AI in the Public
Interest: Confronting
the Monopoly Threat
NOVEMBER 2023

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TABLE OF CONTENTS
Executive summary................................................................................................................................................................ 4
Definitions - what are we talking about when we talk about AI?..................................................................................... 10
I: The state of debate on competition policy and AI........................................................................................................... 11
A revolution in antimonopoly thinking and enforcement........................................................................................................ 11
Diversion and distraction in the AI debate ...............................................................................................................................12
II: A few platform monopolies broadly control AI..............................................................................................................15
Generative AI is a function of concentrated technological capacities and capabilities ....................................................... 15
Dominant platform monopolies are using their power to co-opt and eliminate rivals..........................................................17Competition is not one click away............................................................................................................................................ 20
III: AI reinforces existing monopoly power and harms...................................................................................................... 23
Monopolists are using AI to further entrench their existing dominance...............................................................................23
Steering debate and innovation through economic and political power................................................................................25
AI is amplifying today’s monopoly harms, and is creating new ones ....................................................................................26
Distortion of public debate............................................................................................................................................... 26
Ever more extreme targeted manipulation and discrimination .......................................................................................27
Non-discrimination rules as anti-monopoly policy: A short history...............................................................................30
Exploitation and manipulation of sellers and publishers................................................................................................... 31
Exclusion of rivals and undermining of innovation........................................................................................................... 33
The threat to creators and creative property................................................................................................................... 34
The threat to workers and jobs.......................................................................................................................................... 35
The threat to resilience and security ................................................................................................................................ 35
IV: Solutions......................................................................................................................................................................... 37
AI regulation thus far: necessary first steps but far from sufficient ......................................................................................37
Using competition policy to keep AI safe, open, and accountable .......................................................................................40
Immediate next steps – eight actions governments can take now ........................................................................................ 41
Conclusion ...........................................................................................................................................................................47
Endnotes...............................................................................................................................................................................48

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ACKNOWLEDGEMENTS

AUTHOR S:

Barry Lynn, Max von Thun, Karina Montoya
E D ITOR:

Anita Jain
CONTR IBUTORS:

Philip Longman, Courtney Radsch, Daniel Hanley,
Ezmeralda Makhamreh
S PECIA L THANKS TO:

Ian Brown, Amba Kak, Michelle Meagher, Johnny
Ryan, Nicolas Moës, Nick Shaxson, Kris Shrishak,Matthias Spielkamp, Andrew Strait, Sarah West

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EXECUTIVE SUMMARY
Since bursting into public view a year ago when OpenAI released its ChatGPT
generative AI “chatbot,” the latest generation of Artificial Intelligence (AI)technologies has been celebrated by many observers as a revolutionary
breakthrough in computing. Boosters have claimed that AI will accelerate drug
discovery, help reduce carbon emissions, improve government efficiency, improve
the quality of work, and help us manage our finances. They have told us toenvision AI as our personal online agent, mental health aide, friend, even digital
lover, ever vigilant for our welfare. And that’s just for starters. Many of the mostprofound changes of the age of AI, we are assured, have yet even to be imagined.
Many others, including leading technologists, loudly
warn that AI also or even mainly poses extreme, evenexistential, threats to individuals and society as awhole. These include disrupting democracy throughmisinformation and propaganda, starving the free press
and warping public debate, the theft of the properties andideas of independent businesses and individual creators,
increased levels of digital addiction and depression, and themanipulation and exploitation of individuals as they seek to
do business with one another or simply get through theirlives. Others, meanwhile, warn of mass unemployment andthe wholesale destruction even of many high-skilled jobs,
including those of writers, computer coders, and doctors.
Some respected experts believe AI poses grave threats
to national security, especially when put to use by the
Chinese state. Recently a group of industry leaders said AI
may even pose a “risk of extinction” akin to pandemics andnuclear weapons.
Governments around the world have responded witha wide variety of actions, including hearings, studies,and early-stage regulation. In Europe this includes theEU’s flagship Artificial Intelligence Act, and an in-

depth study of competition in foundation models by
the UK’s competition authority. In the United States,
the Senate has convened high-profile meetings withthe top executives of the corporations that dominate AI
technology and infrastructure. The Biden Administration
published a Blueprint for an AI Bill of Rights and thenin late October published a far-reaching Executive
Order that targeted AI-related harms and threats
across society. Many of the private individuals andcorporations who helped to develop and/or now control
AI technologies have loudly embraced the idea that someforms of regulation are necessary, and some are actively
working to shape whatever new rules emerge.
To the extent that any common assumptions underpin
such regulatory efforts, two stand out. First, that AI is a
new technology, hence we need to carefully develop new
forms of regulation and even new regulatory institutions
designed to strike the right balance between control
and innovation. Second, that AI has so disrupted thecompetitive landscape that many, if not most, of theexisting efforts by law enforcers and legislators to address
the power, structure, and behaviors of Google, Amazon,4

Microsoft, Meta, and other immense monopolists
may no longer be necessary and may actually be
counterproductive, such as by blocking smart innovations.
There is, however, another way to understand thepromise, threats, and practical regulatory challenges
we face in managing the advent of AI. This is to viewthe challenge through the lens of the already existing
powers, structures, and behaviors of the corporations
that control the foundational technologies and capacities
underpinning AI. Doing so guides us to a simple set ofconclusions and directs us toward practical, proven,and constructive solutions that will enable us both tominimize the political and social threats posed by AI,
while also maximizing the promise of these technologies
to improve the lives of individuals and society as a whole.
When viewed through this lens, what we see is that:
I. AI is a function of concentrated technologicalcapacities and capabilities
Today’s leading AI models are largely beingcommercialized by a handful of corporations able toexploit unique technological capabilities. For instance,
although foundation models are trained on datasetscollected from the open internet, Google, Microsoft,
Amazon, Meta, and Apple each control extensive private
caches of data that give them unassailable advantages overpotential rivals. Similarly, Google, Microsoft, and Amazon
control a wide variety of computing and cloud capabilities,
data management technologies, and other capacities andexpertise that further cement their dominance.
II. Monopolists are exploiting existing advantages toshape and speed the rollout of AI
The existing market dominance of these five corporations
is based – in addition to their control of data andcomputing and cloud capabilities – on the power andreach of the online platforms they already control, their

sophisticated models for manipulating and exploiting
the behaviors of businesses and individuals, and othertechnological and commercial chokepoints they haveforged and honed over two decades. Over the same
period, these corporations have developed a vast and farreaching system of political relationships, lobbying power,
and legal expertise to protect and expand these monopoly
positions. These dominant positions and concentrationsof power give these corporations vast abilities both toaccelerate the development and commercial rollout of AI
technologies, and to shape and manipulate today’s publicdebate over how to design, use, and regulate AI.
III. Monopolists are using AI to further entrench their
existing dominance.
AI is enabling these dominant corporations to dramaticallyincrease the quality and quantity of the many competitive
advantages they already enjoy vis-à-vis potential rivals
in ways that only deepen the moats that surround theirsearch and mapping engines, social media platforms, digitaladvertising systems, e-commerce platforms, email andoperating systems, and more. This includes, foremost, byradically amplifying their abilities to:• Engage in sophisticated, personalized, and highly

profitable manipulation and exploitation of individuals
and businesses who depend on their services forcommunication and commerce.

• Misappropriate or simply outright steal informationand other digitizable properties developed by smaller
businesses and individuals, including notably journalism,music, books, photography, art, and even the most
personal skills and attributes of particular individuals,
while making it even harder than in recent years to
track the theft.

• Further entrench their already formidable control
over the development and application of criticaltechnologies and key digital infrastructure.

5IV. Monopolists control the direction, speed, and natureof AI innovation
These corporations already broadly control the direction,
speed, and nature of innovation in many if not mostof the key technologies in the internet tech stack. In
addition to cloud capacity, computing technologies, anddata, this includes chokeholds over computer and mobile
phone operating systems, the standards and governanceof the World Wide Web, and increasingly even thedesign and commercialization of semiconductors. These
existing concentrations of power, in combination with
their emerging dominance in AI, give this same handfulof corporations the ability to determine when, how,
and in whose interests AI is developed and rolled out.
Their control over AI’s ‘upstream’ infrastructure means
they can easily identify any serious potential rival in its
earliest stages and then move swiftly to crush, sidetrack,co-opt, or simply acquire the upstart.1 In short, these
corporations are already shaping the entire ‘downstream’
ecosystem to serve their own short-term privateinterests in ways that will in many instances prevent
other companies and individuals from using AI to solve
urgent challenges and improve people’s lives.
V. AI amplifies many of today’s most dangerous
monopoly harms.
These corporations are exploiting AI to rapidly boost
many of their existing abilities to engage in the
personalized manipulation and exploitation of individuals
and companies that depend on the platforms andservices they control. This is already worsening a numberof political, economic, and social harms, including:• Suppression of trustworthy information. In recent

years, these corporations have restructured criticalcommunications and commercial systems more
dramatically than any previous period in history.
They have done so in ways that severely reduce anindividual’s ability to gather, report, verify, share,

and make practical political and economic use oftrustworthy information. Specific harms include
diversion of advertising revenue and readers awayfrom publishers, the routine manipulation of theinformation and services delivered to individuals
and businesses, distortion and censorship of publicconversation and debate, and degradation of essential
services.

• Propaganda and misinformation. The extreme

explosion of propaganda and misinformation inrecent years is largely a function of the reach andpower of the corporations that control the maincommunications and commercial platforms on theinternet, combined with business models designed
to directly manipulate what individuals buy, where
they go, what they read, and how they act politically.
With some exceptions, it is not Facebook and Googlethat are leveraging their own platforms to spread
propaganda and disinformation, but rather state-leveland private actors who pay to use their platforms tomanipulate the thinking and actions of individuals andorganizations. The introduction by these gatekeepersof generative AI capabilities dramatically boosts theability of these actors to personalize propaganda andmisinformation in ways that geometrically increase its
political, social, and psychological effects.

• Addiction to social media, gaming, and gambling.

Growing use of social media, gaming and other
online services has been associated with gamblinglike addiction, depression and other serious harms
to mental health, particularly among minors. These
problems are magnified by our collective dependence
on the products and networks of a few underregulatedmonopolistic platforms, whose business models
prioritize screen time and viral content over quality
information and engagement. Generative AI’s
unprecedented ability to customize and target
bespoke content to individual users appears already to
be reinforcing these harmful effects.

6• Manipulation of workers and contractors. Dominanttech corporations have a long track record ofsurveilling workers themselves as well as giving other
employers the ability to do so. Examples include
Amazon’s aggressive surveillance of its warehouse
workers and delivery contractors, and workplace
tools provided by Microsoft and Google that enableinvasive monitoring of individual workers. Digitalsurveillance and AI also give employers the ability
to manipulate workers through the payment ofdifferential wages, particularly in employment in
the so-called “gig economy.” There is a significant
risk that generative AI increases these surveillancecapabilities, while also giving employers newopportunities to manipulate workers through tailoredcontent.

• Monopolistic extortion of sellers. Monopolization of
key commercial gateways – especially e-commerce

platforms but also cloud computing, app stores, andother platforms – has given a handful of corporations
the power to decide who gets access to the market,
and on what terms. Through their control of thesegateways, Amazon, Apple, and Google are able toextract extortionate fees from sellers, set the terms
and conditions governing how they do business, anddegrade or cut off access at will.2 This gatekeeper
power also stifles criticism from sellers, who
understandably fear retribution. These abuses are
set to be magnified by concentration in the cloudcomputing platforms and foundation models uponwhich the majority of AI tools will be built.

• Reduced security and resilience. As growing

numbers of businesses, industries, and governments
incorporate AI into their services and operations,the risks to security and resilience from extreme
concentration in the core infrastructure upon which
AI depends will rapidly increase. We already have seen
this threat play out in the highly concentrated cloudcomputing systems, especially those run by Amazon.
While outright failure of these systems is the mostobvious threat, cyberattacks, rogue algorithms, andfaulty data would have equally systemic implications.

• Degradation of essential communications andcommercial services. While initially praised forproviding innovative new services, many of theplatforms developed by today’s tech monopolies
have deteriorated in quality over time.3 Examples
include increasingly inaccurate search results on
Google, the crowding out of genuine sellers withpaid advertisements on Amazon, and replacement oforganic content with algorithmic recommendations
on social media networks such as Instagram andFacebook. Generative AI is already accelerating thisprocess in numerous ways through the creation at
ever-faster speeds of low-quality and inaccuratecontent4 which crowds out useful and genuinematerial.

VI. Competition law, rules, and regulations are our mostimmediately effective tools.
History teaches that our economies and societies
have faced many revolutionary communications andtransportation technologies in the past, including therailroad, telephone, electricity, and the internet. It alsoteaches that the only way to prevent powerful private
corporations from exploiting these new technologies inways that upset basic political or social balances is to use
antitrust and other forms of competition law to carefully
regulate the behavior, structure, and internal governanceof both individual corporations and entire industries.
Doing so has repeatedly empowered the public as awhole to take full advantage of these great advances inscience and engineering. Doing so has also repeatedly
made it easier to use health, safety, consumer, and otherregulatory regimes to help protect the interests andwellbeing of individuals, communities, and the public as awhole.

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Our broad failure thus far to apply these lessons to
the digital gatekeepers that dominate our online andoffline lives has already resulted in a wide array of
extreme harms to our democracy, individual liberty, andprosperity. In recent years this has begun to change,
thanks to a revolution in antimonopoly lawmaking inEurope and in law enforcement in the United States. But
this work remains far from done, and the advent of AI
makes it all that much more urgent to complete the job.
BROAD RECOMMENDATIONS:
Our report sets out broad recommendations for
enforcers and policymakers in four key areas:
I. Establish a clear hierarchy of goals for regulatory
action, to help prioritize the use of limited resources.
Given limited resources, and the scale of the challenge
at hand, it is crucial that lawmakers and regulators
establish a clear hierarchy of goals for regulatory action.
The top priority should be tackling threats to individual
liberty and democratic institutions, which are essential
if we are to break and harness the power of the online
gatekeeper corporations that now threaten us. This inturn implies a close and immediate focus on the many
ways in which the existing power and existing behaviorsof these immense, privately controlled gatekeepers
threaten our ability to communicate and debate, gather
and share news, and do business directly with one
another. Questions of technological innovation should
be of secondary importance compared to making theseplatforms safe for democracy, although given sufficient
resources, law enforcers can also focus simultaneously on
promoting an open and competitive political economy.
Indeed, many actions that would protect our core
political rights and interests would also begin to provide
individuals and businesses with greater opportunity topromote innovation and to master AI and other new
technologies in the public interest.

II. Make aggressive use of existing law, and invest inlegislation and new regulatory institutions only where
there’s a clear need and reasonable chance of success.
Many governments already possess wide-ranging
powers that can be deployed now to prevent today’s
monopolists from using AI to further cement theirpower and to more effectively exploit and manipulate
individual people and organizations, and indeed to begin
to unwind dangerous existing concentrations of capacity
and control. These powers include competition law andpolicy, trade policy, consumer protection laws, privacy
regulation, and copyright protection. This approach isespecially important in the United States, where there isan extremely vast and robust collection of powerful laws
and regulatory regimes to address such threats, built up
and refined over the course of more than two centuries,
and where longstanding gridlock in Congress makes it
unlikely the institution will pass new competition lawssoon.
III. Accelerate efforts to adapt existing competition law
to address today’s threats.
Law enforcers and lawmakers are engaged in themost fundamental and comprehensive rethinking ofcompetition policy since the Chicago School revolutionof the early 1980s and, in some respects, since the New
Deal. In the United States, Europe, and elsewhere, they
are scrambling to restore traditional pre-Chicago School
goals, principles, and analysis, as we see in the newdraft merger guidelines published by the Departmentof Justice and Federal Trade Commission. They arealso moving to adapt and update those regimes forthe digital age, as we see in new European laws such as
the Digital Markets Act and Digital Services Act andin the wide-ranging lawsuits by both the federal andstate governments in the United States against Google,
Amazon, and Facebook. This effort is still, however, in its
early stages. It is vital to immediately extend this effort
to cover such factors as control of data and computing

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capacity, including directly in relation to AI. It is also

largest gatekeeper platforms, and regulate it as a utility.

vital to move swiftly to integrate this effort with legal
and regulatory regimes that intersect with antitrust,
including communications, trade, privacy, copyright, andconsumer protection, among others.

III. Recognize that any data collected by large platforms
in their capacities as essential services is public innature, and establish a public-interest regime to govern
access.

IV. Ensure that dominant corporations in control ofessential platforms and services treat all users the same.
A core tenet of modern competition policy is therequirement that private corporations that control
essential services do not discriminate in the delivery ofthese services, and provide equal access to all comers. As
Senator John Sherman said in a speech in favor of the
U.S. antitrust law that bears his name, such regulations
lie “at the foundation of the equality of all rights andprivileges.” Today this principle is especially importantwhen addressing the actions and business models of
essential communications and commercial platforms,
as well as all essential AI infrastructure including cloudcomputing capacity and foundation models.
IMMEDIATE ACTIONS:
Our report sets out specific recommendations for
enforcers and policymakers to take immediate action in

IV. Aggressively enforce copyright laws to protect theproperties of authors, creators, and other independent
publishers from misappropriation and misuse by
gatekeeper corporations, and establish a trustworthy
and transparent system for auditing the use of
copyrighted material in AI systems.
V. Clearly map how the structures, behaviors, andbusiness models of the largest gatekeepers threaten
national security, including by enabling foreign
surveillance and interference. Use government
investment and procurement policies to breakchokepoints and promote security.
VI. Reverse gatekeeper efforts to control AI
development through mergers, investments andpartnerships and block similar deals in future.

eight key areas.

VII. Establish bright-line rules to limit digital

I. Ban all discrimination by powerful gatekeeper

complete ban on biometric surveillance and automatedplatforms in the delivery of essential services tomanipulation.

exploitation of workers and contractors, including a

individuals and businesses.
VIII. Increase strategic collaboration between
II. Recognize cloud computing as an essential

competition law enforcers and data protection andinfrastructure, separate ownership and control from theprivacy regulators.

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DEFINITIONSWhat are we talking about when we talk about AI?
The contemporary discussion around AI may be confusing given the multiplicity of terms
in use. Below are some basic definitions of terms used frequently throughout this report.
Algorithm: A set of mathematical instructions or rules that, especially if given to acomputer, will help to calculate an answer to a problem (Cambridge)

Artificial intelligence: Umbrella term for a range of algorithm-based technologies thatsolve complex tasks by carrying out functions that previously required human thinking(UK Information Commissioner’s Office)Foundation models: Models trained on broad data (generally using self-supervisionat scale) that can be adapted to a wide range of downstream tasks (Stanford)

General purpose AI: Largely synonymous with foundation models; refers to an AI
system that can be used in and adapted to a wide range of applications for which it
was not intentionally and specifically designed (European Parliament)

Generative AI: Describes algorithms and applications that can be used to create newcontent, including audio, code, images, text, simulations, and videos (McKinsey)

Large language model: AI systems trained on significant amounts of text data thatcan generate natural language responses to a wide range of inputs (Ada Lovelace
Foundation)

Machine learning: The study of how computer agents can improve their perception,knowledge, thinking or actions based on experience or data (Stanford)

Figure 1:AI glossary10I : T H E S T A T E O F D E B A T E
ON COMPETITION POLICY AND AI
With this report, the Open Markets Institute seeks tobring together two debates that have largely been kept
separate thus far: that of the promise and perils of AI,
and of the harms of monopoly power in the digital age.
As the rest of this report will make clear, it is our belief
that to design appropriate solutions that place thedevelopment of AI in service of the public interest, weneed to view this technological development through thelens of the power and structure of dominant corporations
and competition policy. The real and urgent harms
posed by AI cannot be fully understood or addressed
without bringing to the forefront of the debate theexisting monopoly power of corporations such as Google,
Microsoft, and Amazon, as well as the well-developed
debate and many already existing tools and proposals to
address their power and behavior.

A REVOLUTION IN ANTIMONOPOLY
THINKING AND ENFORCEMENTOver the last decade, there has been a profound change inattitude towards the power and structure of large private
corporations. The ‘libertarian’ or ‘neoliberal’ philosophy
that has guided political economic regulation sincethe 1980s promoted a laissez-faire approach to large
corporations and financial institutions. But in recent years
the people of the United States and many other nations
have come to conclude that the resulting concentrationof power, control, and capacity poses a wide and growing
variety of grave threats to our democracy, national
security, prosperity, and the environmental sustainabilityof human society. This has been especially true of thecorporations that have captured control over dominant
online communications and commerce, particularly
Google, Amazon, Microsoft, Meta, and Apple.

As a result, lawmakers and law enforcers in the UnitedStates, Europe, and elsewhere are now engaged in a
revolutionary rethinking of the purpose and processesof competition policy. For the last generation, the‘Chicago School’ philosophy of legal theorists such as
Robert Bork and Richard Posner generally supported
the concentration of power and control based on theidea that doing so would help drive down prices andthereby better promote the ‘welfare’ of the consumer.
Today, by contrast, policymakers are increasingly using
antimonopoly enforcement to protect democracy
and individual liberty, and to break or neutralize
any concentration of power that threatens workers,
entrepreneurs, farmers, and local communities.
These changes are well captured in the present effortsof the U.S. Department of Justice and Federal Trade
Commission to draft new ‘guidelines’ to govern how
these agencies will interpret the legality of particular
types of deals. We also see this new thinking in thegrowing number of large enforcement actions in thedigital economy. In the last few years, antitrust enforcers
have brought major legal cases against monopolists
including Google, Meta, and Amazon, taken action
to block anti-competitive takeovers, and passed or
proposed new competition legislation to outlaw abusive
conduct. Viewed in a broad historical context, it is clearthat the United States, Europe, and much of the rest of
the world is in the midst of the most important debate
about how to address concentrations of corporate powerand control since the Second World War, and in certain
respects, since the Progressive Era in the United Statesat the turn of the last century.
In the United States, the idea that existing competition
policy tools could be used to rein in the power of the11new ‘robber barons’ – including the largest of online
gatekeeper corporations – did not arise spontaneously.
On the contrary, it is the result of a long and carefully
developed effort to shine a light on the threats posed byconcentration of power and control, and the many existing
tools available to help protect democracy and security.
A few moments stand out, including:• A major speech delivered by Senator Elizabeth

Warren on June 29, 2016, in the U.S. Capitol. The
speech, hosted by the Open Markets Institute, traced
the decline of competition in American markets andkickstarted efforts to scrutinize and tackle market
concentration by policymakers in both major U.S.
parties;• The 2019 Cicilline Committee hearings andsubsequent 2020 report detailing the monopolistic
practices of Google, Facebook, Amazon, and Apple;• The first major lawsuits against Google and Facebook

in 2020 by the DOJ, FTC, and the attorneys generalof almost every U.S. state and territory.

After President Joe Biden took office in January 2021,
his Administration reinforced and accelerated these
efforts. Most important was the President’s July 2021
signing of a landmark Executive Order that called forthe complete overturn of the Chicago School philosophyof competition policy, and an unprecedented ‘wholeof-government approach’ to promoting competition
across the U.S. economy. Around this same time,
President Biden also appointed Lina Khan to chair theFederal Trade Commission and Jonathan Kanter to serveas Assistant Attorney General to the Department ofJustice’s Antitrust Division.
During these same years, lawmakers and enforcers
across Europe also ratcheted up efforts to address theconcentration of power and control, especially within

the tech sector. Although this includes important
litigation – especially by the UK’s Competition andMarkets Authority and the Bundeskartellamt antitrust
agency in Germany – the most important advances were
through legislation. These include the EU’s General Data
Protection Regulation, Digital Markets Act and Digital
Services Act, Section 19a of Germany’s foundational
competition law, and the UK’s Digital Markets,
Competition and Consumers Bill.
These efforts have been strongly opposed, especially by
the corporations that would be most affected. Dominant
corporations across many industries have benefitedenormously from the past several decades of promonopoly competition policy. They are naturally reluctant
to give up the power and privileges that resulted, andthey have launched a wide variety of legal and politicalchallenges and threats against lawmakers and enforcers
working to rebuild traditional systems of democratic
checks and balances within the political economy.
But in many ways, the battle is only beginning. And
already, it is being dramatically reshaped by the rapid
adoption of AI technologies by many of these same
corporations, as well as by how these corporations
and their close allies are using fear of AI to reshapethe debate about how to address their existing power,
structures, and behaviors.

DIVERSION AND DISTRACTION
IN THE AI DEBATE
If we want to truly understand how AI is being designed
and implemented, and the likely consequences of this,the first factor we must consider is who controls andowns the underlying technology. Contrary to what
proponents of technological determinism might claim,
AI – and technology in general – is not the result of
any sort of natural evolution but rather the result of aseries of technical, business, and political decisions byhuman actors, above all those who control the most12

powerful corporations. One thing this means is that the
AI technologies and services that have been introduced
in recent months and years by Microsoft, Google,
Amazon, and companies they control are very different
in key respects than would be the case if AI systems
were created and implemented by a diverse ecosystemof actors, including startups, SMEs, large companies, andpublic and non-profit actors.
Yet so far, the debate on what to do about AI has paid
little attention to the role played by concentrated powerand control. While the role of the likes of Microsoft,
Google, and Amazon in funding and developing AI
systems is acknowledged, there have been few attempts
to seriously grapple with how the economic and politicalpower of these corporations is already shaping the form
that much AI technology is taking, and the impact it will
have on our societies.
Consider ongoing efforts to ensure AI is used ethicallyand responsibly. Proposed interventions range fromgreater algorithmic transparency and mandatory efforts
to reduce bias, to auditing and human oversight of
AI-driven or assisted outputs and decisions. Notable
examples of this approach include the EU’s AI Act,
which imposes stringent regulatory obligations on
high-risk AI applications, and the Biden administration’sBlueprint for an AI Bill of Rights, which sets out fiveprinciples to guide the design, use, and deployment of
automated systems.
These and similar forms of regulation that aim toprevent AI from being used to harm, exploit, anddiscriminate against people are clearly necessary. But
they will only be effective if accompanied by aggressive
enforcement of competition policy to prevent a few
firms from dominating everything from the nature of thetechnology to access to the technology. One lesson of
the many recent efforts to enforce privacy regulations onthese same corporations is that no matter how well rules
are designed on paper, regulators will struggle to enforce

them on corporations that can treat fines as a cost of
doing business. By contrast, by preventing control overAI from becoming concentrated, we both increase theeffectiveness of AI regulation, while decreasing theability of any single actor to inflict systemic harm.
Just as important is the fact that many of the harms
highlighted by those calling for regulation to ensure theethical and responsible use of AI are already widespread
across our societies. Indeed, these harms are the direct
result of the business models these same corporations
developed to exploit their gatekeeper positions overpublic communications and commerce. Although AI will
make many of these problems worse, the fact that they
are already widespread demonstrates that AI is not thefundamental source of the problem. Here again, the only
way to get at the root is to view the problem through thelens of antimonopoly laws, especially those designed to
set strict rules on how gatekeeper corporations behave
vis-à-vis the companies and individuals that depend on
their services.
Even more dangerous are narratives that, intentionallyor unintentionally, deflect debate away from bothpresent AI harms and the market concentration
driving or amplifying them. Two in particular are worth
examining in more detail. The first is the suggestionthat policymakers, regulators, and law enforcers should
focus primarily on the idea that these technologies pose
long-term “existential risks.” The second is the argumentthat measures to regulate AI or rein in the corporations
behind it risks giving China the competitive advantage.
There is nothing outlandish about the idea that, as thecapabilities of technological systems grow, so do therisks they pose to individuals and society. But the rise ofgenerative AI has resulted in a disproportionate focus on
far-flung risks such as human extinction and malicious“superintelligence,” despite little concrete evidence thatcurrent or future AI models possess such capabilities.
Nonetheless, this narrative is routinely promoted by13certain tech industry figures (including OpenAI CEO
Sam Altman, DeepMind CEO Demis Hassabis, and techbillionaire Dustin Moskowitz) and policymakers including
British Prime Minister Rishi Sunak and UN Secretary
General António Guterres.
There is no reason why human beings cannot focus
simultaneously on both existing real harms andhypothetical but potentially more serious risks in thelong term. In practice, however, policymakers and law
enforcers have limited resources and limited bandwidth,
meaning a focus on one set of harms will divert attention
and energy from another. Given clear evidence thatmany of the existing threats posed by the largest
gatekeeper corporations – including misinformation,
manipulation, discrimination and copyright theft –already pose existential threats to our democracies andour abilities to communicate and make decisions, andthat AI is already amplifying and accelerating these
problems, it makes sense to prioritize tackling thesethreats first. Doing so will, if anything, help ensure thatthe existential threats of tomorrow never come to pass.
There is another more cynical way to interpret theemphasis on existential risks by many in the technology
industry. This is to see these warnings as an intentional
attempt to deflect attention away from present
harms being inflicted by the same large companies
and individuals raising the alarm. Moreover, thenarrative around existential risks is giving dominant AI
companies (i.e. the only ones theoretically capable ofproducing models that pose these hypothetical risks)
unprecedented access to policymakers and policy
discussions, dramatically increasing their ability to shapepolicy debates and potential interventions.
Equally questionable is the alarmism about potential
threats to Western democracies posed by the AI
prowess of the Chinese state and Chinese corporations.
Tech industry leaders have frequently and for many
years now referenced technological competition with
China as a reason to weaken or abandon efforts toenforce antimonopoly and other law against theircorporations, albeit with little success. Over the lastyear or so, however, the advent of AI has helped thesearguments finally find a receptive audience, especiallyin Washington.5 To cite just one example, in April 2023
former Google CEO Eric Schmidt said he didn’t supporta pause in AI research “because it will simply benefit
China.”6
We can acknowledge the importance of remaining
globally competitive – and be concerned about how
the authoritarian Chinese state will deploy AI against
our nations and communities – without allowing these
deceptive arguments to distract us from existing efforts
to make behavior by the gatekeeper corporations safefor democracy. Tackling concentration in AI, and putting
sensible guardrails on how the technology is used, will
not only ensure safety and resilience but also promote
innovation in the market, all of which are essential if
the technology is to be rolled out at scale in a way that
benefits both individuals and society. Abandoning ourvalues in the hopes of “competing” with China will not
only therefore be counterproductive, but undermine theWest’s legitimacy in providing a democratic, pluralistic
alternative to China’s centralized totalitarian model.

14

I I : A F E W P L A T F O R M M O N O P O L I E S
B R O A D LY C O N T R O L A I
Ever since the boom in generative AI caught the public’s
attention, some have sought to portray it as evidence
that competition is alive and well in digital markets. Yet
this is misguided for several reasons.
First, prowess in AI – particularly when it comes to largescale models – is heavily dependent on access to advanced
technological capabilities and data, both of which arehighly concentrated in the hands of a few gatekeeper
corporations. This gives these platforms a huge advantagethat is nearly impossible for others to overcome.
Second, while the landscape for advanced AI models may
at first glance appear to resemble a thriving marketplaceof both large incumbents and innovative challengers, thisinitial impression fails to survive even a cursory scrapeof the surface. Already, dominant platform monopolies
own, fund, and/or provide the underlying infrastructure
for almost every significant player on the market.
Third, competition among a few already dominant platforms
is not the same as competition within genuinely diversified
digital markets. For example, while Microsoft might be able
to use AI to eat into Google’s market share in search, themarket would at best still be dominated by two colossal,
sprawling corporations. The hegemony that a tiny handfulof unaccountable corporations maintains over online
communications and commerce – and indeed almost every
aspect of our digital lives – would remain unchallenged.

companies to move swiftly to establish and maintaindominance over the market. Other than the largest
gatekeeper corporations, few actors have the necessary
computing power (including both cloud capacity andaccess to the most advanced semiconductors), data,
and technical expertise needed to develop advanced AI.
Moreover, it is precisely because the giants possess somuch data and computing power that they have chosen tosteer AI innovation in a direction that makes maximum useof those assets. These concentrated resources are in largepart the result of extreme levels of concentration in digitalmarkets, fortified by a variety of monopolistic conductthat most governments largely ignored until recently.
A deeper look at a few links in the AI “supply chain” or“tech stack” help illustrate the problem.
Computing power. Just three companies (Amazon,
Google, and Microsoft) control over two-thirds of the$600 billion (measured in annual revenue) global cloudmarket, ensuring these corporations have access tocomputing capabilities and storage capacity which noother actors enjoy.7 Even gatekeeper corporations without
cloud arms, namely Meta and Apple, find themselves
reliant on the cloud oligopoly for their computing needs.
The current emphasis on large-scale AI models, which
use approximately 100 times more computing capacity
than other types of models, means that access tohuge amounts of computing power is essential for any

GENERATIVE AI IS A FUNCTION OF
CONCENTRATED TECHNOLOGICALCAPACITIES AND CAPABILITIES

corporation that wants to prosper in this business.8 Then

The sheer quantity of resources needed to train cuttingedge AI models inevitably rewards scale and incentivizes

million to train.9 These are costs that only the very largest

there are the exorbitant costs of training advanced models.
OpenAI’s GPT-4 model, housed on Microsoft’s Azure
cloud infrastructure, is reported to have cost over $100
companies, or those backed by them, can afford.10

15

The supply of the advanced semiconductors essential totraining cutting-edge AI models is similarly monopolized.
Nvidia, a corporation which originally rose to prominence
producing graphics chips for gaming, now enjoys a nearmonopoly designing chips used to train large-scale AI
models.11 Nvidia’s chips are in turn manufactured byanother monopoly, Taiwan Semiconductor Manufacturing
Company (TSMC), which produces 90% of the world’s
advanced semiconductors.12 As a result of skyrocketing
demand, Nvidia’s market capitalization has soared from$145 billion as recently as January 2020 to over $1 trillion
as of November 2023, illustrating investors’ confidence in
its increasingly untouchable market position.

train advanced models, puts it beyond the reach of all
but the largest corporations.14 Moreover, the lack ofalternatives in this segment of the market has alreadyresulted in shortages, with powerful platform monopolies(and the startups partnered with them) best placed tosecure critical supplies. Increasingly the gatekeepers
are vertically integrating into the semiconductor designbusiness directly. Amazon, Apple, Google, and Meta haveall developed in-house semiconductors purpose-built for(among other things) AI operations, with Microsoft set tofollow suit imminently.15

UPSTREAM

Control of and access to data is another advantage
enjoyed by today’s tech giants. Thanks to their hugeThis power translates into astronomical prices for the
scale and business models based on invasive collection
most advanced semiconductors. Nvidia’s flagshipSource:
AI Ada Lovelace Institute of personal data, the dominant gatekeeper corporations
Figure 2: Overview of the foundation model supply chain
13
chip, the H100, costs approximately $40,000 , which,have amassed vast quantities of highly personal andcombined with the fact that thousands are needed to
specific data, which can in turn be used to train andFoundation model supply chain
fine-tune their AI models. This is in addition to the vast
amounts of non-personal data which the tech giants have
DATA
COMPUTE
acquired through their enterprise and cloud computing
L AYERL AYER
services, which provide them with additional insights
and capabilities that further entrench their centrality in
the AI ecosystem. This includes data from their cloud
FOUNDATION
MODEL
customers and data generated by connected devices and
DEVELOPER
L AYERsensors, such as smart speakers.
HOST
L AYERDOWNSTREAMAPPLICATIONL AYERAPPLICATION
USER

Figure 2: Overview of the generative AI supply chain

Some have suggested that the huge data advantage
enjoyed by the biggest gatekeeper corporations isless relevant when it comes to generative AI, because
foundation models are trained primarily on the open web
rather than proprietary datasets. But this theory does
not hold up under close scrutiny, for two main reasons.
First, the tech giants have the advantage of being able totrain their models on both the open web and their own
vast proprietary datasets, giving them a huge advantage
over those who must rely only on the former. Thisadvantage is likely to become increasingly important
as websites, publishers, and creators begin to restrict
access to their content in response to mass trawling16

by AI models.16 Second, even where access to data iscomparable, the dominant gatekeeper corporations
control far greater computing capabilities, enjoy better
access to specialized expertise, and have developed vast
experience in scraping, labeling, and analyzing data.
Smaller specialized AI models and applications, tailoredto specific purposes, will continue to exist in thislandscape, but by definition will not garner the scaleneeded to challenge the giants. Even here, the techmonopolies could in many cases hold the advantage,
primarily through their ownership of personalized,
specialized datasets in areas such as health, finance, andtransport e.g., Google’s ownership of mapping and Fitbit
data and Amazon’s access to sensitive health data via its
recent acquisition of One Medical.

DOMINANT PLATFORM MONOPOLIESARE USING THEIR POWER TO CO-OPTAND ELIMINATE RIVALS
Despite the huge advantages of the dominant gatekeeper
corporations, the market for cutting-edge AI applications
might at first glance appear to be a diverse and competitive
one. Major players participating in the AI race include
OpenAI, Google, DeepMind, Meta, Microsoft, Amazon,and leading startups such as Stability AI, Anthropic, Cohere,
and Hugging Face. Yet what becomes quickly clear when
one begins to dig beneath the surface is that, whoever isultimately left standing, the biggest platform monopolies
are in prime position to maintain their dominance, both overtheir existing lines of business and over AI.
An April 2023 report by the AI Now Institute aptly
summarizes the current landscape: “Only a handful ofcompanies actually run their own infrastructure – thecloud and compute resources foundational to building
AI systems. What this means is that even though ‘AIstartups’ abound, they must be understood as barnacles
on the hull of Big Tech – licensing server infrastructure,
and as a rule competing with each other to be acquiredby one or another Big Tech firm.”17
Take OpenAI, arguably the leading contender in therace thanks to its popular ChatGPT chatbot and GPTlarge-language model. While ostensibly an independentcompany, OpenAI has to date received over around $13
billion worth of investment from Microsoft. In return,according to media reports (the exact terms of thedeal remain confidential), Microsoft received a 49%
stake in the company and the right to three-quarters of
OpenAI’s profits.18 The “partnership” between the twocompanies also requires OpenAI to rely exclusively on
Microsoft’s Azure cloud platform to power its services.
Meanwhile, Microsoft has been allowed to integrate
OpenAI’s models across its entire sprawling suite ofproducts.19
One recent estimate put the cost (primarily consisting ofcomputing resources) to OpenAI of running ChatGPT
at $700,000 per day, or around $21 million permonth.20 And as mentioned above, GPT-4 reportedly
cost over $100 million to train. These are costs very few
independent startups can afford, and a key reason behind
OpenAI’s decision to accept a position of dependence on
Microsoft.
Given this dependence, it is questionable whether
OpenAI can truly be considered an independent player inthe market. The partnership has also blunted Microsoft’s
force as an independent competitor in the market, withthe corporation winding down some its own in-house
AI products, including its flagship AI-driven assistantCortana.21 Indeed, partnerships like Microsoft’s havebegun to attract the attention of antitrust agencies, withthe head of Germany’s competition authority recently
warning that in some cases cooperation agreements
should be seen as “mergers in all but name.”22
The company’s status was described well by Elon Musk,
who took part in the founding of OpenAI, in a recent
tweet:

17

“OpenAI was created as an open source (which is why
I named it ‘Open’ AI), non-profit company to serve asa counterweight to Google, but now it has become aclosed source, maximum-profit company effectively
controlled by Microsoft. Not what I intended at all.”23

library, a leading tool for machine learning developers
which helps Google ensure that AI innovation benefits
the corporation’s vast array of operations and interests.
Incidentally, TensorFlow’s main competitor, PyTorch, wascreated by Meta.

Google/Alphabet’s influence also extends far beyond
its own PaLM model and Bard chatbot. The corporation
purchased leading British AI research lab DeepMindback in 2014, facing little scrutiny from competition
authorities at a time when takeovers by the largest
platform monopolies were typically waved through byregulators. For a while, Google allowed DeepMind tocontinue operating as a largely independent entity andpursue its research free from commercial pressures. This
independence paid off significantly, with DeepMind’s
AlphaGo program defeating Go world champion Lee
Sedol in 2016, and its AlphaFold program making major
breakthroughs in the science of protein folding. But in
April 2023, with the AI arms race in full swing, Google
announced that it was absorbing DeepMind directly
into its AI-focused Brain division, ending the unit’s
independence and enlisting its employees in the service
of Google’s commercial goals.24

Google and Microsoft are taking the most aggressive steps
to dominate the emerging AI ecosystem and co-opt, if
not eliminate, potential challengers. But other platform
monopolies are quietly making moves of their own.

Google also announced this year that it was investing up to$2 billion into Anthropic, representing a significant shareof the startup’s total funding to date and giving Google asignificant ownership stake – in addition to Google’s status
as the main provider of Anthropic’s cloud capacity.25 While
not as large as Microsoft’s investment in OpenAI (whichitself started as a smaller initial investment), Google’s
presence as a major financial and infrastructural backer
will certainly influence the development of Anthropic’s
technology and commercial strategy.
Google is also attempting to position itself as the cloudprovider of choice for generative AI startups, with CEO
Sundar Pichai recently boasting that “more than 70%
of gen AI unicorns are Google Cloud customers.”26 It
also operates the TensorFlow software development

In February of this year Amazon announced a major
partnership with Hugging Face, a hub for open-sourcemachine learning and AI innovation.27 The deal gives
developers on Hugging Face access to Amazon’s cloudinfrastructure, chips, and software, while embedding
them in Amazon’s ecosystem. Amazon Web Services has
also reached a similar deal with open-source AI company
Stability AI.28
Then there is Amazon’s new service Bedrock, whichallows customers to access different foundation AI
models – including Anthropic, Stability AI, and Amazon’s
own models – but only within the confines of the
AWS walled garden.29 Amazon has even announced agenerative AI accelerator to entice startups to build theirproducts using its tools and on its platform, all the whilelocking in their dependence on its cloud infrastructureand services. And it is also following the lead of Microsoft
and Google by investing directly in AI developers, in thiscase a $4 billion play on Anthropic. One of the catches
to the deal is that the startup must use Amazon’s cloudinfrastructure and semiconductors.30
The approach being taken by Meta, meanwhile, has been
to open up its model, Llama, to the wider developer
community by permitting a degree of fine-tuning thatclosed models such as OpenAI’s GPT and Google’s PaLM
do not offer. While at first unintentional – Meta had sought
to limit access to a pre-vetted list of researchers andorganizations before the entire model leaked31 – Llama’s

18Figure 3: How gatekeeper corporations are using partnerships, investments, and acquisitions to establish control over AI

Company

Partnerships

Major investments

Acquisitions• Hugging Face (2023)• Hugging Face (2023,
amount undisclosed)• Evi (2013; $26 million)• Stability AI (2023• AI21 Labs (2023)• Anthropic (2023,
up to $4 billion)• Anthropic (2023)• Orbeus (2015; fee undisclosed)• Harvest.ai (2017; $20 million)• Snackable AI (2023; fee undisclosed)• Perceptio (2015; fee undisclosed• Lattice Data (2017; $200 million)• Regaind (2017; fee undisclosed)• Laserlike (2018; fee undisclosed)• Silk Labs (2018; fee undisclosed)• Fashwell (2019; fee undisclosed)• Vilynx (2020; $50 million)• Voysis (2020; fee undisclosed)• Xnor.ai (2020; $200 million)• AI Music (2022; fee undisclosed)• WaveOne (2023; fee undisclosed)• Cohere (2021 onwards)• Anthropic (2023)• Character.ai (2023)• Midjourney (2023)• Runway (2023)• Anthropic (2023;
Up to $2 billion)• Runway (2023;
amount undisclosed)• Hugging Face (2023,
amount undisclosed)• DeepMind (2014; $400 million)• Moodstocks (2016; fee undisclosed)• Halli Labs (2017; fee undisclosed)• Onward (2018; fee undisclosed)• Alter (2022; $100 million)• Phiar (2022; fee undisclosed)• Microsoft (2023)• Atlas ML (2019; fee undisclosed)• Bloomsbury AI (2018; $30 million)• AI.Reverie (2021; fee undisclosed)• OpenAI (2019 onwards)• Meta (2023)• OpenAI (2019, 2021,
2023; over $10 billion)• Builder.ai (2023;
amount undisclosed)• Inflection AI (2023;
amount undisclosed)• Swiftkey (2016; $250 million)• Maluuba (2017; $140 million)• Bonsai (2018; fee undisclosed)• GitHub (2018; $7.5 billion)• Lobe (2018; fee undisclosed)• Nuance (2022; $20 billion)

19

open model has led to a flurry of experimentation on top
of Meta’s underlying technology. Most recently, Metaannounced that it would be building on this by making
its next model, Llama 2, available free to businesses andresearchers on what it claims is an “open-source” basis,
although this is disputed (more on this below).32
At first glance, these initiatives from Meta and Amazon
may look like a tacit acknowledgement of the need tocoexist alongside, rather than dominate, a flourishingand diverse AI ecosystem. But they should be seen forwhat they really are: attempts to control the technology
by owning the underlying infrastructure on which AI
depends. Just as the gatekeeper corporations’ control ofapp stores and online marketplaces allows them to exploit
countless independent sellers and small businesses, theircontrol of the cloud infrastructure, foundation models,and marketplaces needed to design, train, host, and run
AI applications will give them similar power to steer thetechnology in a direction conducive to their interests.
Of the dominant gatekeepers, the one major outlier
thus far is Apple. So far, unlike Google, Meta, Amazon,and Microsoft, Apple has not announced any AI models
or applications of its own, nor major partnerships withprominent AI startups. In many ways this is unsurprising,
given that Apple’s core strengths lie in hardware andoperating systems, not applications. Apple also recently
announced the launch of its cutting-edge Vision Pro
virtual reality headset, suggesting it is betting on adifferent area of emerging technology.
But neither can Apple cannot be written off at thisearly stage. Quite the contrary. Apple has acquired
more AI startups than any other tech giant (see Figure3) and has recently been hiring actively for positions
focused on large language models.33 Its dominance in
hardware, operating systems, and app stores will surely
allow it to exert significant power over how consumers
and businesses interact with AI models and applications,
including extracting value from those interactions.

COMPETITION IS NOT ONE CLICK AWAY
It should be clear by now that there is little prospect of
independent AI companies surviving, let alone thriving,
without becoming dependent on the largest gatekeeper
corporations in one way or another. But there are otherarguments used to claim that AI is competitive, which
also require close examination.
One is that “open-source” AI will increasingly exert realcompetitive pressure on the dominant platform monopolies. An internal Google memo, leaked in May of thisyear, claimed that when it comes to AI models, Google
and OpenAI have “no moat” and are set to be usurped byopen-source alternatives, an argument echoed by others.34
Yet as has been pointed out by numerous observers, theopen-source AI landscape is not as open as it seems.35 36
As we saw earlier, Llama, one of the most popular models
for open AI innovation, is owned by tech giant Meta.
And indeed, the open-source community has alreadymade clear that given the restrictions Meta has placed on
Llama’s commercial use, the release terms do not meet
the widely-accepted definition of open source developedby the Open Source Initiative.37 Furthermore, Meta
has suggested that it may look to further substantiallylimit access in the future to a small pool of partners with“strong credentials” based on safety risks, which the tech
giant would be able to unilaterally define.38
In fact, a move from open to closed AI models is alreadywell underway. While never truly open source,
OpenAI previously provided public information on its
large language models, information that has been helpful
to companies and developers working on their own AI
models. Yet upon on the launch of its latest GPT-4 model, OpenAI announced that it would not be publishing
any details about the “architecture, hardware, training
compute, dataset construction, training method, orsimilar,” citing the “competitive landscape” and “safety
implications.”39

20

With Meta already failing to meet the definition of opensource, OpenAI shutting off what limited information it
provided, Google’s model being closed to start with, andother players dependent on the giants’ support for theirsurvival, the prospects of a real competitive challenge
from open source models look slim. While there may besome limited fine-tuning of models developed and released by the largest platform monopolies, these modest
third-party efforts will struggle to keep up with developments taking place inside these corporations’ own lavishly
watered walled gardens. Meanwhile, the dependence of
open-source developers on the technological capabilitiesof the dominant gatekeeper corporations means that thelatter will be in a position to snuff out any real competition from the former.
Additionally, openness – if not open source – in AI
models can actually serve to reinforce, rather thanchallenge, dominant platforms’ power. As a recent paper
by David Gray Widder, Sarah Myers West, and Meredith
Whittaker explains, while “some tech companies initiallyfought open-source, seeing it as a threat to their own
proprietary offerings, more recently these companies
have tended to embrace it as a mechanism that can allow
them to entrench dominance by setting standards of development while benefiting from the free labor of open
source contributors.” The authors argue that while truly
open-source AI does exist, the term is frequently usedinaccurately or misleadingly, and is increasingly being
instrumentalized by large companies to entrench their
dominance and fend off regulation.40
The trajectory of other, mature digital markets provides an indication of what we might expect to see with
open-source models in the context of AI. While Linux in
PC operating systems, Mozilla Firefox in web browsers,
and Android in mobile operating systems (to name a few
better known examples) all promised to bring a new levelof openness and transparency to key technologies, all –in different ways – were unable to challenge or supersede
the tech giants.

Both Linux and Firefox have received praise for theirreliability, customizability, and security, yet they havestruggled to overcome the vastly superior financial resources of the largest gatekeeper corporations, let alone
the ability of these corporations to exploit their controlof wider ecosystems to lock in users and self-preference
their own products. As for Android, while initially createdby a consortium of developers, it is Google’s proprietaryversion that has come to dominate the market, alongwith the closed apps Google has built on top of it. Google
achieved this dominance in part through anti-competitive practices, including incentivizing hardware manufacturers to avoid installing versions of Android not
approved by Google.41
While the past is never a perfect guide to the future, thefailure to date of open-source solutions to take on dominant platforms across a range of markets should makeus wary of claims that the wall surrounding the dominant gatekeeper corporations will soon be breached byopen-source solutions. This isn’t to say that we shouldn’t
take steps to promote genuine open source efforts, andprevent attempts to quash them. But alone, they are
unlikely to meet the scale of the monopoly threat.
Another argument is that fierce rivalry between thelargest platform monopolies is proof of a healthy andcompetitive market. Microsoft’s campaign to dislodge
Google from its dominant position in search using
OpenAI’s technology is one example, with excited
predictions that Bing could supersede Google Searchwithin a couple of years.42 The tech giants are deploying
AI against each other in other areas too, including online
advertising, office software, e-commerce, cloud computing, social media, and more.
But it is important not to confuse limited competition
on a handful of fronts among a handful of dominant
platforms with a truly diverse digital ecosystem in whichlarge corporations control few if any key chokepoints. In
this hypothetical world, an ever-changing cast of incum-

21bents and challengers perpetually vie for supremacy, withno single platform succeeding in gaining a permanentchokehold over any particular market or technology.
Seen from this perspective, Microsoft replacing Google
as the dominant search provider, or coexisting alongside
Google in a search duopoly, would hardly be an improvement on the status quo. The search market would remain
highly concentrated, and no genuinely new player would
have succeeded in breaking into the cozy club dominated by the same companies over many years. The same
could be said of Apple or Amazon gaining more of thedigital advertising pie, or Meta launching a popular online
shopping platform.
Indeed, a recent analysis of trends in U.S. tech stocks
since the 1960s found that while in the past, each newtechnological wave brought fresh players to the fore,
since the early 2000s a few massive corporations havesucceeded in entrenching themselves at the top. Thisis reflected in the fact that today, the five largest techcompanies represent roughly 20% of the value of theentire S&P 500, compared to around 1.3% in 2000. The
average age of the market leaders is creeping up quickly,
from around 12 years in the early 2000s to closer to
40 today.43 In the case of AI, dominance by these same
corporations thus far looks all but inevitable.
What’s more, this burst of AI-fueled competition
between the tech giants is likely to gradually settle into
established “spheres of influences” mirroring those weare familiar with from today’s tech landscape. Over thelast 15 years, the largest platform monopolies have showna strong preference for avoiding direct conflict with one
another’s key bastions of power, in favor of peaceful coexistence. Examples of this preference in action include
the tens of billions of dollars Google pays Apple every
year to remain the default search engine on iPhones andMacs, and the ‘Jedi Blue’ agreement between Google
and Meta designed to strangle competition to theirdigital advertising duopoly. Meanwhile Apple and Meta,
which do not have their own cloud computing arms,
depend on Google, Microsoft, Amazon, and Nvidia for
their needs.
Such cartel-like behavior even includes markets thatshould be easy to challenge; some years back, both Google and Apple abandoned efforts to break into the sale ofbooks and compete head-on with Amazon, after initiallymaking large investments in that business. This dynamic
is already becoming evident in AI too, with Meta recently announcing Microsoft as its preferred partner for therollout of its Llama 2 model.44
Finally, as discussed in the next section, the sheer scaleof the largest gatekeepers is likely to magnify the harms
arising from AI, even more so when those platforms
are aggressively competing against each other. A morecompetitive AI landscape, as measured by the pluralityof actors involved, would help dilute the tech giants’power. But unregulated competition in a market in whichpowerful corporations already enjoy immense power overtheir customers is highly likely to result not in better services for the companies that depend on the giants, but inmore efficient exploitation of those customers.45

22

I I I : A I R E I N F O R C E S E X I S T I N G
M O N O P O LY P O W E R A N D H A R M S
The previous section demonstrated how success in AI
today is a function of pre-existing concentration in thetechnology supply chain. In this section, we explain why
AI is set to reinforce this logic of concentration further,and amplify monopoly harms.

MONOPOLISTS ARE USING AI TO FURTHERENTRENCH THEIR EXISTING DOMINANCE
The combination of monopoly power and AI has alreadyunleashed a vicious cycle in which concentration begets
more concentration. The dominant gatekeeper monopolies are exploiting AI to reinforce and expand many of thealready existing concentrations of economic power andtechnological resources they control. The basic process
is relatively simple. The largest models – backed by themost data, computing power, and expertise – produce
the best results. This in turn attracts the most users, entrenching the existing dominance and profitability of thecorporations deploying them. The winners are then able
to extract higher profits and collect more and more data,
which empower them to further refine their AI models
and applications, every day digging the moats aroundtheir monopolies that much wider and deeper.

to gravitate towards the AI models and applications thatare most effective at responding to their prompts, which inturn will be those trained on the largest amounts of data andcomputing power.46 And as with social media platforms or
mobile operating systems, consumers and businesses may
prefer to use the same AI tools and services as their peers,particularly if those tools are promoted by and integrated
with systems those users are already locked into.
For example, Amazon could exploit its powerful positionin cloud computing infrastructure and services to makeit easier for businesses to collaborate internally andwith other companies using its preferred AI tools within
the AWS ecosystem (while simultaneously making it
harder to collaborate outside it). Meanwhile Microsoft
and OpenAI could seek to limit AI-driven collaboration
outside of Microsoft’s vast array of products and services(including chatbots, email, operating systems, cloudcomputing, and office software).

This dynamic is not new. Control over data has long been amajor factor in how today’s dominant gatekeeper corporations were able to establish and fortify their monopolies over online activities including search, social media,
advertising, e-commerce, and cloud computing. But AI
risks taking this logic of concentration to a new and more
extreme level.

It is important to note that network effects are notinherently negative, as there are obvious benefits to bothindividuals and the public as a whole when many people
use the same service. But left unchecked, this dynamic
is likely to result in a very small number of corporations
holding extreme power over how individual businesses andcitizens interact with and are acted upon by AI. Relatedly,
absent broad rules requiring the gatekeeper corporations
to provide the same quality service to every customer,
another result is that these giant corporations are free toengage in politically and economically dangerous forms ofmanipulation and extortion of each individual user.

Large-scale AI systems may also come to exhibit the
network effects that characterize many of the markets
mentioned above. As with search engines, people are likely

Should the present concentration of control over AI
remain unchallenged, or get even worse, a handful of
all-powerful firms would be able to influence the work,

23

Figure 4: The self-reinforcing
cycle of AI concentration

Superior access todata, computing power,
and expertise

Growing userbase, greater
profitability

Foundation model
outperforms rivals

experiences, and fates of hundreds of millions or even

The dominant gatekeepers and their supporters pushbillions of people simply by tweaking the parameters ofback against these arguments by claiming that hightheir systems. By controlling AI models themselves, or

degrees of concentration in the AI supply chain create

the cloud platforms upon which AI models and appli-a host of benefits, from economies of scale and lower

cations are built and accessed, these gatekeepers can,prices for consumers, to greater safety and increased

from one moment to the next, suddenly change thecompetitiveness with China. But the past two decades

information people receive in response to their queries,

of technological development have shown us that theiror the services provided to small businesses. They can doservices are anything but free or low-cost, and in fact

so across their platforms, in ways that affect every user

are designed to drive up the prices users pay across thethe same. Or they can do so in ways that target specificinternet.47 Meanwhile the largest gatekeeper corpora-

individual people and companies, either arbitrarily, out oftions have completely failed to live up to their promisesa desire to extract more money from these captive users,

on safety, and in recent months have been abandoning

or in retribution for public opposition.

many of their few modest efforts to protect users. And
it should be clear that competing with autocratic China

24

cannot entail allowing tech monopolies to further disrupt
democracy at home.

STEERING DEBATE AND
INNOVATION THROUGH ECONOMIC
AND POLITICAL POWER
The present efforts by the gatekeepers to dominate AI
are made possible by the enormous financial and politicaladvantages they have built up by exploiting their monopoly power. Not only do they already dominate almost all
key links in the AI services and technology supply chains,
they also are shaping the policy debate on whether andhow to regulate AI in ways that protect and promote
their existing interests. This is largely thanks to their vast
lobbying and influence systems,48 as well as to a carefully
curated public narrative that their technical expertise isfree from conflicts of interest49 and that the technologies they control evolve in certain directions naturally,
even inevitably,50 largely outside the influence of power,
profit, and politics.
In the U.S., OpenAI, Microsoft, Google, and others
have publicly and repeatedly called for regulation while
simultaneously attempting to steer the focus of any
actual potential regulation away from existing harms (including those caused by their existing suite of platforms,
products, and business models) towards abstract andspeculative future threats. OpenAI CEO Sam Altman,for example, expressed strong support for regulation in
his testimony before the U.S. Congress, only to later
threaten to pull OpenAI’s services out of Europe if the
EU’s AI Act proved too burdensome.51 OpenAI has also
lobbied aggressively behind the scenes to water down the
EU legislation.52
But the political influence of the platform monopolies
is not only about shaping ongoing legislative efforts. It isalso about ideological capture of key sources of information and advice for policymakers. As recently revealed by
Politico,53 a powerful network of billionaires and corpora-tions is pouring tens of millions of dollars into promoting
the idea that AI poses a variety of apocalyptic threats tosociety, and that policy should focus on these long-term
threats as opposed to present-day harms. To back thiseffort, this network is paying to place staffers with “technical expertise” in the offices of key senators and House
committees, federal agencies, and think tanks.
Through their monopoly power, the tech giants are also ina position to determine the direction, speed, and nature of
AI innovation. A useful case study is the role Google andFacebook have played in restructuring the entire news andadvertising industries to serve their interests.54 Over the
past two decades, Google aggressively acquired the businesses and technologies necessary to capture almost complete control of the underlying infrastructure publishers
and advertisers use to do business with one another. Then
Google, with Facebook following close behind, exploited
this control to divert scores of billions of dollars away fromnews publishers and broadcasters and into their own vaults,
simultaneously starving news media in the United States
and around the world and concentrating enormous political and economic power.55
The two corporations have used countless tools toachieve this, including Google’s Accelerated Mobile Pages, Google News, Google Ad Manager, Facebook Videoand Instant Articles, and Facebook Ad Network. But
the result is that “innovation” has been used to reinforce
their monopoly power in search, social media, and advertising at the expense of other actors in the supply chain.
For example, a recent close study of Google’s business in
Switzerland concluded that news content contributes toaround 40% of the corporation’s entire revenues in thatcountry, indicating the extent to which the company’scontrol over platforms and advertising infrastructure has
diverted advertising dollars away from news media.56
It isn’t difficult to imagine how the tech giants – who already have far more power over AI than they initially did
over other industries – will seek to use AI technologies in25

ways that reinforce their existing areas of dominance. Instead of harnessing the technology to solve urgent social
challenges and improve people’s lives, the digital gatekeepers will be far more inclined to use it to entrench
their existing monopolies and to put the pursuit of shortterm commercial benefit over the economic and political
wellbeing of our societies. Their influence over how AI
innovation unfolds is likely to extend to all corners of
the realms they control. Through their control of key
inputs such as cloud computing, foundation models, andprogramming tools – the “upstream” infrastructure powering the rest of the AI supply chain – the gatekeepers
will also have the power to define innovation for many, if
not most, of the industries developed by entrepreneurs
operating “downstream.”

AI IS AMPLIFYING TODAY’S MONOPOLYHARMS, AND IS CREATING NEW ONESThe chokehold that a few corporations possess over key
digital markets – including online search, video streaming, social media, and digital advertising – has resulted in
an extensively documented array of threats and harmsthat legislators and regulators have struggled to understand and master. This ranges from democracy-disrupting disinformation57 and the exploitation and manipulation of children,58 to the mass expropriation of content
creators and small businesses.59
With these same monopolies driving the latest AI boom
both directly and through their control over other actors,
we should not be surprised to see these same harms andthreats become even more extensive and disruptive inthe days and years ahead.
Already, there have been plenty of warnings about how
the race to profit is leading even these fantastically richcorporations into reckless and dangerous behaviors andactions. Take the case of Timnit Gebru, a former Google
researcher who has compared the AI boom to a “goldrush” and was fired from the corporation for suggestingthat the drive towards ever-larger models was coming at
the expense of safety.60 In the event, Gebru’s warnings
proved to be only too prescient. This was made especiallyclear by Google’s rushed launch of its Bard chatbot earlier this year, which was heavily criticized by employees for
putting the pursuit of profit above ethical considerations,and where the corporation reportedly overruled warnings from its safety team that Bard could cause serious
harm.61 Much the same apparently occurred inside
Microsoft, which ignored similar warnings from OpenAI
about integrating its technology into its Bing searchengine without full and careful testing.62
The rest of this section explores the different ways inwhich monopoly power in AI reinforces existing monopoly harms, both now and in the future.
DISTORTION OF PUBLIC DEBATE
Over the past two decades, the platform monopolies haverestructured critical communications and commercial
systems more dramatically than in almost any previous
period in history. They have used acquisitions and othermonopolistic strategies to concentrate these activities
on the platforms they own and control, then exploited
their power to radically alter how we communicate witheach other and consume information. While new forms ofcommunications technology can provide many benefits toindividuals and society as a whole, our failure to carefully
regulate the power and behaviors of these corporations
left them free to exploit these new technologies in waysthat have disrupted basic democratic and social institutions
and the checks and balances governing them. Specificharms include diversion of advertising revenue and readers
away from publishers, distortion and censorship of publicconversation and debate, dissemination of extreme, false,and misleading content, and degradation of essential communications services.
Generative AI, reinforced by the monopoly power of thedominant gatekeeper platforms, appears already to be26

amplifying these harms in several ways.
Perhaps most dramatically, generative AI tools are
reducing the cost and time needed to produce false andmisleading information.63 Chatbots like ChatGPT are
already demonstrating an almost unlimited capacity
to generate false information and narratives on
various topics. To cite just one example, researchers at
NewsGuard successfully prompted ChatGPT to claim, inthe voice of anti-vaccine advocate Joseph Mercola, that
Pfizer secretly added organic compounds to vaccines
to lower health risks for children.64 The capabilities ofgenerative AI models and tools are so wide-ranging thatthey are already being used to produce pornographic
deep fake videos of real women (without theirconsent)65 and fake videos of terrorist attacks on criticalinfrastructure.66 The models have also demonstrated aworrying ability to fabricate sources and carefully embedthem in convincing prose.
Of course, the problem of technology being used tocreate and disseminate false and problematic content is
not new. Conspiracy theorists, propagandists, and otherdistorters of the truth have long been early adopters of
new technologies to spread false information. But generative AI is taking an existing crisis of trust in news andmaking it dramatically more acute by suddenly and drastically cutting the time, cost, and skill needed to createsuch misinformation. Combined with the systemic scaleof the social networking and communications platforms
controlled by these same corporations, and their generalized failure to prevent their customers from disseminating harmful content on those networks, generative AI
is swiftly destroying our ability to trust what we read andview on our screens.
Generative AI also poses a clear threat to the survival ofa financially independent press, building on the damage
already inflicted on news media by the dominance andbusiness models of Google, Facebook, and Twitter. This isbecause when ChatGPT, Bard, Bing, and other chatbots
scrape and aggregate text from the web to learn to gen-erate the best answers to user prompts, they are doing
so by processing information largely provided by news
organizations, blogs, and independent websites.67 Theiranswers also greatly reduce the need to go to the original
source, thus preventing publishers from receiving traffic
and ultimately compensation for their content.
To make matters worse, Google is in the process of incorporating detailed AI-generated results into its searchengine – the so-called Search Generative Experience –which will further reduce the flow of traffic and revenues
to third-party websites while entrenching even deeper
Google’s advertising dominance and monopoly power.68
If websites and publishers respond by restricting accessand putting up paywalls (as indeed they already are),then the amount of reliable and up-to-date information
available to the public will shrink further, while results
produced by generative AI tools will continue to degrade
the overall quality of what is available.69 Meanwhile,plans by Google and Meta to reinforce their advertising
dominance through generative AI threaten to further
weaken the financial health of the media industry, while
also making it yet harder for individuals to find and sharetrustworthy information.
EVER MORE EXTREME TARGETEDMANIPUL ATION AND DISCRIMINATION
Surveillance advertising, whereby dominant techcompanies intrusively collect huge quantities of sensitive
personal data in order to more accurately target
information and advertising at users, accounts for thevast majority of revenues for Google and Meta. Today,
most digital advertising across the web is underpinnedby platforms and tools run by the two giants, and to a
growing degree Amazon and Apple. Amazon’s advertisingbusiness, which consists primarily of ads appearing within
its search results, generated $38 billion in revenues in
2022 and is expected to continue growing.70
To protect this business model – which has jarringly27disrupted carefully crafted and long-established political and economic balances within the news media andadvertising industries – the tech giants have spent muchof the last 15 years constructing a narrative that disguises
the invasive harvesting of personal data as a harmlessand inevitable part of a thriving digital economy.71 The
commercial imperative to maximize users’ exposure
to targeted advertising has also been blamed for theplatforms’ failure to develop or even enforce effective
content moderation practices. The basic idea is that tokeep users online and collect as much data as possible,
the gatekeeper corporations have an interest in promoting inflammatory and viral material over more nuanced
and less addictive types of content.
The data-intensive nature of large-scale AI models andgenerative AI tools is already resulting in new forms andnew degrees of violation of user privacy. A recent classaction lawsuit launched in California alleges that OpenAI
violated the privacy of millions of people when it harvested their social media comments, blog posts, and othercontent to train its models.72 The company is also being
investigated by the Federal Trade Commission (FTC)for its data security practices.73 In Europe, ChatGPT was
temporarily banned in Italy for leaking users’ private conversations and payment information, while data privacy
and consumer protection regulators in France, Germany,and Hungary have launched their own probes into thechatbot’s compliance with the EU’s General Data Protection Regulation (GDPR) and other laws.
The integration of generative AI with the surveillance-driven business models of Google, Meta, andothers is now taking this targeted manipulation toanother level entirely. Combined with the large swathsof personal and non-personal data already controlled bythese corporations – including data on what promotionaland manipulative tactics have worked most effectively so
far on each particular individual – generative AI is em-

powering the gatekeeper corporations and advertisers tocreate and disseminate content designed to manipulate
individual customers with extreme precision, while also
increasing the time they spend online and on particularonline websites and platforms.74 This in turn is reinforcing
the tech giants’ efforts to gather, store, and use personaldata, further undermining our privacy. Researchers predict that this one-two punch of monopolistic scale andprecise personalized manipulation will worsen the online
addiction, anxiety, eating disorders, and other mental
health issues associated with social media use.75 76
More concretely, Google and Meta are already rollingout generative AI-powered services to advertisers.77 Thismeans that advertisers, instead of having to rely on a set
number of images and captions for an advertising campaign, will be able to prompt AI to automatically generate
images and text tailored to distinct small audiences andeven particular individuals.78 Generative AI thus threatens
to further entrench the market dominance of the digitaladvertising giants, increasing their ability to target andmanipulate users with a level of precision stratosphericallybeyond the reach of potential competitors.
Even if generative AI-powered advertising ends up not
being as novel or effective as its purveyors claim, thesheer dominance of Google, Meta, and Amazon over
online advertising means that these models are likely
to become widespread regardless. This combination of
dominance and inefficiency – and even outright sloppiness and destructiveness – is by no means new. Despite
their ubiquity across today’s digital advertising supply
chain, there is little evidence that Google and Meta’s
platforms benefit publishers and advertisers, and plentyof evidence to the contrary. A July 2023 study by advertising research group Adalytics found that around 80%
of Google’s video-ad placements on third-party websites
violated the corporation’s own standards.79 Facebook has
repeatedly been accused of inflating the size of audiences28

exposed to its ads80 and allowing scam ads to proliferateon its networks.81 As one former advertising executive
turned critic recently put it, the industry’s incentive
structures are “fundamentally flawed” and its operating
models “inherently conflicted.”82
Generative AI-powered advertising also risks amplifying
discrimination based on ethnicity, sex, religion, and otherprotected categories, including in relation to advertising for housing, credit, and employment opportunities.
We have already seen how ad-targeting tools, as they
are used today, can cause substantial harm to vulnerable communities. One well-known example is a JusticeDepartment investigation of Facebook that found thatthe corporation allowed the placement of discriminatory
housing ads that violated federal law.83 A similar study by

AlgorithmWatch showed that Facebook relies on crude
gender stereotypes when deciding who to target with job
ads.84 It is not hard to see how generative AI, based on its
ability to expedite hyper-targeted ads across these same
flawed models, will make such discrimination far moreprevalent and dangerous.
The same AI technologies that are empowering Google,
Meta, and other dominant gatekeeper corporations toturbocharge their use of hyper targeted ads are also
amplifying their ability to exploit their existing abilities
to manipulate the behavior of individuals in a variety ofother ways. This is true both in terms of what people buy
and read, and how they vote and otherwise engage inpolitical activity.

29

NON-DISCRIMINATION RULES AS ANTI-MONOPOLY POLICY: A SHORT HISTORY
A key contributor to the digital gatekeepers’ power is thefailure of U.S. and other regulators to apply traditionalcommon carrier-style non-discrimination rules tothe online gatekeepers, despite the fact that these
corporations control many essential commercial andcommunications platforms. Until the 1980s, such rules
provided perhaps the single-most important foundation
for preventing the consolidation of power and control
in the hands of the corporations that controlled vital
networks. Such rules have traditionally provided the mainbasis for the protection of the properties of independent
businesses and the individuals who depend on these
services, and hence have also played a fundamental rolein the protection of human liberty and democracy.
A useful recent example of how these laws and rules
are designed to work is the ‘net neutrality’ regulations
applied to internet service providers in the United Statesby the Federal Communications Commission in 2015.
These rules were designed to ensure that executives
at these corporations were not left free to exercise
arbitrary power over the businesses that relied on
their services, such as via threats to cut off, reduce, orotherwise alter the services and access they provide to
specific customers. (Although the Trump Administration
overturned these regulations in 2018, FCC Chair Jessica
Rosenworcel has made clear that restoring them is a toppriority.)85
The imposition of net neutrality rules on the backbone
physical infrastructure of the internet was simply thelatest example of a traditional approach to regulating thepower of corporations that control essential services. In
the United States, Congress has applied such regulation
to every essential communications and commercial
platform in the nation’s history, including the telephone,telegraph, railroad, trucking, ocean shipping industries,and even the Postal Service. Through a related set oflaws, U.S. policymakers also applied these same principles
and rules to services such as retailing and warehousing.
The basic idea of such regulation is that every individual
has a right to equal treatment by every provider of
essential services, much in the same way that individuals
have a right to equal justice before the state. A closely
related idea behind such rules is the understanding that
failure to enforce such equal access allows powerfulgatekeeper corporations to all but routinely extract wealthand political favors from the businesses and people who
rely on them to get to market – through the simple threat
to close the gate to the market to anyone who does notbend to their wishes. Traditionally, such actions wereviewed as extortionary in nature. Worse, policymakers
believed that such relationships resulted in extremely
dangerous concentrations of political power in the handsof gatekeeper corporations.
In the United States, the single most important example
of such regulation is the Interstate Commerce Act (ICA)of 1887, through which the federal government appliedsuch rules to the railroad corporations. In 1910, Congressthen extended the ICA to cover also the telegraph,
telephone, and wireless industries, as well as otheressential services. The relative importance of such nondiscrimination regulation is made clear by the fact that it
was only three years after the ICA that Congress passed
the Sherman Antitrust Act. Indeed, Senator Sherman
himself, in the debate over the law that bears his name,reiterated the fundamental role of non-discrimination
rules when he said: “It is the right of every man to work,
labor, and produce in any lawful vocation and to transport
his production on equal terms and conditions and under likecircumstances. This is industrial liberty, and lies at thefoundation of the equality of all rights and privileges.”(emphasis added).

30

In the absence of strict non-discrimination requirements
imposed on their operations (see table above) Google,
Amazon, Meta, and the other platform monopolists built
business models designed specifically to extort individual
people and businesses in their capacities as sellers andpublishers. In doing so, they have routinized behavior
that would have been recognized as completely illegal as
recently as the 1990s.
In addition, Google, Amazon, Meta, and the otherplatform monopolists have done something that previous generations of network monopolists could hardly
imagine, which is to apply these same systems of tailored
manipulation and discrimination to individual consumers.
These gatekeeper corporations were able to do so by taking advantage of their rapidly growing abilities to gather,store, and manage data about the interests, behaviors,
and actions of individual people.
Perhaps the most immediately useful explanation for
how such a system works was provided by Hal Varian,
now the chief economist at Google. In a paper he cowrote in 2001, titled “Conditioning Prices on Purchase
History,” Varian described how the technologies andstructures of online commerce were making it easierfor sellers to charge different people different prices forthe same product.86 “The rapid advance in information
technology now makes it feasible for sellers to condition
their price offers on consumers’ prior purchase behavior,” Varian and his co-author wrote. Indeed, not only
was such targeting now feasible, it was also “profitable toengage in this form of price discrimination.”
Varian then provided a blunt recommendation to online sellers. “[I]f enough customers are myopic, or thecosts of anonymizing technologies are too high… sellers
will want to condition pricing on purchase history.” Forbuyers his warning was even more blunt. “[P]urchasing at
a high price … guarantees that [you, the consumer] will
face a high price in the future.”

From the point of view of the individual buyer or customer, the overarching result is a system of economic
exploitation and political manipulation that goes far beyond so-called ‘dynamic’ pricing models, whereby prices
automatically adjust supposedly in response to fluctuations in supply and demand.
In the late 19th century, railroad bosses spoke of how
they used discriminatory pricing strategies to charge
businesses that relied on their services “all that the traffic
will bear.” Today, Google, Amazon, Uber, Ticketmaster,and a fast growing array of other corporations use amped
up versions of these same practices to extract all that theindividual consumer is willing to pay, increasingly throughautomated systems carefully designed to identify withextreme precision the size of this consumer surplus.
EXPLOITATION AND MANIPUL ATION OF
SELLERS AND PUBLISHERS
The introduction of AI technologies is already rapidly increasing the ability of the gatekeeper platforms to more
effectively manipulate sellers and publishers in much thesame way it is boosting the ability of these corporations
to manipulate and exploit the individual consumer. The
FTC’s new case against Amazon provides important
insight into how this power is being put to work in today’s
digital political economy.87
But it is important also to understand the political effectsof such power applied over long periods of time by theseimmensely powerful gatekeeper corporations, even toother extremely large and influential corporations, suchas NewsCorp, the New York Times, Penguin Random
House, and Paramount, as well as otherwise all-dominant
manufacturers such as Procter & Gamble.
The tech giants have repeatedly shown us that their core
goal is not merely to shut out challengers, nor is it mainly
even to lock users and small businesses into using theirproducts and services. Nor is it simply to exploit their31

position as gatekeepers to extract wealth, properties, andideas from the businesses, contractors, and individuals
who depend on their platforms to get to market and to
exchange their ideas. Rather it is also to impose various
forms of politically useful influence and control over thebusinesses and individuals who depend on them to get tomarket.
The key political fact of political economics today is thatthe monopolization of essential commercial and communications platforms – as well as everything from search
and mapping engines, social media platforms, digitaladvertising systems, e-commerce platforms, email andoperating systems, app stores, cloud computing, and now
AI infrastructure – has given this handful of corporations the power to decide who gets access to the market
and on what terms. This control of these gateways gives
Amazon, Apple, Google, and other platforms the ability
to discriminate based on price and other factors, set theterms and conditions governing how they do business,and degrade or cut off access at will.88
This gatekeeper position also empowers these corporations
to extract political value from these captive customers.
One of the most obvious such benefits is the ability to stifle criticism from any seller or publisher who might choose
to complain in public, through the threat of direct andperhaps commercially fatal retribution for speaking up.89
Perhaps the most useful model for understanding how
such systems of control threaten our democracy andfundamental liberties is to focus on how Google, Facebook, Amazon, and now Twitter under Elon Musk have
exploited their gatekeeper positions to cut off revenue
and readers from even the most powerful news andinformation publishers in the world.
The first major instance of one of these platforms exploiting its power to cut off a publisher’s access to market
came in 2014 when Amazon stopped selling books

published by Hachette, as part of an effort to force thatcompany to pay more for Amazon’s services.90 A second example is Google’s reported use of its control overGmail to suppress the campaign literature of particular
U.S. presidential candidates, while simultaneously boosting the advertising of their rivals.91 Another example wasthe collapse of traffic from Facebook to Wired in February 2018, after that magazine published an article criticalof Mark Zuckerberg.92 More recently, Facebook cut off
the ability of Canadians to share news with one anotherand threatened to do the same to Californians.93
Some of the most egregious examples have happened
on Twitter in the year since Musk took full control. This
includes the cutoff of access of individual journalists,
researchers, entire news publications, and research andadvocacy organizations.94
The ultimate political result of such a system – combining control over the gate with a de facto license to open
or close the gate at will – was well captured by Wired’s
then executive editor Nicholas Thompson in 2018.
“Every publisher knows that, at best, they are sharecroppers on Facebook’s massive industrial farm,” he wrote.
“If Facebook wanted to, it could quietly turn any numberof dials that would harm a publisher, by manipulating its
traffic, its ad network, or its readers.”95
The political effects of such exercises of power are
obvious, which is to make every publisher today – andincreasingly each individual employee of these businesses – think twice about criticizing or even questioning thepower of Google, Meta, Amazon, or any other corporation that enjoys the power to arbitrarily, at a moment’s
whim, cut them off from revenue, readers, and markets.
And now AI is dramatically amplifying this existing abilityof these already immensely powerful gatekeepers to suppress, censor, or simply choke off anyone whose actionsor speech does not please them.

32

EXCLUSION OF RIVALS AND
UNDERMINING OF INNOVATIONThis same gatekeeping power also gives these dominant
corporations the ability to suppress, exclude, and eliminate potential rivals through a variety of monopolistic
tactics. While this is not the place for an exhaustive
review, such practices include tying or bundling different products and services together; self-preferencing
proprietary services through control over multiple layersof the supply chain; using data gathered from sellers tocompete against them; restricting or denying access todata, infrastructure, and other critical inputs; and using
acquisitions to extinguish or absorb future competitors.
Notable historical examples of such tactics include Microsoft’s attempt to choke off rival internet browser Netscape by the bundling its own browser with its alreadydominant operating system,96 Google’s self-preferencing
of its own services across the highly lucrative ad-tech
supply chain,97 Amazon’s use of seller data to benefit
its own retail business,98 and Facebook’s acquisitions ofpotential rivals Instagram and WhatsApp.
AI technologies have already empowered dominant gatekeepers to further perfect these practices. When it comesto tying, bundling, and self-preferencing – all of which
are based on leveraging control over multiple markets andproducts to disadvantage rivals – the already existing power of Microsoft, Google, Apple, Meta, and Amazon acrossdifferent product markets (including search, advertising,operating systems, cloud, and hardware) presents themwith a myriad of opportunities both to extend their dominance over AI, and to simultaneously use AI to reinforce
their existing dominance elsewhere.
Microsoft, for instance, has integrated OpenAI’s technology into its Edge browser and Bing search engine,99
and is beginning to do the same in relation to its dominant Office software, Windows operating system, Outlook email service, Azure cloud platform, and more. The

same applies to Google, which is using AI to strengthen
the moat surrounding its ubiquitous email, maps, shopping, and other services. Indeed, Google has already announced that only developers who use its cloud platform
will be able to access the Pathways Language Model, oneof its most powerful AI tools.100 And it is in the processof integrating its Bard chatbot into its search engine,
simultaneously reinforcing its search monopoly while also
locking users into its AI tools.
As discussed earlier, Amazon is attempting to position
itself as the platform for AI innovation by hosting AI
models and services directly on its cloud infrastructure.
Should Amazon succeed in securing this position, it could
use this to exert power over and extract value from businesses dependent on that infrastructure, just as it doeswith sellers on its marketplace. It will also have the ability
and incentive to self-preference its own AI models andtools and those produced by its partners and investees,such as Anthropic, and to copy or disadvantage potential
competitive threats.
Another area where the largest gatekeepers are in astrong position to exploit their market power is throughtheir control over the crucial inputs needed to train AI
models, particularly computing power and data. This
is not merely hypothetical. An early example of this is
Microsoft’s recent threat to cut off access to its Bing
search-index data to smaller search engines such as
DuckDuckGo that use this data to train their own AI
applications.101
But in many, if not most, cases the behavior is likely to
be more subtle; for example, an incumbent providing
access to its AI capabilities could limit or downgrade thisaccess in order to protect its market share. A recent
study by the UK’s communications regulator, Ofcom,
found that leading cloud players use technical restrictions on interoperability to limit how well their platforms
work with services offered by rivals, and that access often
starts off open but narrows over time.102

33

THE THREAT TO CREATORSAND CREATIVE PROPERT Y
For more than two decades, the authors and publishersof news, books, music, photography, and other forms ofeasily digitizable materials have fought with little success to protect their copyrighted properties and livelihoods from the power of the platform monopolists. This
includes a long list of lawsuits against Google and othercorporations for not preventing individuals and businesses from sharing copyrighted materials on platforms ranging from YouTube to Google News without permission.
Such transmission of data all but eliminates the abilityof copyright owners to get paid for their work, while also
removing most of the incentives for consumers to pay
for what they read, watch, and listen to.
Now the rapid rollout of generative AI is taking thisexisting suite of problems and amplifying it at an almost
astronomical rate, in four specific ways.
The first new question posed by AI is how creators will be
paid for the use of their materials in training foundation
models and applications such as ChatGPT and search engines. The U.S. Copyright Office has launched an inquiry
into these issues, to assess whether copyright laws need
to be updated in response to AI. The IAC – one of theworld’s largest internet holding companies and home todigital publishers such as DotDash Meredith – recently
warned in its comment to the Copyright Office that if
generative AI firms don’t pay publishers for copyrighted
content, the technology will undercut not only the news
media industry, but all kinds of other web publications
that deliver reliable and safe information.103
A second new question posed by AI is whether it can beused to create art that until now has required human beings as producers – such as music, books, news, and films.
For writers, this threat is so tangible that prohibiting theindiscriminate use of AI-generated text in screenwriting
was a key demand during the five-month strike led thisyear by the Writers Guild of America, which successfully
secured the guarantees it demanded.104 As the Open
Markets Institute put it in a letter to the U.S. Copyrightoffice: “Enforcing copyright laws and requiring companies to meet their legal obligations – whether that iscompensating rights holders or ensuring transparencyof training data – are critical in order to protect humancreativity and reward the labor that goes into it.”105
A related set of questions arise over whether ideas andinventions generated by AI models should be granted
patents, and if so, who gets the credit. These concerns
are not just hypothetical. An analysis by the Washington
Post of a major data set used by Google and Meta’s main
LLMs found that the biggest single category of websites were patent, business, and industrial sites including
Kickstarter and Patreon, raising concerns that the technology may copy businesses’ and creators’ original ideas
in suggestions to users.106 Granting patent rights to AI
models poses a wide variety of risks to the entire system
of intellectual copyright. One potential risk is simply
empowering large corporations to further reinforce their
dominance. For example, a pharmaceutical corporationthat owned the largest datasets on chemical structuresand prescriptions could use AI to mass-generate patents
for potential new drug compounds, driving market concentration and excluding rivals.
Finally, generative AI is enabling dominant retail platforms like Amazon and music streaming services like
Spotify to host and promote AI-generated books, music,
and similar products. Writers are reporting that Amazon
is recommending AI-generated rip-offs of their own
published books on its marketplace.107 Meanwhile, thee-commerce giant refuses to disclose how many of thesebogus publications it prevents from being published or
how many it takes down.108 Spotify not only generates
revenues from AI-generated music that violates copyright109 but hosts an undisclosed number of bots that act
as listeners of both AI-generated song rip-offs and realartists – effectively reducing royalties for the latter.110

34

THE THREAT TO WORKERS AND JOBS
The debate on the impact of automation and AI on jobs
long predates the emergence of generative AI. On the
one hand, many of the fears surrounding the impactof specific technologies on employment – be it the
automobile or ATMs – have proved to be overblown.
On the other, there is clear evidence that automation
over the past few decades has increased inequality111 anddisproportionately harmed specific communities,112 even
if it hasn’t led to mass unemployment. There is ample
evidence, meanwhile, that communities disproportionately impacted by automation have in turn become
more politically polarized, undermining the integrity andstability of democracy.113
It is too early to say with certainty whether AI specifically (as opposed to automation more generally) will
have a net positive or negative affect on employment.
Studies diverge significantly in their predictions, withmuch depending on whether AI is implemented in a waythat supports and augments human labor, or replaces anddegrades it. Generative AI’s generalized capabilities andability to execute more complex tasks – including drafting essays, writing code, and creating images – could
expose more ‘white collar’ work to automation, although
the low-quality of much AI generated output to dateputs this in doubt for the time being. What is clear however is that concentrated ownership of the technologies
driving automation will result in similar concentration
among the corporations and individuals that enjoy thefinancial rewards.
At the individual level, many of the dominant tech firms,
and Amazon in particular, have a long track record ofeither surveilling workers themselves or giving other
employers the tools to do so. Examples include Amazon’s
aggressive surveillance of its warehouse workers anddelivery contractors,114 Google spying on its white-collar
employees,115 and workplace tools provided by Microsoft and Google that enable invasive monitoring ofindividual workers.116 These abuses, many of which are
already enabled by AI, could foreseeably be made worse
by generative AI. For example, these corporations andother employers could use chatbots and other generative
AI tools to manipulate or coerce their staff, or to makedecisions affecting employees that they are unable tograsp or challenge.
Automation also risks aggravating worker exploitationand inequality in the form of wage discrimination.117 As
documented by academic Veena Dubal, tech giants
including Uber and Amazon increasingly compensate
their workers and contractors based on sensitive dataand performance indicators unknown to those workers,
and which they therefore cannot control.118 This not only
subverts the principle of equal pay for equal work, but
amplifies discrimination and impairs economic mobility.
Further advances in AI, combined with the monopsony
power that economic concentration gives dominant
corporations over their workers, threaten to take these
abusive practices to the next level.
THE THREAT TO RESILIENCEAND SECURIT Y
Allowing major companies and even industries to become dependent on a small number of technology giants
for critical inputs not only concentrates control overtechnological innovation and the financial returns tothat innovation in a few hands, it also concentrates many
forms of physical and systemic risk.
The concentration of power and control over cloud computing capacity and foundation models by the largest
gatekeeper platforms, for instance, poses a number oflarge threats to the resilience and security of many ofthe basic commercial and information systems on which
our society depends. To give just one example, a major
outage of Amazon’s cloud services in June 2023 hit
sectors including transportation and financial services,and organizations including Southwest Airlines, the U.S.

35

securities regulator, the New York Metropolitan Transportation Authority, and the Boston Globe.119
The severe disruptions caused by the Covid-19 pandemic,
the Russian invasion of Ukraine, and other crises to global
supplies of oil, gas, wheat, and semiconductors, and toocean and rail freight systems, have amply demonstrated
the dangers posed by extreme chokepointing of manufacturing and transportation capacities in a handful of companies and places. Drawing on that experience, it should beclear that to allow a few immensely powerful corporations
to control both the leading large-scale AI models and thecloud infrastructure those models are trained and hosted
on will concentrate risk as well as capacity and control, inways that set up these highly centralized systems to fail in
potentially catastrophic fashion.
A further level of risk is created as generative AI, andAI in general, is rapidly embraced by sectors generally
understood to be systemically important, from financial
services and energy to defense and transport. In financial
services, for example, Goldman Sachs is exploring thepotential of generative AI to classify millions of documents, including legal contracts,120 while JPMorgan
is developing a chatbot to help customers select theirinvestments.121 In energy, potential use cases include
demand forecasting, grid management, energy trading,and supporting customers in monitoring energy usage,122
while the U.S. military has confirmed it is exploring
how generative AI can help it generate information andimprove decision-making.123 In the automotive space,
Amazon has sought to promote the role of generative AI
in autonomous driving systems.124
Outright failure of the generative AI services or cloudinfrastructure provided by this oligopoly, with potentiallyexistential cascading effects across key industries andsystems, is the most straightforward threat to envision.
But even without this worst-case scenario, systemic
harm could be inflicted by serious flaws in AI models,
including everything from faulty data to manipulationand sabotage by malicious actors.
Gary Gensler, chair of the influential U.S. Securities andExchange Commission, has expressed deep concerns
about these and related issues. Recently he warned thatthe dependence of financial institutions on models anddata provided by Microsoft, Google, and other dominantgatekeeper corporations, as well as the fast-growing
ability of AI to concentrate and exacerbate certain risks,
threatens to trigger a financial crisis within a decade.125
As he explained in a 2020 paper when he was teachingat MIT, reliance on the AI services of a few players would
expose the financial sector to an extremely dangerous
combination of single points of failure and the amplification of herd-like behavior among individual investors andother actors.126
Similar arguments can easily be applied to other activities,
from AI-generated deepfakes and disinformation spreading at lightning speed on social media, to fatal accidents
in AI-powered autonomous vehicles and inefficiencies or
failures in an AI-regulated energy grid. One recent paper
gave the hypothetical example of a foundation model
used for the majority of medical diagnoses, which if flawed
could lead to systematic misdiagnoses and misprescribed
remedies – a problem that would be particularly catastrophic during a health emergency such as a pandemic.127
The key point is not that AI should not be used in criticalsectors, but that market concentration will greatly amplify
problems when things inevitably go wrong.

36IV: SOLUTIONS
As we have seen, today’s digital monopolies are the driving
force behind the latest advances in AI. They have achieved
this position largely by buying up leading technologies andstartups and leveraging their already enormous existing
powers and capabilities to impose their particular AI strategies and business models on the world.
As we have also seen, these same online gatekeeper
corporations are aggressively wielding these new technologies, as well as their control of most key links in the AI
supply chain, to reinforce their existing monopolies acrossthe digital economy. And, of perhaps greatest immediate concern, these corporations are also using these newtechnologies to amplify and accelerate many of their mostdangerous present behaviors. This includes reinforcing
business models based largely on manipulating and extorting almost every business and individual that depends
on their services in ways that distort public discourse,
threaten democracy, violate fundamental political liberties,
undermine property rights, and degrade online services.
The tech giants’ tightening grip over artificial intelligence
technologies and infrastructure also puts them in prime
position to shape the direction and nature of AI innovation. As it stands, a handful of giant corporations alreadyhave the power to largely determine – either directly or
through the control they exert over other actors – what
AI looks like, how it works, how it impacts our jobs andlives, and whose interests it serves. In this bleak visionof the future, the great majority of those affected by
AI – be they citizens, businesses, workers, consumers,
and even the state itself – will be dependent rule takers
rather than independent rule makers.
Fortunately, a different future is possible, one in whichtoday’s gatekeepers no longer control and exploit the
key platforms and technologies of AI. By imposing strict

limitations on their behaviors and through careful restructuring of their businesses, the citizens of the UnitedStates, Europe, and other nations can repurpose these
corporations to serve the public interest – or at least notwork against it. This in turn would create the space for abroad democratization of AI and other technologies now
monopolized by these corporations, in ways that empowera diverse set of actors - including independent businesses,
universities, public bodies, civil society groups, and individual people - to play a far greater role in the design, use,
and improvement of these technologies and services.
Bringing about this future will require us to accelerate
our efforts to break and neutralize the power of thedigital gatekeepers, a fact that has been largely ignored
thus far in the debate on AI. The good news is that focusing on the intersection of AI and power teaches us that
we can achieve most of our most important goals using
already existing law and institutions.

AI REGULATION THUS FAR: NECESSARY
FIRST STEPS BUT FAR FROMSUFFICIENT
Policymakers and experts are currently focusing theirefforts on establishing regulatory frameworks that place
guardrails around how AI is designed, brought to market,
and deployed. Such guardrails include requiring organizations to scrutinize their algorithms for bias, introducing
greater transparency on how their AI systems make decisions, and providing avenues for challenging automateddecision-making, including through human review. But inthese discussions, the question of who controls the technology – and how that power is used – rarely surfaces.
One of the earliest significant regulatory initiatives was
the European Union’s AI Act, published in April 2021,37

which seeks to impose horizontal obligations on the useof AI by public and private actors. The most notablefeature of the Act is its so-called “risk-based approach,”according to which the toughest requirements apply
to AI use cases perceived as posing “unacceptable” or“high” levels of risk, such as AI used in facial recognition
or worker surveillance.
The AI Act’s provisions range from outright bans forunacceptable use cases and mandatory risk assessments
and security measures for high-risk AI, to light-touchtransparency obligations for “limited risk” applications.
At the time of publication, the Act has not yet completed its passage through the EU legislative process, andchanges are to be expected. Significantly, the European
Parliament is pushing to ensure the Act also applies to AI
foundation models, a move which has faced opposition
from certain corporations and EU Member States.128
In the U.S., the Executive Order on AI issued by President Biden in October 2023 demonstrated that theWhite House clearly intends to ensure safety and security in artificial intelligence. Key measures include developing standards on safe and trustworthy AI, requiring
developers of powerful AI systems to share safety test
results with the government, and instructing agencies toprotect consumers and workers from algorithmic harms
and discrimination across a variety of fields, including
education, criminal justice, healthcare and housing. Crucially, the EO’s holistic approach makes clear that government intervention should protect not only the privacy
and civil rights of individuals, but also our wellbeing andliberty as workers, consumers, workers, entrepreneurs,
and independent business owners.
Going forward, it is vital to approach all such regulation of specific risks posed by AI with great care. Suchregulation is necessary to ensure that corporations andgovernments do not use the technology in unethical andharmful ways. But we must be alert to the risk that such
regulation, if poorly designed, can be exploited by thebiggest corporations to further entrench their dominance over both this suite of technologies and the rulesthat govern its use. The reason is simple. Government
regulators of complex technologies often adopt complex
and costly rules that can prove onerous for smaller andmedium-sized businesses – thus deepening the advantage dominant corporations already enjoy thanks to theirgreater size and resources. This well documented fact
may help to explain why, as discussed earlier, many of thedominant tech corporations have spoken out publicly infavor of AI regulation. Perhaps the best recent exampleof how such regulation can actually help or at least fail torein in incumbents is the ongoing difficulty in enforcing
the European Commission’s General Data Protection
Regulation (GDPR).129
More problematic yet are the voluntary initiatives
introduced by the tech industry itself, ostensibly toensure that AI is deployed responsibly and ethically.
These initiatives include the AI ethics teams establishedby individual large companies, as well as alliances such as
the Partnership on AI, a coalition founded by Amazon,
Facebook, Google, DeepMind, Microsoft, and IBM in2016 and which now counts a large number of non-profit actors among its membership. Recently, leading AI
companies have sought to use self-regulation to deflect
mounting concerns over the safety of their products.
These include a set of vague voluntary commitments
made by seven companies at the behest of the Biden
administration,130 and the launch of a “Frontier Model
Forum” by Microsoft and Google, along with OpenAI
and Anthropic.131
Serious questions about the intentions and effectivenessof such voluntary, industry-led initiatives are raised by
the fact that many of these projects have already beenwatered down or entirely abandoned. Google’s AI advisory board, established and shut down within the space ofa fortnight, surely holds the record for most short-lived.
And as seen earlier, that corporation subsequently forcedout leading AI ethics researcher Timnit Gebru after she

38

HARM(S)SOLUTION(S)Discrimination and manipulation by dominant AI andcloud gatekeepers against customers.

Ban discrimination by powerful gatekeeper platforms
providing essential services.

Leveraging of monopoly power in cloud to control AI;
monopolistic conduct by dominant cloud providers.

Separate ownership and control of the cloud fromgatekeeper platforms; regulate cloud platforms as utilities.

Concentration of data ownership; exploitation of thisadvantage to dominate AI.

Establish public-interest data regime to govern access todata collected by gatekeeper platforms.

Use without permission of creative property to train AI
models and tools.

Aggressively enforce copyright laws to protect creative
property; audit use of copyrighted material in AI systems.

Threat to resilience and national security fromconcentration of key technological capabilities.

Use government investment and procurement
policies to break chokepoints; map the impact ofgatekeeper corporations on national security.

Market concentration through by anti-competitive deals
between gatekeepers and challengers/startups.

Block and reverse gatekeeper efforts to control AI
through mergers, investments and partnerships.

Surveillance and exploitation of workers through AI and
AI-augmented means.

Establish bright-line rules to limit digital exploitation ofworkers and contractors.

Privacy violations from illegal/unethical use of personal
data to train AI models and tools.

Increase strategic collaboration between competition law
enforcers and data protection regulators.

Figure 5: Overview of AI concentration harms and solutions

39

raised uncomfortable questions about the safety of its AI
models. More recently, the race to dominate AI regardless of the consequences has led to large tech firms –including Microsoft, Meta, Amazon, Google, and Twitter– cutting, and in some cases entirely liquidating, teams
working on ethical AI and trust and safety.132

USING COMPETITION POLICY TO KEEP
AI SAFE, OPEN, AND ACCOUNTABLE
By failing to apply to today’s digital gatekeepers therules and measures historically used to rein in dominant
corporations in other industries, policymakers now findthemselves scrambling to address the many harms thisunchecked dominance has inflicted on our democracies
and economies – a task that AI makes both more urgent
and more difficult.
Precisely because AI is so worrying in so many ways, it isvital that policymakers learn the lessons of the past anduse competition policy to ensure this powerful new technology is designed and used in the public interest. Doingso will not only protect the public and enable it to takefull advantage of AI’s benefits, but will also make it easier
to apply other forms of regulation – including privacy,
consumer protection, and health and safety rules – totoday’s dominant corporations.
Above all, regulators should focus on studying and addressing AI’s role in amplifying and accelerating existing
monopoly harms, before moving onto emerging andhypothetical threats.
To maximize their chances of success, enforcers and policymakers should be guided by four core principles:
I. Establish a clear hierarchy of goals for regulatory
action, to help prioritize the use of limited resources.
Given limited resources, and the scale of the challenge athand, it is crucial that lawmakers and regulators establish

a clear hierarchy of goals for regulatory action. The top
priority should be tackling threats to individual liberty
and democratic institutions, which are essential if weare to break and harness the power of the online gatekeeper corporations that now threaten us. This in turnimplies a close and immediate focus on the many ways inwhich the existing power and existing behaviors of theseimmense, privately controlled gatekeepers threaten ourability to communicate and debate, gather and sharenews, and do business directly with one another. Questions of technological innovation should be of secondary
importance compared to making these platforms safefor democracy. That said, given sufficient resources, law
enforcers can also focus simultaneously on promoting
an open and competitive political economy. Indeed,
many actions that would protect our core political rights
and interests would also begin to provide individuals andbusinesses with greater opportunity to promote innovation and to master AI and other new technologies in the
public interest.
II. Make aggressive use of existing law, and invest inlegislation and new regulatory institutions only where
there’s a clear need and reasonable chance of success.
Many governments already possess wide-ranging powersthat can be deployed now to prevent today’s monopolists from using AI to further cement their power andmore effectively exploit and manipulate individual people
and companies, as well as to unwind dangerous existing
concentrations of capacity and control. These powers
include competition law and policy, trade policy, consumer protection laws, privacy regulation, and copyrightprotection. This approach is especially important in the
United States, where there is an extremely vast androbust collection of powerful laws and regulatory regimes
to address such threats, built up and refined over thecourse of more than two centuries, and where longstanding gridlock in Congress makes it unlikely the institution
will pass new competition laws soon.

40III. Accelerate efforts to adapt existing competition law
to address today’s threats.

IMMEDIATE NEXT STEPS – EIGHT ACTIONSGOVERNMENTS CAN TAKE NOW

Law enforcers and lawmakers are engaged in the mostfundamental rethinking of competition policy since
the Chicago School revolution of the early 1980s andin some respects since the New Deal. In the UnitedStates, Europe, and elsewhere, enforcers are scrambling
to restore traditional pre-Chicago School goals, principles, and analysis, as we see in the new draft merger
guidelines published by the Department of Justice andFederal Trade Commission. Lawmakers and enforcers
are also moving to adapt and update those regimes forthe digital age, as we see in new European laws such as
the Digital Markets Act and Digital Services Act andin the wide-ranging lawsuits by both the federal andstate governments in the United States against Google,
Amazon, and Facebook. This effort is still, however, in its
early stages. It is vital to immediately extend this effort
to cover how we treat such factors as control of data andcomputing capacity, including directly in relation to AI.
It is also vital to move swiftly to integrate this effort withlegal and regulatory regimes that intersect with antitrust,
including communications, trade, privacy, copyright, andconsumer protection, among others.

The rest of this section sets out what a few such policies
could look like, in general terms, while focusing on eightstrategic areas of action where law enforcers and lawmakers can swiftly begin to address the gravest dangers
posed by AI.

IV. Ensure that dominant corporations in control ofessential platforms and services treat all users the same.
A core tenet of competition policy is the requirementthat private corporations that control essential services
do not discriminate in the delivery of these services,and provide equal access to all comers. As Senator John
Sherman said in a speech in favor of the U.S. antitrust
law that bears his name, such regulations lie “at the foundation of the equality of all rights and privileges.” Today
this principle is especially important when addressing theactions and business models of essential communications
and commercial platforms, as well as all essential digitaland AI infrastructure including cloud computing capacityand foundation models.

1. Ban all discrimination by powerful gatekeeper platforms in the delivery of essential services to individuals
and businesses.
Given the many actual and potential harms that the business models of the dominant gatekeepers already poseto citizens, workers, creators, independent businesses,
and consumers – particularly through the automatedand personalized discrimination in pricing and delivery of
information and services outlined earlier in this report –it is paramount that competition authorities, consumer
protection bodies, data protection agencies, and otherregulators take swift action – both individually and inconcert – to stamp out such practices.
In the United States, both the FCC and FTC haveample authority to establish rules to guide and control
the broad behavior of the gatekeepers. At more specificlevels – such as the divisions of these gatekeeper corporations that oversee transportation services or intersectwith financial services – so do other departments andagencies including the Federal Reserve, Department ofCommerce, Department of Treasury, and Departmentof Transportation, among others. Fortunately, President
Biden recognized this broad need in July 2021, in callingfor a whole-of-government approach to addressing thethreats and harms posed by concentrated economic
power and control. But given the proven ability of AI toaccelerate and amplify existing monopoly threats andharms, it is vital to upgrade the ability of different agencies to more effectively coordinate in the enforcementof their existing authorities.

41

Gary Gensler, the chair of the U.S. Securities andExchange Commission, recently made this point in aninterview in which he warned that AI will almost definitely trigger a financial crisis within the next decade. The
wide-ranging nature of the threat, Gensler said, poses “across-regulatory challenge.”
In Europe, the EU has already demonstrated througha variety of recent enforcement actions and legislation – including the Digital Markets Act and the Digital
Services Act – that it has a general understanding of thethreats posed by the business models of the gatekeeper
corporations. So too have the competition law enforcement agencies of some individual nations, especially
Germany and the United Kingdom, which have both
pursued ambitious antitrust cases against the gatekeepers and significantly expanded their own powers. Here aswell however more coordination is needed, both between
different European competition authorities and between
competition authorities and their counterparts in otherareas of regulation.
Encouragingly, competition, consumer protection andother agencies seem attuned to the risks of concentration in AI, and ready to move faster than in the past.
In April 2023, four U.S. enforcement agencies issued
a joint statement pledging to use their existing powers
to crack down on the unlawful use of AI applications indifferent industries. And in October, the UK’s Competition and Markets Authority published a detailed study on
the risks of concentration in foundation models. Numerous competition authorities around the world have also
undertaken detailed studies of concentration in the cloudand several are investigating anti-competitive conduct inthe market for advanced semiconductors.
2. Recognize cloud computing as an essential infrastructure, separate ownership and control from the largest
gatekeeper platforms, and regulate it as a utility.
As we have seen, much of the tech giants’ emerging

power in AI is rooted in their cloud computing dominance and unparalleled access to data. Given theirsystemic role across the economy and public sector,
large cloud computing providers should be regulated as
public utilities under common carrier principles, subject
to strict obligations on non-discrimination, fair and equal
treatment of all customers, and ensuring safety andreliability.
As multiple studies have shown, the cloud computing industry is extremely concentrated, with just three providers(Amazon, Microsoft, and Google) controlling roughly twothirds of the global market.133 This figure varies significantly by market, with the combined share of the top three
approaching 80% in the UK.134 As discussed earlier, thisconcentration creates a wide variety of threats.
This includes the financial threat to any individual
business or government that depends on these cloudservices, as this concentration of power and control puts
the cloud giants in a position to lock in and exploit theircustomers through excessive switching costs, anti-competitive discounts, arbitrary restrictions on interoperability, and the usage without permission of the data andideas of their customers.135 It includes the major threat
to economic and societal resilience posed by the relianceof our governments and key industries on a handful ofgeographically concentrated cloud providers, as illustrated by a number of critical failures in the past.
Despite these threats and the infrastructural, utility-likerole played by the dominant cloud platforms, governments have so far failed to put in place the comprehensive regulatory regimes commensurate to this privileged
status. While a number of recent initiatives, including theEU’s Data Act, Digital Markets Act and Cybersecurity
Act, impose a number of standalone responsibilities on
cloud platforms, none of these are designed to provide
the overall regulatory framework for the structure, management, and behaviors of the dominant cloud providers
that is clearly necessary.

42Law enforcers should move to immediately impose anon-discrimination regime on the entire cloud industry.
Under such a regime, large cloud computing providers
would be required by law to treat all customers fairly
and equally while ensuring high standards of safety andreliability. This would include refraining from denying
service arbitrarily; offering discounts or other benefits tofavored customers (including its own vertically integrated business lines) or discriminating against others; andmaking it unnecessarily difficult to contract with alternative providers. These providers would also be required
to uphold the highest standards when it comes to data
privacy, cybersecurity, operational resilience and more.
Additional measures are needed to tackle the unavoidable conflicts of interest presented by the concentrated
ownership of today’s dominant cloud platforms. Even
if the likes of Amazon and Microsoft are banned fromself-preferencing their own services or discriminating against users, they are likely to continuously find
new ways to leverage their dominance in the cloud tostrengthen their positions in AI and other areas.
A lasting solution to the conflicts of interest inherent
in owning both critical infrastructure and services that
rely on that infrastructure would be to force Microsoft,
Google, and Amazon to divest their cloud units, and toprevent cloud providers from being active in conflicting
lines of business. This would eliminate any ability or incentive on their part to give their proprietary AI models– or those of close partners – special treatment. And it
would make the utility-style regulation of cloud providers
proposed above far more manageable.
Additionally, a general requirement of interoperability
across the wider AI ecosystem would greatly help toprevent today’s dominant corporations from using existing
structural advantages to eliminate competition and lockin users and customers. In addition to making it easier tomove from one cloud system to the next, such obligations

could also aim to ensure that foundation model providers
can train, host, and run their models across different cloudproviders, and that AI developers and applications are able
to run queries on different foundation models.136
A number of ongoing actions by competition authorities
provide avenues for intervention. The CMA is currently
undertaking an in-depth market investigation into thesector, which will give it the power to impose structuraland behavioral remedies, while the FTC is in the earlystages of its own investigation. France’s competition authority, noting similar market failures in a recent study, isalso considering the need for litigation.137 Meanwhile theEU’s recently passed Data Act contains provisions enabling switching between different cloud providers, whilecloud providers are in the scope of its Digital Markets
Act.
3. Recognize that any data collected by large platforms
in their capacities as essential services is public innature, and establish a public-interest regime to govern
access.
Most of the data collected by the dominant technology
platforms through the provision of essential commercial
and communications services should be considered public
in nature, and therefore governed as a common publicresource.
There are various ways this principle could be operationalized in practice. One option would be to require
dominant technology firms to share aggregated and anonymized data with other actors, including competitors,
startups, public bodies, and non-profit organizations.
This would have a number of desirable consequences,from helping companies to develop rival offerings to the
AI models and services dominated by these few corporations, to supporting socially beneficial research andinnovation at universities and government agencies.138

43

An alternative option would be to task a distinct institution or trust with governing access to the data collectedby these corporations whenever they are engaged in theprovision of essential communications or commercial
services. This entity could be a public body or a certified
non-profit intermediary entrusted with data stewardship. It would be responsible for ensuring that the data
it holds is stored and shared safely, responsibly, and incompliance with any relevant laws and regulations. Theintermediary could also be given – or required to establish – principles and objectives that guide its decision-making in ensuring that the data is used to pursue
beneficial rather than harmful ends.139 This would include
a clear system of rules limiting access to the data by law
enforcement and other public officials.

treat their inputs as copyrighted work – and thus be required to obtain consent, provide compensation, and set
transparent and fair parameters for use of the outputs
generated – show there is strong support for this courseof action.141

Creating a workable public interest regime for data will
also require balancing competing objectives and ethicalpriorities, such as protecting privacy while promoting innovation. A technology known as “federated learning,” whichallows models and algorithms to be trained on datasets
hosted in different places without that data actually being
moved or centralized, is one potential path forward.140

AI also greatly magnifies the longstanding need torebalance power between publishers and digital monopolies. This time around, the media sector appears far
more cognizant of the threat. The News Media Alliance,
representing over 2,000 members across the globe, hasalready put forward principles to govern how generative
AI systems use publisher content.142 Similarly, media
leaders from more than 24 countries recently proposeda framework for any country seeking to use regulation
to force digital platforms to negotiate with publishers
over fair use of their content.143 And by way of the U.S.
Journalism Competition and Preservation Privacy Act,legislators are pushing for publishers to have the rightto negotiate compensation with companies using news
content to train AI models,144 mirroring similar initiatives
already in force or being implemented in Australia, Canada, the EU, the UK, and other jurisdictions.

4. Aggressively enforce copyright laws to protect theproperties of authors, creators, and other independent
publishers from misappropriation and misuse by gatekeeper corporations, and establish a trustworthy andtransparent system for auditing the use of copyrighted
material in AI systems.

5. Clearly map how the structures, behaviors, andbusiness models of the largest gatekeepers threaten
national security, including by enabling foreign surveillance and interference. Use government investment andprocurement policies to break chokepoints and promotesecurity.

Given clear evidence of copyright violations by AI
models and applications, enforcing existing copyrightlaws to protect the properties of authors, creators, andother independent publishers from misappropriationand misuse must be an urgent priority. To support thisenforcement, corporations should be subject to audits of
their use of such copyrighted material to train AI models
and applications. Recent calls from across the creative
industries in the U.S. and beyond for AI companies to

The disruption caused by the Covid-19 pandemic and war
in Ukraine have highlighted like never before the dangerous levels of concentration in global supply chains foressential goods and materials, including semiconductors,
medical equipment, food, and critical minerals. In response to these shocks, governments and multinationals
are taking steps to make supply chains more resilient bypromoting diversification of production, including using
public funding and incentives to encourage companies

44

to manufacture more locally and regionally. Notable
examples of such initiatives include the U.S. Inflation Reduction Act, the EU and U.S. Chips Acts, and the EU’s
Critical Raw Materials Act.
Building on this experience, there are steps governments
can already take to ensure that our growing reliance on AI
does not create new vulnerabilities. To start with, governments should clearly map how the structures, behaviors,and business models of the largest gatekeepers threaten
national security, and identify practical means of addressing this. They should focus especially closely on the waysthat various platforms and business models within Google,
Meta, Amazon, Apple, Microsoft, and Twitter provide
foreign states and non-state antagonists with the ability to
disrupt commercial, communications, or political systems
in the United States, Europe, and other nations.
Industrial policy, broadly defined, offers a variety of ways
forward. The most obvious tactic is simply to build public
capacity directly. Early examples of policies in this direction include the EU’s recently announced plans to give
startups access to its supercomputers,145 the French government’s funding of a supercomputer later used to train
BigScience’s BLOOM large-scale AI model, and recent
investments by the UK government worth several billion
pounds into supercomputers, quantum technologies, andadvanced semiconductors.146 Similarly, the French andother governments are exploring the potential to build
publicly owned ‘national cloud’ systems.
Government can also use their massive procurement
and investment budgets to help break dangerous chokepoints both in the ownership and the location of essential
computing capacity and infrastructure, including cloudcomputing and semiconductor manufacture. Successfulprograms such as the U.S. Defence Advanced Research
Projects Agency (DARPA) provide a potential templatefor such efforts.

6. Reverse gatekeeper efforts to control AI development
through mergers, investments and partnerships andblock similar deals in future.
Much if not most of the existing power of the digital gatekeepers is based directly on the hundreds of acquisitions
these corporations have made over the last two decades.
According to one study, Apple, Google, Microsoft, Meta,
and Amazon have acquired at least 700 companies since
the year 2000, none of which were stopped by regulators.147 Many of these were takeovers of minisculestartups, which failed to generate much public attention
or draw the scrutiny of regulators, making it difficult toknow in each instance how important their technology
and talent were to the giants’ subsequent growth. But
when it comes to a number of high-profile deals, including
Facebook’s $1 billion acquisition of Instagram in 2012 andGoogle’s $3.1 billion purchase of DoubleClick in 2007,
it is indisputable that these were significant in laying the
ground for the platforms’ future dominance.
As seen earlier, when it comes to today’s leading AI
startups, the tech giants have so far opted to provide
substantial financial and logistical support over outrightacquisition, partly out of a desire to avoid regulatory
scrutiny. But this is likely to change soon, if the past is a
useful guide. Putting aside the recent generative AI boom,
there are many examples of the tech giants acquiring AI
firms, including Google’s purchase of DeepMind in 2014,
Microsoft’s purchase of speech recognition firm Nuancein 2022,148 and Apple’s acquisition of AI Music in the same
year.149 According to data from PitchBook, around a fifthof the combined acquisitions and investments of these
corporations since 2019 have involved AI firms.150 It does
not require a great leap of the imagination to see Microsoft making an outright bid to acquire OpenAI, or Google
or Amazon attempting to purchase Anthropic.
Preventing today’s monopolies from dominating AI will
therefore entail restricting their ability to acquire or co-opt

45

innovative startups and potential rivals, either to gain accessto their technology or eliminate them from the market.
Major investments and partnerships should not escapescrutiny, particularly where these enable the dominant firm
to exert significant control over its partner or investee. In
most jurisdictions, regulators already have the power toinvestigate cartels and anti-competitive mergers and investments; where this is not the case, these powers should be
upgraded. Acquisitions, investments, and agreements thathave already been completed – including Microsoft/OpenAI, Google/DeepMind and Amazon/Anthropic – must be
urgently investigated and if necessary unwound.
7. Establish bright-line rules to limit digital exploitationof workers and contractors, including a complete ban on
biometric surveillance and automated manipulation.
Law enforcers and lawmakers should also take immediate
action to prevent AI from being used to further undermine the rights, autonomy, and privacy of workers andcontractors.
Where workers are harmed by the application of AI tosystems of management and control, the burden of proof
in justifying such activity should lie with employers anddevelopers rather than employees, given the obvious
asymmetries in information and power.151 Key regulatory
initiatives intended to shield workers from AI surveillance and exploitation, including the EU’s Platform Work
Directive and the U.S.’s Stop Spying Bosses Act, should
be adapted and updated where necessary to reflect thelatest developments in generative AI.
The AI Now Institute has proposed a set of measures toprotect workers from AI exploitation, including baseline
protections from algorithmic management and workplace surveillance, clear bright line rules in relation to
specific practices (such as automated hiring and firing)and technologies (such as emotion recognition), andensuring workers have the right to engage in collective

bargaining and action.152
8. Increase strategic collaboration between competition
law enforcers and data protection and privacy
regulators.
Privacy regulation, properly understood, is also a form ofcompetition policy, in that it can be used to limit certain
behaviors that corporations engage in order to concentrate
power, control, and wealth. Similarly, competition law andregulation can be used to help achieve fundamental privacy
and data protection goals. One obvious example would be
the use of antimonopoly law to separate cloud infrastructurefrom the largest gatekeeper corporations. A less obvious
example is common carrier law, which radically reduces thevalue of the data that platform monopolies gather, by eliminating most of the potential to use that data to manipulate
the behaviors and decisions of customers.
Previous generations better understood these fundamental interconnections. One example is the U.S. Congress’ decision to entrust the Federal Trade Commission
with both competition and privacy powers. In recent
decades, however, in large part thanks to Chicago School
efforts to isolate antitrust authorities within hardenedsilos, this fundamental interlinkage has been largely
ignored. As a result competition enforcers and privacy
regulators have largely ignored one another’s work.
This is beginning to change. The European Data Protection Supervisor’s office, under the leadership of Giovanni
Buttarelli, worked hard to bring these two realms backinto close alignment. More recently, a landmark ruling by
the European Court of Justice recognizing that privacy
violations can be a form of monopolistic conduct hashelped point the way to practical cooperation. Meanwhile, pioneering competition law enforcers such as
Germany’s Bundeskartellamt and the FTC have begun
to actively explore and exploit the many intersections ofthese two legal regimes.

46

CONCLUSION
As with any technology, we cannot be sure what path AI will take, or how
successful it will ultimately be. Will it become as ubiquitous as search and social
media today, or are we in the midst of another overhyped bubble, in the samevein as crypto and the metaverse?
Either way, how AI is developed and the impact it hason our democracies and societies will depend on who isallowed to manage, develop, and deploy these technologies, and how exactly they put them to use. We havea choice to make: between allowing some of the mostpowerful corporations the world has ever seen to develop AI in their own narrow self-interest, or structuringmarkets in a way that ensures AI promotes the public
interest, and is subject to democratic control by citizens,
not corporations.
Competition policy is the single most powerful tool at
our disposal when it comes to restructuring markets inthis way. And we should fully understand that this does

not simply mean antitrust law and regulation.
The Biden White House has demonstrated this with its
whole-of-government approach to addressing the many
threats posed by the concentration of power and control
in our political economy. This approach brings together
powers held under privacy, consumer protection, corporate governance, copyright law, trade policy, labor law,and industrial policy regimes.
The more closely we integrate these regimes, in the
United States and elsewhere, the more swiftly andeffectively we will be able to ensure that AI truly serves
the interest of the people as a whole, and not simply theinterests of the very largest corporations.

47

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Ibid.

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