roose, “An A.I.-Generated Picture Won an Art Prize.

Artists Aren’t Happy,” New York Times, September 2, 2022,

https://www.nytimes.com/2022/09/02/technology/ai-artificial-

intelligence-artists.html

16 Walter Benjamin, “The Work of Art in the Age of Mechanical

Reproduction,” in Illuminations, ed. Hannah Arendt (New York:

Schocken Books, 1968), https://web.mit.edu/allanmc/www/

benjamin.pdf

17 Susan Sontag, On Photography (New York: Farrar, Straus and

Giroux, 1977), https://www.amazon.com/Photography-Susan-

Sontag/dp/0312420099

18 Glyn Moody, “European Publishers Council Stays True to the

Tired Old Trope about ‘Copyright Theft’,” Walled Culture, May

28, 2025, https://walledculture.org/european-publishers-council-

stays-true-to-the-tired-old-trope-about-copyright-theft/ 19 Carys J. Craig, The AI-Copyright Trap (Osgoode Legal Studies Research Paper No. 4905118, July 15, 2024), https://ssrn.com/

abstract=4905118

20 Carys J. Craig, The AI-Copyright Trap (Osgoode Legal Studies

Research Paper No. 4905118, July 15, 2024), https://ssrn.com/

abstract=4905118

21 Arvind Narayanan and Sayash Kapoor, AI Snake Oil: What

Artificial Intelligence Can Do, What It Can’t, and How to Tell

the Difference (Princeton, NJ: Princeton University Press, 2024),

https://press.princeton.edu/books/hardcover/9780691249131/ai-

snake-oil

22 Pamela Samuelson, “Does Using In-Copyright Works as Training

Data Infringe?” Wolters Kluwer Copyright Blog, July 18, 2025,

https://legalblogs.wolterskluwer.com/copyright-blog/does-using-in-

copyright-works-as-training-data-infringe/

23 Mark A. Lemley and Bryan Casey, Fair Learning (Texas Law

Review, forthcoming 2021), https://papers.ssrn.com/sol3/papers.

cfm?abstract_id=3528447

24 Nancy S. Baym, Playing to the Crowd: Musicians, Audiences, and

the Intimate Work of Connection (New York: NYU Press, 2018),

https://nyupress.org/9781479821587/playing-to-the-crowd/

Endnotes 251

25 Andres Guadamuz, “How AI is breaking traditional remuneration

models,” TechnoLlama, July 4, 2025, https://www.technollama.

co.uk/how-ai-is-breaking-traditional-remuneration-models 26 Afzana Anwer, “Economics of Streaming & the Rise of the Music Artists’ Rights and Compensation,” IP Business Academy,

February 24, 2025, https://ipbusinessacademy.org/economics-of-

streaming-the-rise-of-the-music-artists-rights-and-compensation 27 Pamela Samuelson, “Does Using In-Copyright Works as Training Data Infringe?” Wolters Kluwer Copyright Blog, July 18, 2025,

https://legalblogs.wolterskluwer.com/copyright-blog/does-using-in-

copyright-works-as-training-data-infringe/

28 Andres Guadamuz, “How AI is breaking traditional remuneration

models,” TechnoLlama, July 4, 2025, https://www.technollama.

co.uk/how-ai-is-breaking-traditional-remuneration-models 29 Caroline De Cock, “Copyright, Creativity, and Generative AI: The Research Sector’s Missing Seat at the Policy Table,” June 11,

2025, information labs, https://informationlabs.org/copyright-

creativity-and-generative-ai-the-research-sectors-missing-seat-at-

the-policy-table/

30 Wikipedia, s.v. “Dead Internet theory,” https://en.wikipedia.org/

wiki/Dead_Internet_theory

31 Isobel Owusu-Sarpong, “What Is Dead Internet Theory and

Could It Change the Web?,” Tech.co, May 22, 2024, https://tech.

co/news/what-is-dead-internet-theory

32 Jake Renzella and Vlada Rozova, “‘The Dead Internet Theory’

Makes Eerie Claims About an AI-Run Web. The Truth Is

More Sinister,” The Conversation, April 8, 2025, https://

theconversation.com/the-dead-internet-theory-makes-eerie-claims-

about-an-ai-run-web-the-truth-is-more-sinister-229609 33 Dani Di Placido, “Facebook’s Surreal ‘Shrimp Jesus’ Trend, Explained,” Forbes, April 28, 2024, https://www.forbes.com/sites/

danidiplacido/2024/04/28/facebooks-surreal-shrimp-jesus-trend-

explained/

34 Dana Srebrnik, “The Dead Internet Theory and the Silent Surge of

Bots,” Wix Blog, September 18, 2024, https://www.wix.com/blog/

dead-internet-theory j

35 Szymon Kubala, “AI and the Dead Internet Theory: Are We

Heading in That Direction?,” Webmakers Expert, August 20,

2024, https://webmakers.expert/en/blog/ai-and-the-dead-internet-

theory

36 Julian Horsey, “Are You Talking to Bots? The Alarming Truth

About the Dead Internet Theory,” Geeky Gadgets, July, 1 2025

https://www.geeky-gadgets.com/dead-internet-theory-explained/

252 AI TOOLS, NOT GODS

37 Stuart Russell, Human Compatible: Artificial Intelligence and

the Problem of Control (New York: Viking, 2019), https://www.

penguinrandomhouse.com/books/566677/human-compatible-by-

stuart-russell/

38 Cathy O’Neil, Weapons of Math Destruction: How Big Data

Increases Inequality and Threatens Democracy (New York:

Crown Publishers, 2016), https://dl.acm.org/doi/10.5555/3002861 39 Eliezer Yudkowsky, “Artificial Intelligence as a Positive and Negative Factor in Global Risk,” in Global Catastrophic Risks,

ed. Nick Bostrom and Milan M. Ćirković (Oxford: Oxford

University Press, 2008), https://global.oup.com/academic/product/

global-catastrophic-risks-9780199606504

40 Toby Ord, The Precipice: Existential Risk and the Future of

Humanity (New York: Hachette Books, 2020), https://www.

hachettebookgroup.com/titles/toby-ord/the-precipice/9780316484

893/?lens=grand-central-publishing

41 Future of Life Institute, “Pause Giant AI Experiments: An Open

Letter,” March 22, 2023, https://futureoflife.org/open-letter/pause-

giant-ai-experiments/

42 Nick Bostrom, “The Alignment Problem,” in Superintelligence:

Paths, Dangers, Strategies (Oxford: Oxford University Press,

2014), 127–145, https://global.oup.com/academic/product/

superintelligence-9780198739838

Chapter 7

1 Claudio Novelli, Luciano Floridi, Giovanni Sartor, AI as Legal

Persons: Past, Patterns, and Prospects, https://a-mcc.eu/wp-

content/uploads/2024/11/ssrn-5032265.pdf

2 Alex Hanna and Emily M. Bender, AI Causes Real Harm. Let’s

Focus on That over the End-of-Humanity Hype, Scientific

American, https://www.scientificamerican.com/article/we-need-to-

focus-on-ais-real-harms-not-imaginary-existential-risks/ 3 European Parliament Committee on Legal Affairs (JURI), Draft Report with Recommendations to the Commission on Civil Law

Rules on Robotics (PE 582.443v01-00, JURI-PR 582443), https://

www.europarl.europa.eu/doceo/document/JURI-PR-582443_

EN.pdf

4 Alex Hern, Give robots ‘personhood’ status, EU committee

argues, The Guardian, https://www.theguardian.com/

technology/2017/jan/12/give-robots-personhood-status-eu-

committee-argues

5 European Parliament Press Release, Robots: Legal Affairs

Committee calls for EU-wide rules, https://www.europarl.europa.

Endnotes 253

eu/news/en/press-room/20170110IPR57613/robots-legal-affairs-

committee-calls-for-eu-wide-rules

6 Jeffrey Dastin, Insight - Amazon scraps secret AI recruiting tool

that showed bias against women, Reuters, https://www.reuters.

com/article/world/insight-amazon-scraps-secret-ai-recruiting-tool-

that-showed-bias-against-women-idUSKCN1MK0AG/ 7 Charlotte Lytton, AI hiring tools may be filtering out the best job applicants, BBC, https://www.bbc.com/worklife/article/20240214-

ai-recruiting-hiring-software-bias-discrimination 8 Edmund L. Andrews, How Flawed Data Aggravates Inequality in Credit, Stanford, https://hai.stanford.edu/news/how-flawed-data-

aggravates-inequality-credit

9 Lee McIntyre, Post-Truth (Cambridge, MA: MIT Press, 2018),

https://mitpress.mit.edu/9780262535045/post-truth/ 10 Julien Kiesse Bahangulu and Louis Owusu-Berko, “Algorithmic Bias, Data Ethics, and Governance: Ensuring Fairness,

Transparency and Compliance in AI-Powered Business Analytics

Applications,” World Journal of Advanced Research and

Reviews 25, no. 2 (2025): 1746–63, https://doi.org/10.30574/

wjarr.2025.25.2.0571

11 Matthew Chiu, “Path Dependency in Policymaking: A Double-

Edged Sword,” Protopia Group, April 19, 2024, https://www.

protopiagroup.org/analysis/path-dependency-in-policymaking-a-

double-edged-sword

12 Jacob Robbins, “AI startups grabbed a third of global VC dollars

in 2024,” Pitchbook, https://pitchbook.com/news/articles/ai-

startups-grabbed-a-third-of-global-vc-dollars-in-2024 13 Sarah Perez, “Character.AI, the a16z-backed chatbot startup, tops 1.7M installs in first week,” TechCrunch, https://techcrunch.

com/2023/05/31/character-ai-the-a16z-backed-chatbot-startup-

tops-1-7m-installs-in-first-week/

14 Kenrick Kai, “Google hires top talent from startup Character.

AI, signs licensing deal,” Reuters, https://www.reuters.com/

technology/artificial-intelligence/google-hires-characterai-

cofounders-licenses-its-models-information-reports-2024-08-02/ 15 Amazon staff, “Amazon concludes $4 billion investment in Anthropic,” https://www.aboutamazon.com/news/company-news/

amazon-anthropic-ai-investment

16 Naomi Nix, Cat Zakrzewski and Gerrit De Vynck, “Silicon Valley

is pricing academics out of AI research,” The Washington Post,

https://www.washingtonpost.com/technology/2024/03/10/big-

tech-companies-ai-research/

254 AI TOOLS, NOT GODS

17 Naomi Nix, Cat Zakrzewski, and Gerrit De Vynck, “Silicon

Valley Is Pricing Academics Out of AI Research,” The Washington

Post, March 10, 2024, https://www.washingtonpost.com/

technology/2024/03/10/big-tech-companies-ai-research/ 18 Nicklas Berild Lundblad, “Unpredictable Patterns #115: Questioning our assumptions,” Unpredictable Patterns (blog),

April 18, 2025, https://unpredictablepatterns.substack.com/p/

unpredictable-patterns-115-questioning

19 Fabrizio Gilardi and Fabio Wasserfallen, “The Politics of Policy

Diffusion,” European Journal of Political Research (2019), https://

doi.org/10.1111/1475-6765.12326

20 Wikipedia, s.v. “Nirvana fallacy”, https://en.wikipedia.org/wiki/

Nirvana_fallacy

21 Audrey Watters, “The History of the Future of Calculators

in Schools,” Hack Education, March 12, 2015, https://

hackeducation.com/2015/03/12/calculators

22 Elana Klein, “ Tech in the Classroom: A History of Hype and

Hysteria,” Wired, August 18, 2025, https://www.wired.com/story/

photo-essay-school-tech-hysteria/

23 Richard A. Clarke and Robert K. Knake, Cyber War: The Next

Threat to National Security and What to Do About It (New York:

Ecco, 2010), https://www.harpercollins.com/products/cyber-war-

richard-a-clarkerobertknake

24 Leon E. Panetta, “Defending the Nation from Cyber Attack,”

Business Executives for National Security, October 11, 2012,

https://nsarchive.gwu.edu/sites/default/files/documents/2700165/

Document-78.pdf

25 Committee on the Judiciary and Select Subcommittee on the

Weaponization of the Federal Government, The Weaponization

of CISA: How a “Cybersecurity” Agency Colluded with Big Tech

and “Disinformation” Partners to Censor Americans: Interim

Staff Report of the Committee on the Judiciary and the Select

Subcommittee on the Weaponization of the Federal Government

(U.S. House of Representatives, June 26, 2023), https://judiciary.

house.gov/sites/evo-subsites/republicans-judiciary.house.gov/files/

evo-media-document/cisa-staff-report6-26-23.pdf 26 Bruce Schneier, Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World (New York: W. W. Norton

& Company, 2015), https://www.schneier.com/books/data-and-

goliath/

27 P.W. Singer and Allan Friedman, Cybersecurity and Cyberwar:

What Everyone Needs to Know (Oxford: Oxford University Press,

Endnotes 255

2014), https://global.oup.com/academic/product/cybersecurity-

and-cyberwar-9780199918096

28 National Academy of Sciences, At the Nexus of Cybersecurity and

Public Policy: Some Basic Concepts and Issues (Washington, DC:

National Academies Press, 2014), https://nap.nationalacademies.

org/catalog/18749/at-the-nexus-of-cybersecurity-and-public-

policy-some-basic

29 Langdon Winner, “Do Artifacts Have Politics?” Daedalus 109,

no. 1 (1980): 121–136, https://www.jstor.org/stable/20024652 30 Frank Pasquale, The Black Box Society: The Secret Algorithms That Control Money and Information (Cambridge, MA:

Harvard University Press, 2015), https://www.hup.harvard.edu/

books/9780674970847

31 Daniel Boffey, “EU eyes temporary ban on facial recognition in

public places,” The Guardian, January 17, 2020, https://www.

theguardian.com/technology/2020/jan/17/eu-eyes-temporary-ban-

on-facial-recognition-in-public-places

32 Thomas Macaulay, “Oh great, the EU has ditched its facial

recognition ban,” The Next Web, February 12, 2020, https://

thenextweb.com/news/oh-great-the-eu-has-ditched-its-facial-

recognition-ban

33 European Parliament, “Resolution on Artificial Intelligence in

Criminal Law and Its Use by the Police and Judicial Authorities

in Criminal Matters,” October 6, 2021, https://www.europarl.

europa.eu/doceo/document/TA-9-2021-0405_EN.html 34 Future of Life Institute, “Pause Giant AI Experiments: An Open Letter,” March 22 2023, https://futureoflife.org/open-letter/pause-

giant-ai-experiments/

35 Geoffrey Gertz and Justin Sherman, “Beyond Bans: Expanding

the Policy Options for Tech-Security Threats,” Lawfare, June

25, 2025, https://www.lawfaremedia.org/article/beyond-bans--

expanding-the-policy-options-for-tech-security-threats 36 Hal Abelson et al., “Bugs in Our Pockets: The Risks of Client-Side Scanning,” October 15, 2021, arXiv, https://doi.org/10.48550/

arXiv.2110.07450

37 Karen Levy, “The Contexts of Control: Information, Power, and

Truck-Driving Work,” Information Society 31, no. 2 (2015):

160–174, https://doi.org/10.1080/01972243.2015.998105 38 Tarleton Gillespie, Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions That Shape Social

Media (New Haven: Yale University Press, 2018), https://doi.

org/10.12987/9780300235029

256 AI TOOLS, NOT GODS

39 Solon Barocas, Moritz Hardt, and Arvind Narayanan, Fairness

and Machine Learning: Limitations and Opportunities

(Cambridge, MA: MIT Press, 2023), https://fairmlbook.org/ 40 Jillian C. York, Silicon Values: The Future of Free Speech Under Surveillance Capitalism (London: Verso Books, 2021), https://

www.versobooks.com/products/882-silicon-values 41 Virginia Eubanks, Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor (New York: St. Martin’s

Press, 2018), https://us.macmillan.com/books/9781250074317/

automatinginequality

42 Pauline T. Kim, “Data-Driven Discrimination at Work,” William

& Mary Law Review 58, no. 3 (2017): 857–936, https://

scholarship.law.wm.edu/wmlr/vol58/iss3/4/

43 danah boyd, “The Politics of ‘Real Names’,” Communications

of the ACM 55, no. 8 (2012): 29–31, https://doi.

org/10.1145/2240236.2240247

44 Svea Windwehr, Jillian C. York, and Christoph Schmon, “Just

Banning Minors From Social Media Is Not Protecting Them,”

Electronic Frontier Foundation, July 28, 2025, https://www.eff.

org/deeplinks/2025/07/just-banning-minors-social-media-not-

protecting-them

45 Sonia Livingstone, “iGen: Why Today’s Super-Connected Kids

Are Growing Up Less Rebellious, More Tolerant, Less Happy,”

Journal of Children and Media 12, no. 1 (2018): 118–123, https://

doi.org/10.1080/17482798.2017.1417091

46 Electronic Frontier Foundation, “Age Verification Mandates

Would Undermine Anonymous Speech and Require Extensive

Data Collection,” March 2023, https://www.eff.org/

deeplinks/2023/03/age-verification-mandates-would-undermine-

anonymous-speech-and-require-extensive

47 Jeff Kosseff, The Twenty-Six Words That Created the Internet

(Ithaca: Cornell University Press, 2019), https://www.cornellpress.

cornell.edu/book/9781501714412/the-twenty-six-words-that-

created-the-internet/

48 Rebecca MacKinnon, Consent of the Networked: The Worldwide

Struggle for Internet Freedom (New York: Basic Books, 2012),

https://consentofthenetworked.com/

49 Sarah T. Roberts, Behind the Screen: Content Moderation in the

Shadows of Social Media (New Haven: Yale University Press,

2019), https://www.jstor.org/stable/j.ctvhrcz0v 50 Joanna Bryson, “AI Regulation and the Limits of Transparency,”

AXA Research Fund Insight (July 12, 2021), https://www.axa.

com/en/insights/ai-regulation-and-the-limits-of-transparency.

Endnotes 257

51 “Predpol,” Predictive Policing, Upturn, https://teamupturn.

gitbooks.io/predictive-policing/content/systems/predpol.html. 52 Wikipedia, s.v. “COMPAS (Software),” https://en.wikipedia.org/

wiki/COMPAS_(software)

Chapter 8

1 Nick Lichtenberg, “Is AI a Bubble? Market Crash Sees $1 Trillion

Wiped From S&P 500 as OpenAI’s GPT-5 Release Flops and Sam

Altman’s ‘Bubble’ Comments Bite,” Fortune, August 24, 2025,

https://fortune.com/2025/08/24/is-ai-a-bubble-market-crash-gary-

marcus-openai-gpt5/

2 Robert Brauneis, “Copyright Law and New Technologies: A Long

and Complex Relationship,” Unfolding the Past (blog), Library of

Congress, May 18, 2017, https://blogs.loc.gov/copyright/2017/05/

copyright-law-and-new-technologies-a-long-and-complex-

relationship/

3 Wikipedia, s.v. “White-Smith Music Publishing Co. v. Apollo

Co.,” https://en.wikipedia.org/wiki/White-Smith_Music_

Publishing_Co._v._Apollo_Co.

4 The Copyright Act of 1909 (as amended and codified), Title

17 U.S.C., https://ipmall.law.unh.edu/sites/default/files/hosted_

resources/lipa/copyrights/CopyrightAct1909amendedcodified_.pdf 5 Zvi S. Rosen, “The Unoriginal Sin of Technology-Neutral Copyright,” Minnesota Law Review 100 (2016): 1495– 1558, https://scholarship.law.umn.edu/cgi/viewcontent.

cgi?article=1206&context=mlr

6 Imagining the Internet | Elon University. “1830s – 1860s:

Telegraph,” n.d. https://www.elon.edu/u/imagining/time-

capsule/150-years/back-1830-1860/

7 Tim Wu, “The Master Switch: The Rise and Fall of Information

Empires” (working paper, Columbia Law School, 2007),

https://scholarship.law.columbia.edu/cgi/viewcontent.

cgi?article=2462&context=faculty_scholarship 8 United States Congress, “Federal Criminal Code, Title 18, Part I, Chapter 63 & Chapter 113”, https://www.oas.org/juridico/

spanish/cyber/us_cyb_law_fed_crim_code.pdf 9 LII / Legal Information Institute. “Wire Fraud,” n.d. https://www.

law.cornell.edu/wex/wire_fraud

10 Investopedia. “Understanding Wire Fraud: Definition, Laws, and

Key Examples,” August 27, 2025. https://www.investopedia.com/

terms/w/wirefraud.asp

11 Valerie C. Brannon and Eric N. Holmes, Section 230: An

Overview, CRS Report R46751 (Washington, D.C.: Congressional 258 AI TOOLS, NOT GODS

Research Service, January 4 2024), https://www.congress.gov/crs-

product/R46751

12 Electronic Frontier Foundation. “Section 230,” n.d. https://www.

eff.org/issues/cda230

13 Electronic Frontier Foundation, “Section 230,” https://www.eff.

org/issues/cda230; see also “What you should know about Section

230, the rule that shaped today’s internet,” PBS News, December

2021, https://www.pbs.org/newshour/politics/what-you-should-

know-about-section-230-the-rule-that-shaped-todays-internet 14 European Parliament and Council, Directive 2000/31/EC of 8

June 2000 on certain legal aspects of information society services,

in particular electronic commerce, in the Internal Market, Official

Journal L 178 (17 July 2000): 1-16, https://eur-lex.europa.eu/eli/

dir/2000/31/oj/eng

15 Wikipedia, s.v. “The Electronic Commerce Directive (2000/31/

EC),” https://en.wikipedia.org/wiki/Electronic_Commerce_

Directive_2000

16 “House of Lords - Regulating in a Digital World - Select

Committee on Communications,” n.d. https://publications.

parliament.uk/pa/ld201719/ldselect/ldcomuni/299/29908.htm 17 CEPS, “Limited liability for the net? The Future of Europe’s E-Commerce Directive,” 2020, https://www.ceps.eu/ceps-

publications/limited-liability-net-future-europes-e-commerce-

directive/

18 Matthijs Maas, “AI is like...: A Literature Review of AI

Metaphors and Why They Matter for Policy,” Institute for Law

& AI, AI Foundations Report 2, October 2023, https://law-ai.org/

ai-policy-metaphors/

19 Britta C. Brugman, Christian Burgers, and Barbara Vis.

“Metaphorical Framing in Political Discourse through Words vs.

Concepts: A Meta-Analysis.” Language and Cognition 11, no. 1

(March 1, 2019): 41–65. https://doi.org/10.1017/langcog.2019.5 20 Centre for Trustworthy Technology, “Metaphors Matter: How Language Influences Technology and Society,” 2024, https://c4tt.

org/metaphors-matter-how-language-influences-technology-and-

society/

21 Keith Rasenberger, “Why Metaphors Matter in AI Law and

Policy,” The Regulatory Review, May 2, 2024, https://www.

theregreview.org/2024/05/02/rasenberger-why-metaphors-matter-

in-ai-law-and-policy/

22 Andres Guadamuz, “Artists file class-action lawsuit against

Stability AI, DeviantArt, and Midjourney,” TechnoLlama,

Endnotes 259

January 15, 2023, https://www.technollama.co.uk/artists-file-

class-action-lawsuit-against-stability-ai-deviantart-and-midjourney 23 Wikipedia, s.v. “Semantic gap,” https://en.wikipedia.org/wiki/

Semantic_gap

24 Jeffrey A. Frankel, “Over-optimism in Forecasts by Official Budget

Agencies and Its Implications,” Oxford Review of Economic

Policy 27, no. 4 (Winter 2011): 536-562, https://www.hks.

harvard.edu/publications/over-optimism-forecasts-official-budget-

agencies-and-its-implications

25 Saul Hudson et al., “Policy failure and the policy-implementation

gap: can policy support programs help?” Policy Studies 40,

no. 1 (2019): 129-148, https://www.tandfonline.com/doi/

full/10.1080/25741292.2018.1540378

26 Landry Signé and Colette van der Ven, “Turning policy into

action: Overcoming policy failures and bridging implementation

gaps,” Brookings, February 28, 2024, https://www.brookings.edu/

articles/turning-policy-into-action-in-africa/

27 Thomas H. Beach, Yacine Y. Rezgui, Haijiang Li, and T. Kasim.

“A Rule-Based Semantic Approach for Automated Regulatory

Compliance in the Construction Sector.” Expert Systems With

Applications 42, no. 12 (March 6, 2015): 5219–31. https://doi.

org/10.1016/j.eswa.2015.02.029

28 Suzy E. Park, Technological Convergence: Regulatory, Digital

Privacy, and Data Security Issues, CRS Report R45746

(Washington, D.C.: Congressional Research Service, May

30, 2019), https://www.everycrsreport.com/files/20190530_

R45746_461fe45bf0ce4a3f25e8d1a507c308fee4d5bfd4.pdf 29 “Closing the ‘RegLag’ Between Prospective Regulated Activity and Regulation,” CLS Blue Sky Blog, October 16, 2020, https://

clsbluesky.law.columbia.edu/2020/10/16/closing-the-reglag-

between-prospective-regulated-activity-and-regulation/ 30 OECD, Case Studies on the Regulatory Challenges Raised by Innovation and the Regulatory Responses (Paris: OECD

Publishing, 2023), https://www.oecd.org/en/publications/case-

studies-on-the-regulatory-challenges-raised-by-innovation-and-

the-regulatory-responses_8fa190b5-en.html

31 Carlos F. Ceballos Ferroglio, Gustavo Ferro, and Áng el

Enrique Neder, “Regulatory Lag, Efficiency, and Performance:

Lessons from a Case Study,” Competition and Regulation

in Network Industries 25, no. 1 (2024): 43-66, https://doi.

org/10.1177/17835917241251811

260 AI TOOLS, NOT GODS

32 Emilio Kyprianou, “The Red Flag Act,” The Open University Law

School Blog, October 2 2023, https://law-school.open.ac.uk/blog/

red-flag-act

33 Winston J. Maxwell and Marc Bourreau, “Technology Neutrality

in Internet, Telecoms and Data Protection Regulation” (Computer

and Telecommunications Law Review, 2014), https://papers.ssrn.

com/sol3/papers.cfm?abstract_id=2529680

Chapter 9

1 Langdon Winner, Autonomous Technology: Technics-out-of-

Control as a Theme in Political Thought (Cambridge, MA:

MIT Press, 1977), https://mitpress.mit.edu/9780262230780/

autonomous-technology/

2 Helen Nissenbaum, “How Computer Systems Embody

Values,” Computer 34, no. 3 (2001): 120–119, https://doi.

org/10.1109/2.910905

3 Myra Cheng et al., “From Tools to Thieves: Measuring and

Understanding Public Perceptions of AI through Crowdsourced

Metaphors,” arXiv, January 31, 2025, https://arxiv.org/

html/2501.18045v1

4 Madhumita Murgia, interview of Ted Chiang, “Sci-fi writer

Ted Chiang: ‘The machines we have now are not conscious’,”

Financial Times, June 3–4, 2023, https://www.ft.com/content/

c1f6d948-3dde-405f-924c-09cc0dcf8c84

5 Melissa Heikkilä, Why it’ll be hard to tell if AI ever

becomes conscious, MIT Technology Review, https://www.

technologyreview.com/2023/10/17/1081818/why-itll-be-hard-to-

tell-if-ai-ever-becomes-conscious/?utm_source=LinkedIn 6 Kevin Roose, If A.I. Systems Become Conscious, Should They Have Rights?, The New York Times, https://www.nytimes.

com/2025/04/24/technology/ai-welfare-anthropic-claude.html 7 Ryan Calo, “Products Liability Law as a Way to Address AI Harms,” Brookings Institution, October 31, 2019, https://www.

brookings.edu/articles/products-liability-law-as-a-way-to-address-

ai-harms/

8 Wikipedia, s.v. “MacPherson v. Buick Motor Co.,” https://

en.wikipedia.org/wiki/MacPherson_v._Buick_Motor_Co. 9 W. Kerrel Murray, “Products Liability for Artificial Intelligence,”

Lawfare, May 1, 2024, https://www.lawfaremedia.org/article/

products-liability-for-artificial-intelligence

10 Ryan Calo, “Robotics and the Lessons of Cyberlaw,” California

Law Review 103, no. 3 (2015): 513–563, https://papers.ssrn.com/

sol3/papers.cfm?abstract_id=2402972

Endnotes 261

11 Michael J. M. L. W. Horton, “Artificial Intelligence & Product

Liability,” McCarter & English, LLP, September 26, 2023, https://

www.mccarter.com/insights/artificial-intelligence-product-liability/ 12 “EU AI Act: First Regulation on Artificial Intelligence,” European Parliament, February 19, 2025, https://www.europarl.europa.eu/

topics/en/article/20230601STO93804/eu-ai-act-first-regulation-

on-artificial-intelligence

13 Julie E. Cohen, “The Biopolitical Public Domain: The Legal

Construction of the Surveillance Economy,” Philosophy &

Technology 31, no. 2 (2018): 213–233, https://papers.ssrn.com/

sol3/papers.cfm?abstract_id=2666570

14 Albert Borgmann, Holding On to Reality: Digital Media and the

Path to Understanding (Chicago: University of Chicago Press,

2006), https://press.uchicago.edu/ucp/books/book/chicago/H/

bo3640475.html

15 Leonardo/ISASTwith Arizona State University. “Review

of Privacy’s Blueprint: The Battle to Control the Design of

New Technologies,” April 29, 2021. https://leonardo.info/

review/2018/12/review-of-privacys-blueprint-the-battle-to-control-

the-design-of-new-technologies

16 Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan,

“Semantics Derived Automatically from Language Corpora

Contain Human-like Biases,” Science 356, no. 6334 (2017): 183–

186, https://www.science.org/doi/10.1126/science.aal4230 17 Oishee Kundu, Elvira Uyarra, Raquel Ortega-Argiles, Mayra M Tirado, Tasos Kitsos, Pei-Yu Yuan, Impacts of policy-driven

public procurement: a methodological review, Science and Public

Policy, Volume 52, Issue 1, February 2025, Pages 50–64, https://

doi.org/10.1093/scipol/scae058

18 Batya Friedman, Peter H. Kahn Jr., and Alan Borning, “Value

Sensitive Design and Information Systems,” in The Handbook

of Information and Computer Ethics, ed. Kenneth Einar Himma

and Herman T. Tavani (Hoboken, NJ: John Wiley & Sons, 2008),

69–101, https://android.eng.ankara.edu.tr/wp-content/uploads/

sites/656/2017/10/10_The_Handbook_of_Information_and_

Computer_Ethics.pdf

19 Peter-Paul Verbeek, What Things Do: Philosophical Reflections

on Technology, Agency, and Design (Penn State University Press,

2005), https://www.jstor.org/stable/10.5325/j.ctv14gp4w7 20 Andrew McAfee and Erik Brynjolfsson, Machine, Platform, Crowd: Harnessing Our Digital Future (New York: W. W. Norton

& Company, 2017), https://www.amazon.com/Machine-Platform-

Crowd-Harnessing-Digital/dp/0393254291

262 AI TOOLS, NOT GODS

21 Deirdre K. Mulligan, Joshua A. Kroll, Nitin Kohli, and Richmond

Y. Wong. “This Thing Called Fairness.” Proceedings of the ACM

on Human-Computer Interaction 3, no. CSCW (November 7,

2019): 1–36. https://doi.org/10.1145/3359221 22 Frank Pasquale, “The Algorithmic Self,” The Hedgehog Review 17, no. 1 (Spring 2015). Available at: https://hedgehogreview.com/

issues/too-much-information/articles/the-algorithmic-self 23 Maja van der Velden and Christina Mörtberg, “Participatory Design and Design for Values,” in Handbook of Ethics, Values,

and Technological Design, ed. Jeroen van den Hoven, Peter E.

Vermaas, and Ibo van de Poel (Dordrecht: Springer, 2015), 41-66,

https://doi.org/10.1007/978-94-007-6970-0_33 24 Jenna Burrell, “How the Machine ‘Thinks’: Understanding Opacity in Machine Learning Algorithms,” Big Data & Society

3, no. 1 (2016): 1–12, https://journals.sagepub.com/doi/

full/10.1177/2053951715622512

25 Shannon Vallor, Technology and the Virtues: A Philosophical

Guide to a Future Worth Wanting (Oxford: Oxford University

Press, 2016), https://global.oup.com/academic/product/

technology-and-the-virtues-9780190498511

26 John Cheney-Lippold, We Are Data: Algorithms and the Making

of Our Digital Selves (New York: NYU Press, 2017), https://www.

jstor.org/stable/j.ctt1gk0941

27 Lina M. Khan, “Amazon’s Antitrust Paradox,” Yale Law Journal

126, no. 3 (2017): 710–805, https://papers.ssrn.com/sol3/papers.

cfm?abstract_id=2911742

28 Mary L. Gray and Siddharth Suri, Ghost Work: How to Stop

Silicon Valley from Building a New Global Underclass (Boston:

Houghton Mifflin Harcourt, 2019), https://www.microsoft.com/

en-us/research/publication/ghost-work-how-to-stop-silicon-

valley-from-building-a-new-global-underclass-co-authored-with-

siddharth-suri/

29 Anu Bradford, The Brussels Effect: How the European Union

Rules the World (Oxford: Oxford University Press, 2020), https://

academic.oup.com/book/36491

30 MDR Regulator, “Clinical trials of medical devices confirm that

the products are designed and manufactured in accordance with

legal requirements,” MDR Regulator: CE Marking for Medical

Devices, https://mdrregulator.com/ce-marking/clinical-trials-of-

medical-devices

31 Michael D. Abràmoff, Philip T. Lavin, Michele Birch, Nilay Shah,

and James C. Folk, “Pivotal Trial of an Autonomous AI-Based

Diagnostic System for Detection of Diabetic Retinopathy in

Endnotes 263

Primary Care Offices,” npj Digital Medicine 1 (2018): 39, https://

doi.org/10.1038/s41746-018-0040-6

32 Guido D. Giebel et al., “Problems and Barriers Related to the Use

of AI-Based Clinical Decision Support Systems: Interview Study,”

Journal of Medical Internet Research 27 (2025): e63377, https://

doi.org/10.2196/63377

33 British Medical Association, “Principles for Artificial Intelligence

(AI) and Its Application in Healthcare” (London: British Medical

Association, 2024), https://www.bma.org.uk/media/njgfbmnn/

bma-principles-for-artificial-intelligence-ai-and-its-application-in-

healthcare.pdf

34 Casey Ross and Ike Swetlitz, “IBM Pitched Its Watson

Supercomputer as a Revolution in Cancer Care. It’s Nowhere

Close,” STAT, September 5, 2017, https://www.statnews.

com/2017/09/05/watson-ibm-cancer/

35 Google DeepMind, “AlphaFold: a solution to a 50-year-old grand

challenge in biology,” https://deepmind.google/discover/blog/

alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology/ 36 Luciana Debenedetti, “Togo’s Novissi Cash Transfer: Designing and Implementing a Fully Digital Social Assistance Program

during COVID-19,” Innovations for Poverty Action, August 4,

2021, https://poverty-action.org/publication/togo%E2%80%99s-

novissi-cash-transfer-designing-and-implementing-fully-digital-

social-assistance

37 Emily Aiken, Suzanne Bellue, Dean Karlan, Christopher Udry,

and Joshua E. Blumenstock, “Machine Learning and Phone Data

Can Improve Targeting of Humanitarian Aid,” Nature 603, no.

7903 (March 31, 2022): 864–70, https://doi.org/10.1038/s41586-

022-04484-9.

38 International Civil Aviation Organisation (ICAO), https://www.

icao.int/Pages/default.aspx

39 International Council for Harmonisation of Technical

Requirements for Pharmaceuticals for Human Use (ICH), https://

www.ich.org/

40 Tim Wu, “ A Brief History of American Telecommunications

Regulation,” Oxford International Encyclopedia Of Legal

History, Vol. 5, p. 95, 2009 (2007), https://scholarship.law.

columbia.edu/faculty_scholarship/1461/

41 U.S. Food & Drug Administration, “The Drug Development

Process – Step 3: Clinical Research,” https://www.fda.gov/

patients/drug-development-process/step-3-clinical-research 42 U.S. National Highway Traffic Safety Administration, “Laws &

Regulations,” https://www.nhtsa.gov/laws-regulations 264 AI TOOLS, NOT GODS

43 Government of Canada, “Algorithmic Impact Assessment tool,”

Canada.ca, https://www.canada.ca/en/government/system/digital-

government/digital-government-innovations/responsible-use-ai/

algorithmic-impact-assessment.html

44 Treasury Board of Canada Secretariat, Algorithmic Impact

Assessment Tool, Government of Canada, https://www.canada.

ca/en/government/system/digital-government/digital-government-

innovations/responsible-use-ai/algorithmic-impact-assessment.

html

45 Serena Oduro, Emanuel Moss, and Jacob Metcalf, “Obligations

to Assess: Recent Trends in AI Accountability Regulations,”

Patterns 3, no. 11 (November 11, 2022): 100608, https://doi.

org/10.1016/j.patter.2022.100608

46 U.K. Financial Conduct Authority, “Regulatory Sandbox,”

https://www.fca.org.uk/firms/innovation/regulatory-sandbox 47 Wikipedia, s.v. “Artificial Intelligence Act,” https://en.wikipedia.

org/wiki/Artificial_Intelligence_Act

48 New York State Assembly bill A6453A, Relates to the training

and use of artificial intelligence frontier models, 2025-2026 Leg.

Sess. (N.Y. 2025), https://www.nysenate.gov/legislation/bills/2025/

A6453/amendment/A

49 California SB 53, Artificial intelligence models: large developers,

2025-2026 Reg. Sess. (Cal. 2025), https://leginfo.legislature.

ca.gov/faces/billTextClient.xhtml?bill_id=202520260SB53 50 Kevin Frazier and Andrew W. Reddie, “Why Context, Not Compute, is the Key to AI Governance,” Tech Policy Press,

August 5, 2025, https://www.techpolicy.press/why-context-not-

compute-is-the-key-to-ai-governance/

51 U.S. Congress, Office of Technology Assessment, The OTA

Legacy, Princeton University (archived), http://www.princeton.

edu/~ota/

52 Bruce A. Bimber, The Politics of Expertise in Congress: The Rise

and Fall of the Office of Technology Assessment (Albany, NY:

State University of New York Press, 1996), https://archive.org/

details/politicsofexpert0000bimb.

53 U.S. Congressional Budget Office, “Introduction to CBO,” https://

www.cbo.gov/about/overview

54 U.S. Food & Drug Administration, “Breakthrough Devices

Program,” https://www.fda.gov/medical-devices/how-study-and-

market-your-device/breakthrough-devices-program 55 Environmental Protection Agency, “Toxics Release Inventory (TRI) Program,” U.S. Environmental Protection Agency, last

Endnotes 265

updated July 3, 2025, https://www.epa.gov/toxics-release-

inventory-tri-program/

56 Bank for International Settlements, “CAP10 Definition of eligible

capital,” in Basel Framework, https://www.bis.org/basel_

framework/chapter/CAP/10.htm docs.oracle.com+15 57 United Nations Environment Programme, “The Montreal Protocol on Substances that Deplete the Ozone Layer,” https://

ozone.unep.org/treaties/montreal-protocol

58 Global Partnership on Artificial Intelligence, “2024 GPAI New

Delhi Declaration,” adopted July 3-4, 2024, New Delhi, para.

1-2, https://gpai.ai/gpai-new-delhi-declaration-2024.pdf 59 European Parliamentary Research Service, Digital Sovereignty for Europe, https://www.europarl.europa.eu/RegData/etudes/

BRIE/2020/651992/EPRS_BRI(2020)651992_EN.pdf 60 Anu Bradford, The Brussels Effect: How the European Union Rules the World (New York: Oxford University Press, 2020),

189, https://global.oup.com/academic/product/the-brussels-

effect-9780190088583

61 Caroline De Cock, “Digital Sovereignty: Europe’s Favourite

(Empty) Buzzword,” Information Labs (blog), October 22, 2025,

https://informationlabs.org/digital-sovereignty-europes-favourite-

empty-buzzword/

62 Ursula von der Leyen, “A Union that strives for more. My agenda

for Europe - Political Guidelines for the Next Commission,”

European Commission, , https://commission.europa.eu/

document/download/063d44e9-04ed-4033-acf9-639ecb187e87_

en?filename=political-guidelines-next-commission_en.pdf 63 Neil Postman, “Technopoly: The Surrender of Culture to Technology”, Vintage Books, 1993, https://ia601802.us.archive.

org/27/items/235p-technopoly-neil-postman/235p%20technopoly-

neil-postman.pdf

64 Brian Walker and David Salt, Resilience Thinking: Sustaining

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Acknowledgments 319

ACKNOWLEDGMENTS

T hey say writing is a solitary activity, but I have found that publishing a book is a distinctly collec-tive eff ort. It requires a village, a lot of patience, and in my case, a very specifi c turning point involving

a dog.

I suspect very few acknowledgments sections begin by

thanking a pitbull that attacked the author. However,

that terrifying encounter served as a wake-up call. It

made me realize that carpe diem does not combine well

with the slow, methodical pace of a fi ve-year doctor-

ate. That incident shifted my trajectory entirely; I aban-

doned the idea of a PhD and decided to write this book

instead. Life is too short to leave things unsaid.

When it came to the writing itself, I owe a massive debt

to my copy editor, Carol King. She did not just pol-

ish the text: she held up a mirror to my writing ticks.

Specifi cally, she gently pointed out that I use the word

"fundamentally" far too often. Beyond saving the read-320 AI TOOLS, NOT GODS

er from my repetitive vocabulary, she was instrumental

in the book’s evolution. She pushed me to ensure the

journey from the first draft to the final version result-

ed in something crisper. She insisted the text remain

action-focused, preventing me from getting lost in the

weeds of theory.

I am equally grateful to Glyn Moody, who bravely ac-

cepted the task of the ultimate proofread. His construc-

tive feedback on the final manuscript was invaluable,

helping me spot any remaining cracks in the foundation

and giving me the confidence to cross the finish line.

Finally, ideas do not exist in a vacuum. I must thank

Luciano Floridi and Anna Nobre. Their article on the

anthropomorphization of AI was a lightbulb moment

for me. It provided the precise language I needed to

articulate my lingering frustrations regarding the inter-

action between policy and technology. Their work gave

me the tools to express what I had been feeling: that we

must stop treating our creations like gods.

About the Author 321

ABOUT THE AUTHOR

CAROLINE DE COCK, LL.M., operates at the often

complex intersection of technology, law, and gover-

nance. For over two decades, she has worked to bridge

the gap between the rigid world of regulation and the

fl uid pace of digital innovation.

Currently serving as Head of Research at information

labs and a Senior Fellow at the Lisbon Council, Car-

oline specializes in translating complex technical is-

sues—from AI and copyright to Internet governance—

into accessible insights for policymakers and the pub-

lic. She acts as a translator between these worlds, a role

she also fulfi lls as the Founder of Square-up Agency

and N-square Consulting.

She is no stranger to the written word or the legislative

battleground. She is the author of iLobby.eu: Survival

Guide to EU Lobbying (2010), a book that pulled back

the curtain on the Brussels bubble. Her analysis of the

digital landscape extends back to the early days of the 322 AI TOOLS, NOT GODS

internet economy, with contributions as a researcher to

The New Virtual Money and Business and Law on the

Internet.

Beyond books, Caroline is a prolific commentator, blog-

ger, and podcaster who actively shapes the debate on

digital rights. Her writing and analysis have appeared

in Techdirt, Euractiv, and Techpolicy, among others.

Her perspective is shaped by a life spent crossing bor-

ders. Having lived in eleven countries across four con-

tinents—from Africa to South America—she brings a

deeply global lens to the challenges of digital regula-

tion. She understands that policy does not exist in a

vacuum, and that the metaphors we use to describe

technology can have real-world consequences. This fo-

cus on narrative, clarity, and democratic accountability

anchors her latest work, AI Tools, Not Gods.

Table of Contents

CHAPTER 8 The Foundation: from Graveyard to Blueprint ��������������������������������169

PART II

CHAPTER 6

CHAPTER 7

PART IV

CHAPTER 8

CHAPTER 9

EPILOGUE

Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

Chapter 8

Chapter 9