AI & Antitrust: Reconciling Tensions Between Competition Law and Cooperative AI Development | Yale Journal of Law & Technology

Yale Journal of Law & Technology
Volume 23, Spring 2021
AI & ANTITRUST : RECONCILING  TENSIONS BETWEEN
COMPETITION LAW AND COOPERATIVE AI DEVELOPMENT
Shin-Shin Hua1 & Haydn  Belfield2

Cooperation between companies developing artificial
intelligence (AI) can help them create AI systems that are safe,
secure , and with broadly shared benefits. Researchers have
proposed a range of cooperation strategies , ranging from
redistributing  “windfall ” profits to assistance to address the
harmful dynamics of a competitive race for technological
superiority.
A critical tension arises, however, between cooperation and
the goal of competition law, which is to protect the very process of
competitio n between rival companies. Whilst these potential
conflicts are significant, they are currently underexplored in the
literature. This paper examine s the relationship between proposed
forms of AI cooperation and competition law, focusing on the
competition law of the European Union (EU).
EU competition law governs the behavior of the world’s
largest AI companies, though many are based abroad, especially in

1 Research Affiliate, Centre for the Study of Existential Risk, University of
Cambridge; antitrust attorney at British Telecom Group and formerly at Cleary
Gottlieb Steen & Hamilton, London. Shin -Shin has an Advanced Masters in
Public International Law from  Leiden University (Cum Laude) and a degree in
Law from Peterhouse, University of Cambridge.
2 Research Associate and Academic Project Manager at the Centre for the Study
of Existential Risk, and Associate Fellow of the Leverhulme Centre for the Future
of Intelligence, both at the University of Cambridge. Haydn is a former Senior
Parliamentary Researcher to a British Shadow Cabinet Minister and  Policy
Associate to the University of Oxford’s Global Priorities Project. He has a degree
in Philosophy, Politics  and Economics from Oriel College, University of Oxford.
416 Yale Journal of Law & Technology  [Vol. 23
the US. Its jurisdiction can extend to any foreign company that is
active in the EU. Scrutiny of US “Big Tech ” is also an area of
strategic focus for the European Commission (EC).
This paper seeks to reconcile the cooperative AI
development and competition law . It examines fourteen forms of AI
cooperation, both those that are applicable today and longer -term
strategies that will apply when AI development is more advanced.
Where we identify potential tensions with EU competition law, we
suggest mitigation steps. Our aim is to ensure the long -term
sustainability of these important safeguards to the responsib le and
beneficial development of AI.

2021 ] Artificial Intelligence & Antitrust  417
Table of Contents
Introduction  ................................ ................................ .................. 418
I. Why is Cooperative AI Development Desirable?  .................... 428
II. Background: The Prohibition on Anti -Competitive Agreements
Under Article 101  ................................ ................................ .433
A. Article 101(1 ) Prohibition  ................................ ........... 434
B. Article 101(3) Exemption ................................ ............ 439
C. Investigation and Enforcement Powers  ....................... 444
III. Legal Analysis of Proposed Forms of Cooperation: Long -Term
Strategies  ................................ ................................ .............. 454
A. OpenAI Assist Clause  ................................ ................. 455
B. The Windfall Clause ................................ .................... 482
IV. Legal Analysis of Proposed Forms of Cooperation: General
Trust Building  ................................ ................................ .......489
A. Agreements Between Competitors  .............................. 490
B. Information Exchange  ................................ ................. 492
C. Standard -Setting  ................................ .......................... 516
Conclusion  ................................ ................................ ................... 537

418 Yale Journal of Law & Technology  [Vol. 23
INTRODUCTION
This paper  surveys types of cooperation between AI
companies and proposes ways to structure cooperation to comply
with competition law.
AI governance3 is concerned that without cooperation
between AI companies, AI development could be less safe and
beneficial. Researchers have therefore propose d many cooperation
strategies , from the Assist Clause to information exchange and
standard setting . However, co mpetition law could be a barrier to
these strategies. This is because, at its heart, competition law seeks
to promote competition and prevent cooperation that harms
consumers. Nevertheless, if structured carefully, these cooperation
strategies should not r aise serious competition law concerns, and so
achieve their objectives of cooperation and risk reduction.
In this paper, we define “AI” as a digital system that is
capable of performing tasks commonly thought to require
intelligence, with these tasks typically learned via data and/or
experience.4 “AI system ” refer s to a software process (with the

3 The study or practice of local and global governance systems —including norms,
policies, laws, processes, and institutions —that govern or should govern AI
research, development, deployment, and use.  Sean S. Ó hÉigeartaigh, Jess
Whittlestone, Yang Liu et al. Overcoming Barriers to Cross -cultural Cooperation
in AI Ethics and Governance , PHILOS . TECH. (May 15, 2020),
https://link.springer.com/content/pdf/10.1007/s13347 -020-00402 -x.pdf.
4 Miles Brundage , Shahar Avin, Jasmine Wang, Haydn Belfield, Gretchen
Krueger et al., Toward Trustworthy AI Development: Mechanisms for Supporting
Verifiable Claims , arXiv preprint  arXiv:2004.07213  (Apr . 20 , 2020 )
https://arxiv.org/pdf/2004.07213.pdf , at 4.
2021 ] Artificial Intelligence & Antitrust  419
characteristics of AI mentioned above), running on physical
hardware, under the direction of humans operating in some
institutional context.5
Companies pla y an outsize role in AI research and
development compared to academic  or government  groups .
Cooperation between competing companies is likely necessary in
order for them to develop and deploy AI systems responsibly —that
is, safely, securely and with broadl y distributed benefits.6 On the
other hand, lack of cooperation could be  irresponsible : unsafe,
insecure, and not socially beneficial. AI governance is especially
concerned with the prospect of AI research taking on the dynamics
of a competitive race for technological superiority .7  These
considerations have led to cooperation between AI companies
becoming a major focus of AI governance, at both theoretical and
practical levels.

5 Id. at 62
6 Amanda Askell, Miles Brundage & Gillian Hadfield, The Role of Cooperation
in Responsible AI Development , COMPUTERS AND SOCIETY, arXiv preprint
arXiv:1907.04534 (2019) . Allan Dafoe, Edward Hughes, Yoram Bachrach,
Tantum Collins, Kevin R. McKee, Joel Z. Leib o, Kate Larson & Thore Graepel,
Open Problems in Cooperative AI , arXiv preprint  arXiv:2012.08630 (15 Dec
2020).
7 See generally  id.; Nick Bostrom, Allan Dafoe & Carrick Flynn, Policy
Desiderata in the Development of Machine Superintelligence , FUTURE OF
HUMANITY INSTITUTE OXFORD (FHI)  WORKING PAPER  (2018); Stuart Armstrong,
Nick Bostrom & Carl Shulman, Racing to the Precipice: a Model of Artificial
Intelligence Development, 31 AI & SOCIETY  201 (2016); Stephen Cave & Sean S.
Ó hÉigeartaigh, An AI Race for Strategic Advantage: Rhetoric and Risks , 2018
PROCEEDINGS OF THE 2018  AAAI/ACM  CONFERENCE ON AI, ETHICS AND
SOCIETY  36; Haydn Belfield, Activism in the AI Community: Analysing Recent
Achievements and Future Prospects , 2020 PROC EEDINGS OF THE 2020  AAAI/ACM
CONFERENCE ON AI, ETHICS AND SOCIETY 15; Ó hÉigeartaigh, Whittlestone, Liu
et al. , supra note 3.
420 Yale Journal of Law & Technology  [Vol. 23
However, competition law could raise barriers to these
proposed strategies. There is an inherent tension between
cooperation and the goals of competition law, which at its core is
meant to protect the very process es of rivalry between companies.
Strategies  that seek cooperation rather than competition between
companies can therefore raise anti -competitive concerns. In
particular, these strategies may breach the EU competition rule that
prohibits agreements between companies that restrict or distort
competit ion between them: Article 101(1) of the Treaty on the
Functioning of the European Union (TFEU). This rule is the
counterpart of Section 1 of the Sherman Act in the US, which
declares that “ [e]very contract, combination in the form of trust or
otherwise, or  conspiracy, in restraint of trade or commerce ” is
illegal.8
The scope of this note focuses on the EU competition
regime, with some limited references to the UK regime where
relevant. While many leading AI companies are based in the US or
China, the pract ical reach of EU competition law is broad and any
foreign company that has activities or customers in the EU, or any
other impact on EU trade, can fall within the jurisdiction of EU
competition law. EU competition law is powerful and influential.
Breaches can lead to fines of up to 10% of worldwide turnover, for

8 15 U.S.C. § 1.
2021 ] Artificial Intelligence & Antitrust  421
example, as well as derivative private litigation claims for sums that
can dwarf the original competition law fine.9 Even if investigations
and fines were only future possibilities , the legal uncer tainty could
dissuade companies from participating in types of cooperation.
Cooperation  that raises competition concerns in an EU context is
likely to raise similar conce rns in the US or beyond. Recent
European cases in the technology sector have influence d similar
investigations across the world .10  Therefore,  it is important that we
structure cooperation in ways that do not raise EU competition law
concerns. We suggest how to do so below.
The purpose of this paper is not to survey the incentives of
AI companies to cooperate or the likelihood that they may do so.
Rather, it is to examine  initiatives that the AI research community
is already discussing  and AI labs  are already employing . We think
cooperative AI development is feasible despite fierce market rivalry

9 In the UK, for example, the current  live claim for Merricks v Mastercard  seeks
£14 billion in damages, brought on behalf of 46 million consumers  (Mastercard
Incorporated and others (Appellants)  v Walter Hugh Merricks CBE (Respondent)
[2020] UKSC 51 ). The original competition law decision (Case COMP/34.579 -
Mastercard, EC decision  of 19 December 2009) , upheld on appeal), did not
impose a fine but the defendant was obliged to bring the infringement to an end.
10 For example, in 2005, South Korea closely followed the EU’s ruling in 2004
against Microsoft relating to its bundling of Windows Media Player with its
operating system ( Case COMP/C -3/37.792, Microsoft, Commission decision of
24 May 2004, 2007 O.J. L32/23) . The EU’s multiple investigations into Google
since 2010 (Case AT.39740, Google Shopping, Commission decision of 27 Ju ne
2017; Case AT.40099, Google Android, Commission decision of 12 February
2018 ; Case AT.40411, Google Adsense, Commission decision of 20 March 2019)
have also led to similar investigations in Russia’s in 2015 regarding Google
Android, Brazil in 2013 for Goog le Search and later for Android, Turkey in 2015
for Google Android, and South Korea in 2016 for Google Search and Android.
For further discussion, see ANU BRADFORD , THE BRUSSELS EFFECT : HOW THE
EUROPEAN UNION RULES THE WORLD , at 122 -27.
422 Yale Journal of Law & Technology  [Vol. 23
between the world’s largest AI companies today  for two main
reasons .
First, governments, civil society and public opinion
currently focus on the need for safe, beneficial and responsible AI.
See, for example, the European Commission’s propo sed Artificial
Intelligence Act, that is currently  passing through the EU legislative
process and seeks to  build trust in  AI systems and ensure they are
used consistently with fundamental rights and European values .11
Responsible AI development is an esse ntial competitive parameter
for AI companies. Conversely, AI companies that do not meet
expected standards can experience severe commercial and
reputational harm, as  most vividly  demonstrated by the Cambridge
Analytica scandal.12
Second, cooperation betwee n AI companies need not be a
zero-sum game. It can be beneficial for innovation, interoperability,
consumer trust and adoption  to cooperate . Incident sharing and
establishing industry safety benchmarks can be mutually beneficial
to all players, for example . We also see robust industry cooperation
today between companies  and in bodies like the Partnership on AI

11 Proposal for a Regulation of the Euro. Parliament and of the Council Laying
Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act)
and Amending Certain Union Legislative Acts, Com/2021/206 final.
12 See, e.g., Nicholas Confessore, Cambridge Analyti ca and Facebook: The
Scandal and the Fallout So Far , N .Y. TIMES  (April 4, 2018 ),
https://www.nytimes.com/2018/04/04/us/politics/cambridge -analytica -scandal -
fallout.html.
2021 ] Artificial Intelligence & Antitrust  423
(PAI), which includes a wide variety of the world’s largest AI and
technology companies .13
It is true, though, that some of the longer -term cooperation
strategies will require companies to sacrifice a large amount of
profits  (Windfall Clause) or effectively shut down or fundamentally
change their business  (Assist Clause) .14 These sacrifices can be
justified by the extreme risks ass ociated with advanced AI systems ,
as we will go on to discuss. Indeed , several  leading AI companies
have committed to the  goal of ensur ing the safe and beneficial
development of AI, such as  DeepMind ( part of  Google) and
OpenAI.
Scope and Contribution
The tensions between AI strategies15 and competition law
are potentially significant, yet currently underexplored in AI
governance. This Article builds on work at this intersection .16 We

13 PAI is a non -profit  organization that brings together 100 ‘Partners’ including
the leading Western technology companies , NGOs such as Amnesty, and
academic groups. It shares best practice, conducts research and XX. It was
founded in 201X and is based in San Francisco.
14 For further discussion of the distinction between near - and longer -term, see
infra  Section  I (Why is Cooperative AI Development Desirable?) .
15 AI strategy is the study or practice of high -level plans for how specific private -
or public -sector actors can ac hieve their AI -related goals. See Ó hÉigeartaigh,
Whittlestone, Liu et al. , supra note 3.
16 See generally  Miles Brundage, Shahar Avin, Jack Clark et al. , The Malicious
Use of Artificial Intelligence: Forecasting, prevention, and mitigation ,
arXiv:1802.07228 ( Feb. 18, 2018) https://arxiv.org/pdf/1802.0722 8.pdf; Askell,
Brundage & Hadfield, supra  note 6; Haydn Belfield, From Tech Giants to a Tech
Colossus: Antitrust Objections to the Windfall Clause  (Mar. 15, 2019)
(unpublished manuscript) (on file with author) ; Brundage, Avin, Wang et al. ,
supra  note 4; Dakota Foster & Zachary Arnold,  Antitrust and AI: How Breaking
Up Big Tech Could Affect the Pentagon’s Access to AI, CENTER FOR
SECURITY AND EMERGING TECHNOLOGY (May 2020); Cullen O’Keefe,

424 Yale Journal of Law & Technology  [Vol. 23
also draw on the academic literature17 and relevant jurisprudence18
of relevant areas of competition law, including its future direction
of travel in regulating emerging technology and AI .19
Based on our legal analysis, the objective of this paper is to
advise on whether forms of AI cooperation raise competition law
risks ,20 and if so how these competition law risks can be practically
mitigated. We assume that we want both to uphold competitio n law,
and for these forms of AI cooperation to  be lawful and to  succeed.
This paper does not comment on whether the competition law
regime hinders or furthers the aims of AI governance more broadly.
Neither does it attempt to compare the potential benefit s to AI
governance from competition law against the costs.

How W ill National Security Considerations Affect Antitrust Decisions in AI? An
Examination of Historical Precedents, FHI WORKING PAPER (forthcoming);
Sophie -Charlotte Fischer et al. , Levers of Influence of the USG on AI
Development , FHI WORKING PAPER (forthcomi ng).
17 See e.g., Lina Khan, Amazon's Antitrust Paradox , 126 Y ale L.J. 710; Matt
Stoller, Goliath: The 100 -Year War Between Monopoly Power and Democracy
(2019).
18 See e.g., Case AT.40099, Google Android, supra  note 10; DIGITAL
COMPETITION EXPERT PANEL , UNLOCKING DIGITAL COMPETITION - REPORT OF
THE DIGITAL COMPETITION EXPERT PANEL , HM TREASURY  (2019),
https://assets.publishing.service.gov.uk/government/uploads/system/uploads/atta
chment_data/file/785547/unlocking_digital_competition_furman_review_web .p
df; Elizabeth Warren, Here’s how we can Break Up Big Tech , Medium (Mar.  8,
2019), https://medium.com/@teamwarren/heres -how-we-can-break -up-big-tech-
9ad9e0da324c.
19 The authors combine on the one hand, nine years’ experience representing
clients such as NVIDIA, Google, Samsung and Sony in competition and
technology cases before the EC and other global regulators; and on the other seven
years’ experi ence developing and researching policy, es pecially around
catastrophic risk and advanced AI.
20 Any reference to “risk” will refer to risk of breaching competition law, unless
otherwise indicated.
2021 ] Artificial Intelligence & Antitrust  425
Our Article suggest s strategies that further the aims of both
competition law and socially beneficial cooperat ion, seeking to
reconcile the two . Our suggestions on how to reduce the ri sk of
breach ing Article 101(1) —that is,  by bringing an agreement outside
of the prohibition altogether, or relying on the exemption for
countervailing efficiencies  under  (Article 101(3))  —are legitimate
and lawful mitigation strategies. Our focus is on Article 101(1).
However,  EU competition law is broader than just this prohibition
and includes rules on abuse of dominance, merger control and state
aid. It follows that even if a form of cooperation complies with
Article 101(1), one must still check  for compliance with competition
law more broadly.
Strategies for Cooperation
We examine fourteen specific proposed cooperation
strategies to encourage safe and responsible AI development that are
under consideration within AI governance. We group these into two
broad categories: longer -term cooperation strategies intended to
apply when AI development is more advanced ; and nearer -term,
more general cooperation strategies. For each cooperation strategy,
we explain what it is, why it furthers AI governance aims, what
competition concerns it might raise, and how to address those
concerns.
In the coming decades, if AI continues to progress rapidly as
a field,  “transformative AI ” may be developed and deployed . In this
426 Yale Journal of Law & Technology  [Vol. 23
Article , we use the term “transformative A I” (TAI) to refer to
advanced AI systems that cumulatively could be  as transformative
of the international economy and society  as the industrial
revolution.21 AI has many possible uses across economy or
society —it is a general -purpose technology like the s team engine or
electricity, and may be as transformative. Current AI systems are
still mostly “narrow ,” or created specifically for a unique or targeted
task or class of tasks such as voice recognition, fraud detection or
vehicle automation. However, the f ield is working towards artificial
general  intelligence (AGI), defined by leading AI company OpenAI
as “highly autonomous systems that outperform humans at most
economically valuable work .”22 One can view AGI as  a subset of
TAI. F or the purposes of our analysis, we will refer to TAI instead
of AGI,  as it is a broader concept that covers a wider set of AI
systems.
Part I  explains why cooperation is desirable for AI
development, and distinguishes between forms of cooperation that
are relevant in the nearer term and in longer term. Part II provides a
brief background on EU competition law, including the prohibition
on anti -competitive agreements, some key concepts, its
investigation and enforcement powers , and an assessment of

21 Ross Gruetzemacher & Jess Whittlestone, The Transformative  Potential of
Artificial Intelligence , COMPUTERS AND SOCIETY , arXiv preprint
arXiv:1912.00747 (2020).
22 OpenAI Charter , OPENAI (9 April 2018),  https://openai.com/charter/.
2021 ] Artificial Intelligence & Antitrust  427
relevant  future developments. As it is introductory, it may be most
relevant to those unfamiliar with EU competition law.
The subsequent legal analysis examines the compatibility of
the fourteen forms of AI cooperation outlined above with
competition law, and sug gests ways to mitigate any competition law
risks. Part III analyze s the Assist Clause and the Windfall Clause .
These are two strategies designed to address longer -term risks.
Although companies will ideally agree to be bound by these
agreements today, they  will not implement them until a future point
in time.
Part IV examines nearer -term, general trust -building
cooperation including agreements, information exchange and
standard setting . Trust -building strategies include agreements
between competitors, mutua l monitoring and information
exchanges, standard setting to proliferate best practices around an
industry, and mergers.23 To promote cooperation, companies should
ideally implement these trust -building strategies now, and sustain
them over the long -term tr ajectory of TAI development.  In Part V ,
we conclude.

428 Yale Journal of Law & Technology  [Vol. 23
I. WHY IS COOPERATIVE AI DEVELOPMENT DESIRABLE ?
Cooperation is likely necessary in order for competing
artificial intelligence (AI) companies to develop AI systems
responsibly —that is, safely, securely and with broadly distributed
benefits .24 We will briefly explain the types of AI risk s that arise in
both the nearer and longer term, and how  cooperation can help to
reduce them .
As discussed above, AI R&D is developing rapidly, with
dramatic brea kthroughs in language models, robotics, drone swarms
and basic scientific research , such as protein -folding . Such progress
may lead to better goods and services at lower prices , as well as
significant leaps forward in science and medicine .25 A leading
subfield within AI is  “machine learning .” Machine learning systems
can iteratively improve their performance over time throug h
experience by “learning ,” that is , through  identifying patterns or
relationships in data with data analysis .26 Many of the benefits a nd
risks arising from AI systems come from their unique ability to
“learn ” in this way.

24 Askell, Brundage & Hadfield, supra  note 6 . Other fields in which cooperation
has proved useful include biotechnology, computer security, etc. Publication
Norms for Responsible AI: Ongoing Initiative , PARTNERSHIP ON AI,
https://www.partnershiponai.org/case -study/publication -norms/ (last accessed
Aug. 25, 2020).
25 Allan Dafoe, AI Governance: A Research Agenda , CTR. FOR GOVERNANCE OF
AI (Aug. 27, 2018 ), at 10.
26 Shin-Shin Hua, Machine Learning Weapons  and International Humanitarian
Law: Rethinking Meaningful Human Control , 51(1) GEORGETOWN J. INTERNAT ’L
L. 117, at 124.
2021 ] Artificial Intelligence & Antitrust  429
Several near -term risks from AI are relevant to our analysis .
For example, AI can generate non -transparent  or unexplainable
outcomes. Many machine learning models generate their results in
a way that is beyond the interpretive capabilities of human -scale
reasoning.  For example , in a model with billions of parameters  it can
be difficult to know which set of learnt patterns  and “weights ” on
particular “neurons ” has led to a particular outcome.  In these cases,
the rationale of algorithmically -produced outcomes that directly
affect decision subjects remains opaque to those subjects. This
opacity of the model may be dee ply problematic , for example , where
the processed data could harbor  traces of discrimination, bias,
inequity, or unfairness.  This is particularly the case when people use
the model  to inform high consequence decisions , such as those
dealing with education,  credit and loans, welfare benefits, or
criminal sentencing .27
In some cases, AI systems may also lead to unreliable,
unsafe , or poor quality outcomes. AI safety issues may arise from a
variety of factors, such as irresponsible data management, poor
design  and production processes, and inadequate deployment
practices. These outcomes can directly harm individuals and
undermine public trust in potentially  societally beneficial AI

27 Understanding Artificial Intelligence Ethics and Safety , ALAN TURING
INSTITUTE , at 4-5.
430 Yale Journal of Law & Technology  [Vol. 23
technologies.28 In addition to the general categories of AI harm
outlined above,  other forms of harm from AI include  amplification
of bias,29 loss of privacy30, propagation of disinformation,31 and
increased unemployment.32
There is considerable disagreement over ho w far and how
quickly the field of AI will  progress , but experts believe that the
capabilities of AI systems may outstrip those of humans across a
range of previously hard -to-automate tasks in the coming decades .33
As discussed above, this  future form of AI - so-called TAI - could
radically transform the distribution of welfare, wealth, or power, to
an extent comparable to the Industrial Revolution . Potential positive
changes include a significant increase in wealth, health, and well -
being. TAI could also enable new forms of e ffective democratic
decision -making and accountability, empowering democracy.

28 Id.
29 Karen Hao. This is how AI bias reall y happens -and why it’s so hard to fix , MIT
TECH. REV. 2019 , https://www.technologyreview.com/s/612876/this -ishow -ai-
bias-really -happensand -why-its-so-hard-to-fix/.
30 Artificial intelligence advances threaten privacy of health data: Study finds
current laws and regulations do not safeguard individuals’ confidential health
information , SCIENCE DAILY  (2019 ), https://www.sciencedaily.com/releases/201
9/01/190103152906.htm.
31 Irene Solaiman et al. , Release Strategies and the Social Impacts of Language
Models , ARXIV (Aug. 2019) , http://arxiv.org/abs/1908.09203.
32 Carl Benedikt Frey and Michael A Osborne , The Future of Employmen t: How
Susceptible Are Jobs to Computerisation? , TECH. REP. THE OXFORD MARTIN
PROGRAMME ON TECHNOLOGY AND EMPLOYMENT  72 (2013 ),
https://www.oxfordmartin.ox.ac.uk/publications/the -future -of-employment/ .
33 Katja Grace, John Salvatier, Allan Dafoe, Baobao Zhang, and Owain Evans ,
When Will AI Exceed Human Performance? Evidence from AI Experts ,
ARXIV:1705.08807 [Cs]  (May 24, 2017 ), http://arxiv.org/abs/1705.08807.
2021 ] Artificial Intelligence & Antitrust  431
However, TAI could also lead to negative transformative
effects. It may radically increase economic inequality or provide
new tools of state repression and control. In internatio nal security,
TAI could significantly alter key strategic parameters, such as the
security of nuclear retaliation and the offense -defen se balance .
Contemporary AI systems cause safety accidents. Safety problems
with advanced TAI could be much more catastro phic.
So-called “harmful race dynamics ” can also arise in the
development of  TAI. The first groups to develop and deploy
advanced TAI could gain extreme power and wealth. These rewards
could drive a fierce technology race, where actors are strongly
incentivized to trade -off against other values (like safety,
transparency, accountability, and democracy) in order to increase
the probability of gaining a competitive advantage in the race. This
could catastrophically compromise responsible AI development,
especially concerns around AI safety.34
The complex and potentially catastrophic risks posed by AI
have led to cooperation between AI companies becoming a major
focus of AI governance. This  paper examines fourteen concrete
proposals for cooperation that encourage responsible AI
development in order to mitigate both near -term and longer -term AI
risks.

34 Dafoe, supra  note 25, 42-43. See also  Ó hÉigeartaigh, Whittlestone, Liu et al. ,
supra note 3.
432 Yale Journal of Law & Technology  [Vol. 23
Cooperation between AI companies can help mitigate these
risks in a number of ways. Cooperat ion could speed up responsible
AI development, for example , by pooling research on a socially
beneficial application ( e.g., image recognition for crop diseases in
the Global South) or sharing details of a particularly good way to
debias a model. Companies might agree to standards to help enhance
AI safety, privacy , or guard against bias. Auditing mechanisms can
help ensure that AI companies are keeping to their promises to
develop  systems that are safe, predict able, privacy -preserving, and
non-biased. These  kinds of cooperation benefit society and
consumers. Cooperation  strategies include agreements between
competitors, mutual monitoring and information exchange s, and
standard setting  to proliferate best practice s around an industry.35 To
promote trust build ing, companies should ideally implement these
near-term strategies  now, and sustain them  over the long -term
trajectory of TAI development.
Cooperation can also help in addressing longer -term harms
if and when we approach  TAI. Although companies will ideall y
agree to be bound by these agreements today, they may not be
implemented until a future point in time. The Windfall Clause has
been suggested as a way to secure an ex ante commitment from AI

35 Note that in a forthcoming companion piece we will assess AI governance
proposals that might intersect with abuse of dominance, public procurement
and/or state aid concerns.
2021 ] Artificial Intelligence & Antitrust  433
companies that they will redistribute their profits where those  profits
exceed a certain threshold. Second, the Assist Clause has been
proposed to address the risk of AI companies “racing ” to develop
TAI first and underinvesting resources ( such as researcher time or
money) into system safety and reliability as a result.
We detail further t he risks that each form of cooperation
seeks to address in the sections analy zing the fourteen proposals.
II. BACKGROUND : THE PROHIBITION  ON ANTI-COMPETITIVE
AGREEMENTS UNDER ARTICL E 101
Competition law consists of rules that seek to protect the
process es of market competition. The benefits of competition
include  lower prices, higher quality products, more innovation, less
concentration of power , and greater efficiency than would be
achieved under monopoly conditions. Consumer welfar e, which
focuses specifically  on gains for consumers as opposed to society at
large,36 increases with more  competition. Article 101(1) of the
Treaty on the Functioning of the European Union (TFEU ) is a key
tool to preserve the rivalry and strategic uncertainty between
competitors that drives the competitive dynamic, and prohibits any
agreements that are capable of restricting competition.  This section
sets out a broad overview of Article 101(1) TFE U, the key

36 The growth of Neo -Brandeisian or sustainability concerns in competition law
is increasingly challenging the focus on consumers rather than society as a whole.
434 Yale Journal of Law & Technology  [Vol. 23
competition law provision that applies to proposed forms of
cooperation between AI companies.37
A. Article 101( 1) Prohibition
Article 101(1) TFEU prohibits agreements that have an
effect on trade between European Union countries and restrict
competition in the EU. The text reads as follows:
“The following shall be prohibited as incompatible with the
internal market: all agreements between undertakings, decisions by
associations of undertakings and concerted practices which may
affect trade bet ween Member States and which have as their object
or effect the prevention, restriction or distortion of competition
within the internal market...”
The four key elements to a breach of Article 101(1) are: (1)
there must be some form of agreement, decision , or concerted
practice between undertakings; (2) which may affect trade between
EU member states; (3) which has as its object or effect the
restriction, prevention , or distortion of competition within the EU;
and (4) which has an appreciable effect on comp etition.  We explore
the key elements of Article 101(1) below.
First, a n analysis under Article 101(1) may require the
definition of a “relevant market ” on which effects on competition

37 If the reader is already familiar with the basics of competition law, we invite
them to skip to Sect ion III (Legal Analysis of Proposed forms of Cooperation:
Long -term Strate gies) .
2021 ] Artificial Intelligence & Antitrust  435
are being assessed. This definition identifies the competitors of the
undertakings involved who are capable of exerting competitive
pressure on other undertakings.38 The market is d efined along two
dimensions . The product market  is the set of all products that a
sufficient number of consumer s would view as interchangeable .39
The geographic market  is the area of supply of the product where
the competitive conditions are sufficiently homogenous, when
compared to neighbouring areas.40
A particular difficulty in the context of AI development is
that the product market may be difficult to define with precision
because  product development is fast-moving  and/or the relevant
market does not yet exist  (if the product is still  at the R&D stage ).
Unlike in other areas of competition law (such as abuse of
dominance), defining the relevant market on which the parties to an
agreement operate is not a prerequisite  to every Article 101(1)
analysis.41 This paper will seek to define the relevant market only

38 Commission Notice on the Definition of Relevant Market for the Purposes of
Community Competition Law (97/C372/03), 1997 O.J. C 372  (“Market
Definition Notice”) , ¶ 2.
39 This is so -called “demand -side substitutability ” which is assessed by a
hypoth etical exercise that asks whether the parties' customers  would switch to
readily available substitutes in response to a hypothetical small (in the range 5 %
to 10 % ) but permanent relative price increase in the products being considered.
If substitu tion w ere enough to make the price increase unprofitable because of the
resulting loss of sales, additional substitutes are included in the relevant market
(id., ¶ 17). Another factor is “supply -side side substitutability, ” which refers to
the ease with which a supplier could switch its supply from one product to another.
40 Market Definition Notice , supra  note 3 8, at ¶ ¶ 7 -9.
41 Case C -439/11, P Ziegler SA v Commission, EU:C:2013:513, at ¶ 63 (holding
that it is unnecessary, in certain circumstances, to define the relevant market in
order to determine if there is an appreciable effect on trade between Member

436 Yale Journal of Law & Technology  [Vol. 23
when it is necessary for the substantive analysis in eac h particular
case.
Second, Article 101(1) only applies to an agreement between
two or more independent “undertakings. ” It does not apply , for
example, to intra -group agreements  i.e. agreements between entities
within the same economic gr oup.42 An undertaki ng can include any
natural or legal person that is en gaged in economic activity (that is,
offering goods  or services to a given market).43 The a ctivities of a
non-profit organi zation can also be “economic activity ” if the
private sector provides them or  could provide  them —there need not
be a profit -earning motive or economic purpose.44 EU competition
law is likely to treat an association of AI companies such as PAI  as
an undertaking under competition law.
Third, any form of “agreements, decisions, concerted
practices ” can fall within the scope of Article 101(1). Competition
law is not concerned with the form of an anti -competitive
agreement —otherwise , evasion of the law would be easy. The key

States for the purpose of Arti cle 101(1), namely where it is possible to establish
that the conduct in question is capable of affecting trade between Member States
and has the object or effect of preventing, restricting or distorting competition,
even in the absence of such a definitio n).
42 This includes for example an agreement between a parent company and its
subsidiar y. See e.g. Case  22/71 - Beguelin Import v. GL  Import
Export,  1971  E.C.R. 949 .
43 Case C -41/90, Hofner and Elser v Macrotron GmbH, 1991 E.C.R. I -1979, ¶  21;
Joined cases C -180/98 to C -184/98, Pavel Pavlov and Others v Stichting
Pensioenfonds Medische Specialisten, 2000 E.C.R. I -6451, ¶ 75.
44 Joined cases 209 to 215 and 218/78, Heintz van Landewyck SARL and others
v Commission, 1980 E.C.R. 3125, ¶ 88.
2021 ] Artificial Intelligence & Antitrust  437
factor is that there is  an expression of a joi nt intention or a “meeting
of minds ” to behave in a particular way between at least two
undertakings.45 Therefore, gentlemen's agreements, simple
understandings and “protocols ” have been held to be
“agreements ”.46 “Concerted practices ” refer s to instances where
businesses, although not entering into a binding agreement as such,
“knowingly substitute practical cooperation between them for the
risks of competition .”47 The broad concept of an agreement covers
the partial and conditional agreements in the negotiations that lead
up to a final agreement.48 If parties never implement an  agreement ,
that is irrelevant to the existence of an agreement.49 However,  if an
agreemen t is implemented, that could constitute a separate and
additional breach of Article 101(1), or could be treated as part of a
single and continuous infringement stemming from the original
agreement.50

45 Case T -41/96, Bayer v  Commission, 2000 E.C.R. III -3383, ¶ 69.
46 Case 41/69, ACF Chemiefarma NV v Commission, 1970 E.C.R. 661 ( Quinine );
Case T -53/03, BPB plc v Commission, 2008 E.C.R. II -1333, ¶ 72 ( Plasterboard );
Re Stichting Sigarettenindustrie Agreements OJ [1982] L 232/1 ( an
‘understanding’ between trade associations held to be an agreement). Case
IV/29.525 and IV/30.000 - SSI, Commission decision of 15 July 1982, 1982 O.J.
L 232.
47 Case C -48/69, Imperial Chemical Industrie s v European Commission
(Dyestuffs) , 1972 E.C.R. 6 19, ¶ 64.
48 Case No IV/35.691/E -4, Pre -Insulated Pipe Cartel , Commission Decision of 21
October 1998, 1999 O.J. L24/1, ¶ 133, upheld on appeal , Case T -9/99, HFB v
Commission, 2002 E.C.R. II -1487.
49 Case CO MP/39181, Candle Waxes, Commission decision of 1 O ctober 2008,
summary published in 2009 O.J. C295/17, ¶ ¶ 299-301, upheld  on appeal Cases
T-558/08, Eni v Commission, 2014 EU:T:2014:1080, ¶ ¶ 132 -133.
50 See infra  Section III.A (Assist Clause as a “Single and Continuous
Infringement” ).
438 Yale Journal of Law & Technology  [Vol. 23
Article 101(1) contains a non -exhaustive list of exampl es of
agreements that have as their “object or effect the prevention,
restriction or distortion of competition” within the EU. The wording
“object or effect” is significant. Agreements can have as their
“object ” the restriction of competition, wherein it i s unnecessary to
prove that it will provide anti -competitive effects. For an agreement
to be a “by object ” infringement, it must reveal, by its very nature,
a sufficient degree of harm to competition that an analysis of its
effects is not necessary to esta blish a breach of Article 101(1).51
Further, it tends to be difficult to argue that a “by object ”
infringement is exempt under Article 101(3) , though it is
theoretically possible to do so.52
“By object ” agreements between competitors include
restrictions s uch as price fixing, output  or sales limitations, paying
competitors to delay the launch of competing products, and
exchanging information that reduces uncertainty about competitor
behavior . Only where it is unclear that an agreement has as its object
the infringement of competition will it be necessary to consider
whether it might have the effect of doing so. These are so -called “by

51 Case C -67/13 P, Groupement des Cartes Bancaires v Commission,
EU:C:2014:1958. 12 , ¶ ¶ 49 and 57.
52 See e.g. Case C -439/09, Pierre Fabre Dermo -Cosmétique SAS v. Président de
l’ Autorité de la concurrence and Ministre de l’ économie, de l’ industrie et de l’
emploi, 2011 E.C.R. I - 9419, ¶ ¶ 49 -57.
2021 ] Artificial Intelligence & Antitrust  439
effect ” infringements and require a more thorough assessment of the
impact of the agreement on the market.
Anti-competitive agreement s between competing (or
potentially competing) parties at the same level of the supply chain
are called “horizontal restrictions .” On the other hand, “vertical
restrictions” are agreements between parties at different levels of the
supply chain, f or example  a supplier and its reseller. Horizontal
agreements are generally regarded as more serious breaches of
Article 101(1) because they involve coordination between
competitors and are therefore more likely to have a significant
adverse impact on comp etition.53
The analysis of this note will focus on horizontal agreements
rather than vertical agreements, as those are the more common
forms of cooperation currently discussed within AI governance.
B. Article  101(3) Exemption
Competition law is concerned  with protecting competition to
further  consumer and societal welfare.  If a company can
demonstrate that the agreement in question can give rise to
countervailing efficiencies  that are passed on , then the effects of the

53 The main reason why vertical restraints are generally seen as less harmful than
horizontal restraints is because, in a horizontal relationship, a competitor tha t
exercises its market power and increases its prices may  benefit its competitors.
This may incentivize competitors to induce each other to behave anti -
competitively. In a vertical relationship, the product of one is the input for the
other. Therefore comp anies in a vertical agreement usually wish to prevent the
exercise of market power by the other (Guidelines on Vertical Restraints, 2010
O.J. (C 130) 1 – 46 (“Vertical Guidelines”), ¶ 98).
440 Yale Journal of Law & Technology  [Vol. 23
Article 101(1)  prohibition will be d isapplied  in relation to that
agreement . In other words, Article 101(3) effectively acts as an
exemption to Article 101(1).
To qualify for this legal exception, an agreement must satisfy
each of the four conditions contained in Article 101(3).  The burden
of proof rest s on the parties seeking to rely on the exemption.54 The
undertakings must put forward “convincing arguments and
evidence ” that the conditions of that provision are satisfied.55
The conditions of the Article 101(3) exemption are as
follows: (1) the agreement must contribute to improving the
production or distribution of goods or to promoting technical or
economic progress; (2) consumers must be allowed a fair share of
the resulting benefit; (3) only restrictions indispensable to the
achievement of those objectives can be imposed on the parties
concerned; and (4) the parties should not be afforded the possibility
of eliminating competition in respect of a substantial part of the
products in question. We will look at each of these elem ents in turn.

54 Article 2 , Council Regulation (EC) No 1/2003 of 16 December 2002 on the
implementation of the rules on competition laid down in Articles 81 and 82 of the
Treaty (Text with EEA relevance), 2003 O.J. (L1) 1 (“Regulation 1/2003”) . Note
that under Wouters , the European Court of Justice found that a regulator y rule fell
outside Article 101(1) altogether where ‘reasonably considered necessary in order
to ensure the proper practice of the legal profession.’ (Case C -309/99, Wouters v
Algemene Raad van de Nederlandsche Orde van Advoca ten, 2002 E.C.R. I -1577)
Altho ugh the circumstances that applied in Wouters  were different to the Assist
Clause (for example, in Wouters  the offending agreement had a public law
character), it highlights an alternative ‘route’ out of the Article 101(1) pro hibition
other than the Article 101(3) exemption. Similarly, see the ‘ancillary restraints’
analysis in discussion infra  Section IV.B  (Seconding staff (researchers or
engineers)).
55 Case C -68/12 Slovenska sporitelna, EU:C:2013:71, ¶ 32.
2021 ] Artificial Intelligence & Antitrust  441
First, an agreement must produce efficiency gains that can
be clearly substantiated and shown to flow directly from the
agreement. This is a relatively high evidential standard  and requires
verification : the nature  of the claimed efficiencies , the direct causal
link between the agreement and the efficiencies; the likelihood  and
magnitude  of each claimed efficiency , and how and when  each
claimed efficiency would be achieved. Generally, the parties are
most likely to achieve  efficiency gains if they combine
complementary skills and assets, such as different research
capabilities. In contrast, if the parties’ skills and assets are
substitutes rather than complements, an agreement is unlikely to
lead to inefficiencies that can accrue to consumers.56
Second , any restrictions on competition from the agreement
should not go beyond what is necessary to achieve the efficiency
gains. In other words, if there is a realistic alternative57 that can
achieve the same ethical or safety benefits  without reducing the
competition on the market, then the agreement will not satisfy
Article 101(3). The parties must explain, for example, why they
could not have achieved the same efficiencies by acting alone.58

56 Communication fro m the Commission — Notice — Guidelines on the
application of Article 81(3) of the Treaty, 2004 O.J. (C 101) 97 (“ Article 101(3)
Guidelines”) , ¶ ¶ 64 -68.
57 There must be no other economically practicable and less restrictive means of
achieving the efficiencies, id., ¶ 75.
58 Id. at ¶ 76
442 Yale Journal of Law & Technology  [Vol. 23
Third, efficiency gains must be passed onto cu stomers and
outweigh the  anti-competitive effects —the ethical or safety
improvements  should  outweigh any negative consumer effects  such
as higher prices or lower output that result from  the loss of
competitive dynamics.59
Whether efficiencies are passed o n to consumers will depend
on whether there is enough residual competition on the market and
is closely linked to the fourth condition of Article 101(3) ,
prohibiting the elimination of competition.60 This condition requires
an evaluation of the extent to which the agreement will reduce
competition in the market. The EC considers b oth actual and
potential competition when making this assessment.61 It examines
the capacity of actual competitors to compe te and their incentive to
do so when assessing actual competition .62 The analysis of potential
competition requires looking at barriers to entry facing undertakings
that are not already competing within the relevant market, and
whether there is the real po ssibility for new entry on a significant
scale.63
Article 101(3) is similar to the “Rule of Reason ” under § 1
of the Sherman Act. Under § 1, agreements among competitors that

59 Id. ¶ 85.
60 Id. ¶ ¶ 95 -97.
61 Id. ¶ 108.
62 Id. ¶ ¶ 109-10.
63 Id. ¶ ¶ 114-15.
2021 ] Artificial Intelligence & Antitrust  443
fix prices, allocate markets or customers, or restrict output are
treated as per se unlawful. Other agreements are judged under the
“Rule of Reason” when they have plausible efficiency justifications.
The Rule of Reason  weighs the potential anticompetitive effects of
the agreement against its procompetitive benefits.
Competition law does not prohibit all forms of cooperation
that restrict competition. For example, it recognize s certain
categories of cooperation between competitors as beneficial for the
partie s and  for consumer welfare. The  EC acknowledges this in its
Horizontal Cooperation Guidelines, noting that it “can be a means
to share risk, save costs, increase investments, pool know -how,
enha nce product quality and variety, and launch innovation faster .”
This paper  will assess two of these type s of cooperation, R&D
cooperation and standardization  agreements. Sections below will
analyze  how these forms of cooperation may be structured to reduce
the risk of breaching Article 101(1) : either because they do not have
any restrictive effects (thereby falling outside Article 101(1)
altogether), or because any efficiencies that they bring outweigh
restrictive effect on competition, therefore engaging t he Article
101(3) exemption. An agreement may benefit from the Article
101(3) exemption either through assessment of individual
agreements on a case -by-case basis, or categories of agreements via
one of the block exemptions. When an agreement fulfils the
444 Yale Journal of Law & Technology  [Vol. 23
conditions set out in a block exemption regulation, the agreement is
automatically valid and enforceable.
C. Investigation and Enforcement Powers
Strategic cooperation between AI companies seeks to
prevent  a range of harms, from biased outputs to potentially
catastrophic accidents , and is likely to involve large, well -resourced
companies. Arguably, if the worst -case scenario is a fine from the
EC, large companies  might simply “price -in” this penalty as an
unfortunate but necessary  cost to achieve a f ar greater gain. It may
follow that competition law cannot practically change the behaviour
of these companies . However, this position is overly simplistic.
For one thing , the EC has the power to apply competition
law to any company a s long as the conduct has an effect on trade in
the EU ( called the “qualified effects ” doctrine).64 This extends to
conduct or agreements entered into outside the EU if it has an effect
within the EU, and covers companies that are not incorporated in the
EU.65 The string of competition law cases against US companies

64 Case C‑413/14 P, Intel v Commission, 2017 ECLI:EU:C:2017:632.
65 Id.. As well as the qualified effects doctrine, which requires there to be
‘immediate, substantial and foreseeable effects’ on within the EC , the EC can also
establish jurisdiction on two other grounds - the single economic entity doctrine
and the implementatio n doctrine, respectively. Under the single economic entity
doctrine, a non -EU parent company of a group of companies can be held liable
for an infringement committed by an EU subsidiary within the same group (Case
C-48/69, Imperial Chemical Industries v Eu ropean Commission (Dyestuffs),
supra  note 4 7. Under the implementation doctrine, the decisive factor to establish
jurisdiction is where the conduct was implemented not where it was entered into
(Cases C -89/85, C -104/85, C -114/85, C -116/85, C -117/85 and C -125/85 to C -
129/85, A Ahlström Osakeyhtiö and others v European Commission, 1988
EU:C:1988:447  (Wood Pulp ).
2021 ] Artificial Intelligence & Antitrust  445
like Google, Qualcomm , and Microsoft , and ongoing cases against
Apple and Amazon ,66 demonstrate this .67 Another way for the EC to
establish subject  matter jurisdiction is by using an AI company ’s EU
subsidiary as an “anchor .”68
Even if AI cooperation raises  competition concerns  in
principle , in practice the EC  might never find out about it. This is
unlikely , however . The EC uses market intelligence monitoring to
identify potential issues and it is likely to focus  particularly on
digital markets and the AI development space in particular for the
foreseeable future.69
Another important avenue for identifying breaches is
through complaints from either a customer or competitor. A
disgruntled competitor who has fallen behind in the lucrative AI
development race  could bring a claim against those leading the race
in order to clip their wings. That disgruntled competitor may ha ve

66 See, e.g., Case AT.39740, Google Search (Shopping,), supra  note 10; Case
AT.40099, Google Android, supra  note 10; Case AT.40411, Google Search
(Adsense,), supra  note 10; Case COMP/C -3/37.792, Microsoft, supra  note 10;
Case AT.39711, Qualcomm (predation), Commission decision of 18 July 2019;
Case AT.40220, Qualcomm (exclusivity payments), Commission decision of 24
January 2018, 2018 O.J. C269/25.
67 Under the economic entity doctrine, the conduct of a subsidiary active in the
EC is attributed for antitrust purposes to the parent company se ated outside the
EC but exercising its corporate control on the subsidiary ( Case C -48/69, Imperial
Chemical Industries v European Commission (Dyestuffs) , supra  note 71 , at ¶ ¶
125-141.
68 Wood Pulp ,, A Ahlström Osakeyhtiö and others v European Commission , supra
note 65, at ¶ ¶ 11 -23. The question of extraterritorial application of EU
competition law, including the power of the EC to compel compliance or punish
non-compliance with its laws (enforcement jurisdiction), is further discussed in
our forthcoming paper on EU competition law.
69 See discussion infra  Section II.B (Investigation and Enforcement Powers) .
446 Yale Journal of Law & Technology  [Vol. 23
been party to some of the discussions leading up to forms of
cooperation, and therefore be able to provide insightful evidence to
a regulator.70 Furthermore, the pressure that a well -resourced
complainant can place on regulators to bring an investigatio n against
their rivals should not be underestimated. In many cases,  powerful
complainants are the driving force behind EC investigations,
including the Google  and Microsoft  cases .
As mentioned above, the regulation of digital markets and
Big Tech and the  development of AI in Europe are currently hot
topics in the EU. This focus suggests that any conduct between AI
companies, especially if they happen to be one of the Big Tech
companies, may be heavily scrutini zed. If it identifies potential
issues, t he EC is likely to prioritise the investigation of these cases
over others. Furthermore, the uncertainty hanging over companies
during the course of investigations can have a detrimental effect on
their incentives and a bility to innovate. The length of investigations
exacerbates this uncertainty —for example, the Google Search
investigation lasted for 8 years.71 Competition law investigations

70 Complainants are also entitled to certain rights to participate in any
investigation that may arise from their comp laint, including providing and
receiving evidence on the file, as well as to potentially participate in any oral
hearing. (Commission Regula tion (EC) No 773/2004 of 7 April 2004 relating to
the conduct of proceedings by the Commission pursuant to Articles 81 and 82 of
the EC Treaty, 2004 O.J. (L 123), Article 6(1) & 6(2)).
71 Case AT.40411, Google Adsense, supra  note 10; see also Damian Reece and
Stephen Castle, Microsoft rivals line up to sue after EU ruling , THE
INDEPENDENT (March 25, 2004),
https://www.independent.co.uk/news/business/news/microsoft -rivals -line-up-to-
sue-after-eu-ruling -756881.html.
2021 ] Artificial Intelligence & Antitrust  447
against c ompanies that are active in digital markets , such as AI , may
be more li kely to see  longer competition law investigations . This is
because  the complexity and fast -moving nature of the industry
makes it more difficult for regulators to make their competition
assessment. As a result, regulators  may seek more in-depth
information  from the business in order to make their assessment .
Serious consequences flow from competition law
enforcement. For an intentional or negligent breach of Article 101,
the EC can impose a fine of up to 10% of annual global turnover.
The agreement will al so be void and unenforceable. Examples of
fines include EUR  4.3bn for Google ’s Android  case in 2018, EUR
997m for Qualcomm in 2018 for its case on exclusivity payments
and EUR 1.06bn for Intel in 2009. These are in addition to the time
and resources necess ary to defend long , in-depth competition cases.
However,  this does not mean that a well -resourced AI
company could simply choose to pay the fine and carry on with its
unlawful cooperation strategy. Aside from the fine, the EC has the
power to order the termination of the offending conduct. It can make
an order for cessation of the infringing conduct, as well as an order
requiring inter alia  the making of supplies to other parties on
particular terms,72 the suspension of a proposed merger pending

72 C.f. the Google Search case, where the EC  avoided specifying precisely how
Google should remedy its abuse, confining itself instead to ordering that the
conduct be terminated and requiring Google to submit evidence of compliance

448 Yale Journal of Law & Technology  [Vol. 23
investigation, and the divestment of assets73 (including the
separa tion of previously combined assets ).74
If the EC  makes an  order  and the company fails to follow it ,
the EC can impose periodic penalty payments of up to 5% of the
average daily turnover in the preceding business year in order to
enforce its order .75 Altho ugh the EC has used this power relatively
rarely, in 2006 and 2008 it imposed a daily penalty of EUR  1.5
million on Microsoft (tota lling EUR  280.5 million ) for failure to
comply with an obligation to provide interoperability information
contained in a deci sion from 2004.76
The EC can impose interim measures on companies prior to
the finding of infringement. This allows the EC to order the
company under investigation to stop certain behavior , during the
course of the investigation, if it deems that there is the risk of serious
and irreparable damage to competition  caused by that company.77
Third parties harmed by breaches of EU competition law can also

within 90 days of the decision or face daily penalties of up to 5% o f global group
turnover. See Case AT.39740, Google Shopping, supra  note 10, at Recital 700,
summarizing the obligations and Pinar Akman, The Theory of Abuse in Google
Search: A Positive and Normative Assessment Under EU Competition Law , 2017
J. L. T ECH. & POLICY  365.
73 However, the EC may only impose a divestment remedy in limited
circumstances (i.e. , where other, less interventionist remedies are not sufficient.).
74 Article 7, Regulation 1/2003 , supra  note 54.
75 Id., Article 24.
76 This amount was later reduced by EUR 39million on appeal to the General
Court (Case T -167/08, Microsoft Corp v European Commission, judgment of 27
June 2012).
77 Regulation 1/2003, supra note 54, Article 8. Though rarely used, interim
measures were impos ed on Broadcom in October 2019 and the EC has expressed
a desire to use them more freque ntly in digital markets in future.
2021 ] Artificial Intelligence & Antitrust  449
bring a private damages action in their domestic courts. These can
include private action claims seeking large sums in damages sums.78
This paper analyze s the state of competition law as at the
date of writing. However, EU competition law is going through a
series of important changes. These include the impact of the
COVID -19 crisis, Brexit , and the focus on regulating digital markets
and Big Tech. A det ailed assessment of these trends and events are
outside the scope of this paper, but we draw out a few of the key
themes and implications below.79
The EC has highlighted that even during the exceptional
circumstances of COVID -19, it will continue to close ly monitor the
market for any breaches of EU competition law. However, the EC
has published a communication80 that permits certain forms of
cooperation between companies and trade associations that would
usually fall foul of competition rules81 if they are  necessary to ensure

78 For example, Barclays, Royal Bank of Scotland and three other banks are
currently being sued by investors for at least £1 billion o ver rigging of the foreign
exchange market in a class action in the UK ( Sean Farrell, Barclays, RBS and
Other Banks Face £1bn Forex Rigging Lawsuit , THE GUARDIAN (Jul. 29,
2019), https://www.theg uardian.com/business/2019/jul/29/barclays -rbs-banks -
forex -rigging-lawsuit -jp-morgan -citigroup -ubs). In another case, retailers
including Asda and Argos “are in line for potential billion -pound payouts after
the UK’s highest court ruled that transaction fee s charged by Visa and Mastercard
breached competition laws” ( Jane Croft, Mastercard and Visa face billion -pound
payouts after UK court ruling, FT (Jun. 17, 2020),
https://www.ft.com/content/e948aa14 -0f44-4e47 -ad2e -397452d859c5).
79 Some of these will be explored in our forthcoming paper, in particular the
regulation of big tech, see supra  note 35.
80 Temporary Framework for assessing antitrust issues related to business
cooperation in response to situations of urgency stemming from the current
COVID -19 outbreak, Apr. 8, 2020, 2020 O.J. (C 116 I) 02.
81 This includes measures to adapt production, stock management and possibly
exchange of information related to the distribution and production of certain
medicines.
450 Yale Journal of Law & Technology  [Vol. 23
the supply and adequate distribution of essential and scarce products
and comply with certain conditions.
The EC ’s communication clearly applies to the exceptional
circumstances of COVID -19. However, this might  introduce a
broader or more flexible approach, for example to the application of
the exemption under Article 101(3) for countervailing efficiencies,
allowing more forms of AI cooperation to comply with competition
law.
The UK left the EU on January 31, 2020. After that period,
there are unlikely to be major changes that will affect  the assessment
of forms of AI cooperation we analyze  in this paper under the Article
101(1) regime.82 Any cooperation and agreements between UK -
based or incorporated AI companies will still be within t he
jurisdiction of the EU competition rules and must comply with
Article 101(1) if they have an effect within the EU ( “effect on trade ”
rule).83
Where an agreement does not affect trade within the EU, for
example if it only has locali zed effects on the UK market, the UK
competition authority will apply the UK equivalent of Article
101(1), called the Chapter I prohibition. The substance of the

82 Brexit will have more significant impact on state aid rules, however that will be
assessed under our separate forthcoming paper, see supra  note 35.
83 This is in the same way that Asian and US businesses are subject to EU
competition law where their agreements and conduct affect EU markets. For
example, a UK participant in a global cartel will continue to face investigation
and fines by the EC .
2021 ] Artificial Intelligence & Antitrust  451
Chapt er I prohibition is very similar to Article 101(1), but over time
it is possible the UK courts will diverge in their interpretation of the
prohibition from the EU jurisprudence. This is because they  will no
longer be subject to a duty to interpret the UK r ules in a manner
consistent with competition case law of the European Court of
Justice.84
In addition, one of the current hot topics in EU competition
law—and competition law globally85—is the regulation of digital
markets and Big Tech. The EC in its Ex perts report , for example,
considered whether competition law should be strengthened or

84 Secti on 60 of the Competition Act 1998 (that contains the duty to interpret the
UK rules consistently with EU caselaw) is revoked by the Competition SI
(Competition (Amendment etc.) (EU Exit) Regulations 2019, SI 1993 No. 93) and
in its place a new pr ovision, s ection 60A, is inserted with effect from Brexit. The
new section 60A merely requires the CMA and courts to avoid inconsistency
between their decisions and EU law and the decisions of the European Court of
Justice before exit day. In addition, in the draft Brexit trade deal (UK -EU Trade
and Cooperation Agreement, dated 28 December 2020 accessible here:
https://ec.europa.eu/info/sites/info/files/brexit_files/info_site/tca -20-12-28.pdf),
both the EU and UK commit to “maintain their high standards of competition law”
to “effectively [address] anticompetitive practices [including] agreements
between economic actors, decisions by associations of economic actors and
concerted practices which have as their object or effect the prevention, restriction
or distortion of competition.” This mirrors the wording of Article 101(1). See UK-
EU Trade and Cooperation Agreement, Part 2 (Trade, Transport, Fisheries and
Other Arrangements), Title XI (Level Playing Field for Open and Fair
Competition and Sustainable Development), Chapter 2 (Competition Policy),
Article 2.2 ( Competition Law), Sub-article 1(a).
85 See also similar proposals by the regulators in the UK, France, Germany, USA
Australia, and others , as well as  recent joint pr oposed options for modernising EU
competition law by France, Germany and Poland, which include increased
scrutiny of Big Tech, as well as proposals to reform merger control rules to
facilitate the formation of European national champions.  The overall ef fect on
forms of AI cooperation is heightened scrutiny of AI companies, but possibly
more relaxed merger control rules if the AI companies merging are European. For
further discussion, see Joint Statement by France, Germany and Poland,
Modernising EU Compe tition Law  (Jul. 4, 2019 ),
https://www.bmwi.de/Redaktion/DE/Downloads/M -O/modernising -eu-
competition -policy.pdf?__blob=publicationFile&v=4.
452 Yale Journal of Law & Technology  [Vol. 23
reformed to sufficiently scrutinize digital markets and platforms.86
We offer a  fuller analysis of the implications of competition law ’s
rise against Big Tech in a s eparate paper. However, the overall effect
is likely to be an increased scrutiny of the AI sector.87
This goes hand in hand with the EU ’s central strategy of
encouraging AI development in Europe, as set out in the AI White
Paper. In that paper, the EC highlights the importance of data and in
training machine learning  systems , the “accumulation of vast
amounts of data” by Big Tech companie s, and the market
imbalances in relation to the access and use of data by SMEs. This
strategic focus on AI and big tech’s position of strength in this field
will probably only increase with advances in AI development, and
may result in a heightened level o f scrutiny over all AI companies
and over Big Tech’s activities in AI development.88
For example, the EC is currently considering an ex ante
regulatory regime for markets characterized by large digital
platforms that act as gatekeepers to ensure the mar kets “stay fair and
open .” On December 15, 2020, the EC published a proposed
regulation called the Digital Markets Act (DMA) on contestable and

86 See e.g.  JACQUES CRÉMER, YVES -ALEXANDRE DE MONTJOYE AND
HEIKE SCHWEITZER, SHAPING COMPETITION POLICY FOR THE
DIGITAL ERA, EC REPORT (4 April 2019),
http://ec.europa.eu/competition/p ublications/reports/kd0419345enn.pdf .
87 Id.
88 See European Commission Communication ‘Shaping Europe’s Digital Future’,
COM   67  (Feb.  19,  2020),
https://ec.europa.eu/info/sites/info/files/communication -shaping -europes -digital -
future -feb2020_en_4.pdf.
2021 ] Artificial Intelligence & Antitrust  453
fair markets in the digital sector.89 This regulation  targets  the
negative consequences arising from certain behavior s by platforms
acting as designated digital “gatekeepers” to the single market. The
proposed DMA addresses unfair practices by gatekeepers that either
fall outside the existing EU competition control rules, or which these
rules cannot always effectively tackle because of the systemic nature
of some behavior s, as well as the ex -post and case -by-case nature of
competition law. The powers in the DMA are additional and
complement ary to existing competition enforcement tools.
The prop osed DMA contains a  series of obligations and
prohibitions relating to self -preferencing, interoperability, data -
related practices and tying. The DMA Proposal gives  the EC new
enforcement and sanctioning powers, including a new power to
carry out targeted market investigations. Specifically, the
Commission would be empowered to conduct investigations to
identify designated gatekeepers and their core services, and to
ensure their compliance with the DMA.
It is still too early to know what the regime will eve ntually
look like and  understand  its full implications, but it will undoubtedly
add an additional layer of scrutiny to the conduct of AI companies.

89 Proposal For A Regulation Of The European Parliament And Of The Council
On Contestable And Fair Markets In The Digital Sector (Digital Markets Act),
COM/2020/0842  (Dec. 15, 2020 ).
454 Yale Journal of Law & Technology  [Vol. 23
III. LEGAL ANALYSIS OF PROPOSED FORMS OF COOPERATION :
LONG-TERM STRATEGIES
In the next two sections, we sur vey two broad types of
cooperation that are under consideration within AI governance. In
this Section, we consider the Assist Clause and the Windfall Clause.
Both strategies are designed to address risks in a scenario where TAI
is developed or nearing deve lopment.
Even though implementation of these strategies may not
occur  until some point in the future, competition law concerns can
arise as soon as the companies agree to be bound , regardless of if  or
when  the strategies are ever implement ed. For both the  Assist
Clause and Windfall Clause, we explain the relevant strategies , what
useful purpose s they would serve , what competition concerns they
might raise, and how to address those concerns. In the following
Section,  we analyze  other strategies to encourage safe and
responsible AI development , includ ing mutual or third -party
monitoring, standard -setting, and strategic mergers
A preliminary question is whether the EC will have
jurisdictio n to apply EU competition law to, for exam ple, a US AI
company that engages in an anti -competitive agreement with
another US company. We do not propose to look at whether
jurisdiction is established in every case, but we generally posit that
the EC ’s jurisdiction is likely to be established. This is becau se (1)
a market for AI products or  services is likely to be global, so any
2021 ] Artificial Intelligence & Antitrust  455
agreement between AI companies is likely to have direct or knock -
on effects on EU trade ; and  (2) the AI companies that are t he focus
of our discussion are likely to be large , global companies with
European subsidiaries.90
A. OpenAI Assist Clause
Key Recommendations
There is a risk that AI companies binding themselves to  an
Assist Clause similar to O penAI’s could breach Article 101(1). The
Assist Clause is essentially a  commitment from one AI compan y not
to compete with another . If the Assist Clause leads to AI companies
who are close competitors agreeing not to compete with each other,
this could lead to a significant reduction in competition given the
otherwise fierce competition between those companies that is now
lost.   A company c ould breach competition law as soon as it  reached
an agreement with another company to be bound by the Assist
Clause, regardless of when  or if it is implemented or triggered.
To mitigate this risk, OpenAI (and any other AI company)
should take care not to contact competitors to persuade them to
follow the Assist Clause, or otherwise seek to influence or reach an
understanding with competitors to that effect. This will help ensur e
that any company ’s decision to be bound by an Assist Clause is truly
unilateral, thus falling outside of the Article 101(1) prohibition.

90 As discussed in infra  Section  II.B (Investigation and Enforcement Powers) , the
EC’s jurisdiction c an be founded on both these grounds.
456 Yale Journal of Law & Technology  [Vol. 23
Separately, implementation of the Assist Clause through a
competitor assisting the Leader can also constitute a sepa rate breach
of Article 101(1), or form part of a single and continuous
infringement stem ming from the original plan, that is  the Assist
Clause itself.
To mitigate risks from the Assist Clause ’s implementation,
we advis e that cooperation occur  at as early a stage as possible.
Cooperation should also be widespread enough across the TAI
development industry to dampen harmful race dynamics, but still
leave sufficient competition in the market  to mitigate competition
law risk .
Amongst the differen t ways to implement the Assist Clause,
a company can significantly mitigate risks  if cooperation between a
competitor and the leading TAI deve loper does not lead to the loss
or shutdown of the competitor ’s AI development activities. It can
achieve this  by inter alia  shifting R&D and production to AI safety
rather than on product development.
Analysis
A central risk from TAI development is that it may result in
harmful race dynamics. Given the great rewards that may accrue to
the first to achieve TAI, the fe ar is that this would lead to a frantic
development race that leads to underinvestment  of resources
(researcher time or money) into system safety and reliability.
2021 ] Artificial Intelligence & Antitrust  457
In order to address these harmful race dynamics, OpenAI
proposed in April 2018 the “Assist Clause ” in its Charter. This
clause states that:
We are concerned  about late -stage AGI development
becoming a competitive race without time for
adequate safety precautions. Therefore, if a value -
aligned, safety -conscious project comes close to
building AGI before we do, we commit to stop
competing with and start assisti ng this project. We
will work out specifics in case -by-case agreements,
but a typical triggering condition might be “a better -
than-even chance of success in the next two years.91

The following sections analyze  the compatibility of the
Assist Clause with A rticle 101(1) in two steps. First, we consider
whether the Assist Clause itself could be a restrictive agreement in
breach of Article 101(1). Next, we look at whether different forms
of implementing the Assist Clause could constitute an anti -
competitive ag reement or form of cooperation that also breaches
Article 101(1).
This two -step analysis is necessary because the Assist
Clause could breach Article 101(1) even if partially or never
implemented.92 A “concurrence of wills ” alone is enough to trigger
a brea ch of Article 101(1). If the Assist Clause does not breach
Article 101(1), its subsequent implementation (for example , through

91 OpenAI Charter, supra  note 22 .
92 Case T -558/08, Eni v Commission, supra  note 49, ¶ ¶ 132 -133 (General Court
held that it was sufficient to show a concurrence of wills on price -fixing; it was
not necessary to show that price increases were, o r could be, implemented).
458 Yale Journal of Law & Technology  [Vol. 23
an R&D cooperation agreement ) could still constitute a separate
breach. Alternatively, both the Assist Clause and its subsequent
implement ation  may breach Article 101(1). The EC could treat t hese
two forms of conduct as a “single and continuous infringement ”,
which has implications for the duration of the infringement and
therefore the level of fine, as well as  for the parties that could be
held liable for the infringement.93
Preliminary Question : Could the Assist Clause itself be an
agreement between  undertakings?
OpenAI ’s Assist Clause is currently a public, unilateral
statement in its C harter , published on their website. If OpenAI ’s
decision to be bound by the Clause  is truly unilateral in nature, it
will fall outside of Article 101(1) , which applies only to agreements
between undertakings.94 However, as mentioned above, the concept
of an anti -competitive agreement under Article 101(1) can be very
wide. For example, if OpenAI ’s competitors publicly announce  their
own Assist Clauses, EU competition  law could deem the respon ses
of competitors to each other's public announcements a strategy for
reaching a common understanding on future commercial behavior .95

93 See infra  Section III.A  (Assist Clause as a ‘single and continuous
infringement”) .
94 Communication from the Commission - Guidelines on the applicability of
Article 101 of the Treaty on the Functioning of the European Union to horizontal
co-operation agreements, 2011 O.J. (C11) 1 (“ Horizontal Cooperation
Guidelines”), ¶ 63.
95 Id..
2021 ] Artificial Intelligence & Antitrust  459
The EC has held that this type of ostensibly unilateral, public
announcement can constitute an agreement under Article 101(1)
where it reduces the level of uncertainty about a competitor ’s future
commercial  behavior .96 Risks could arise, therefore, if regulators see
the Assist Clause as an invitation to other AI companies to
coordinate their behavior . OpenAI (and any other companies that
adhere to the Assist Clause) (“Assist Clause participants”) should
take care not to contact competitors to persuade them  to follow the
Assist Clause, or otherwise seek to influence or reach an
understanding with competitors to that effect.97
Could an agreement that implement s the Assist Clause
breach Article 101(1)?
In the previous section, we looked at whether the Assist
Clause itself could be an agreement that falls within Article 101(1).
However, even if competition law does not find the Assist Clause
itself to be an agreement under  Article 101(1), this section wil l

96 In the 2016 Liner Shipping  investigation, for example, the Commission reached
the preliminary view that announcing fut ure price increases breached Article
101(1). This is despite the fact that there was no direct agreement or even covert
contact between the parties. The public anno uncements may signal the intended
market conduct of carriers and decrease their incentives t o compete against each
other ( Case COMP/39850, Container Shipping, Commission Decision of 31
August 2016 ).
97 At the same time, Article 101 does not “deprive compani es of the right to adapt
themselves intelligently to the existing or anticipated conduct of their
competitors” ( Horizontal Cooperation Guidelines, supra  note 94, ¶ 61) .
Therefore, it does not prohibit an AI company adapting its behavior as a reaction
to / influenced by the OpenAI clause, as long as it is a genuinely unilateral
decisi on.
460 Yale Journal of Law & Technology  [Vol. 23
consider the question of whether the implementation of the Assist
Clause could constitute a separate breach of Article 101(1).
For present purposes, we will not consider in detail the
alternative scenario where both the Assist Clause itself and its
imple mentation breach Article 101(1) and form a “single and
continuous infringement .”
There are several ways that the Assist Clause could be
implemented by a competitor to assist the leading TAI developer
(“Leader”). Each of them could also constitute, in addit ion to the
Assist Clause, a restrictive agreement between competitors in
breach of Article 101(1).
The methods of implementation largely fall into two
categories: first, the competitor assists the Leader with the latter ’s
TAI development, including through a JV or by merging with the
Leader (“collaborative implementation”) ; or second,  the competitor
slows down TAI development ( for instance,  by firing employees) or
switche s resources from TAI development to another area such as
safety research or  applied research. In the second category,  there is
no direct cooperation or merger with the Leader ; instead ,
implementation is unilateral  (“non -collaborative implementation”).
Comp etition law could classify c ollaborative
implementation scenarios as a horizontal co operation agreement on
R&D . In addition, depending on the way the parties cooperate, EU
2021 ] Artificial Intelligence & Antitrust  461
merger approval may also be required, though this falls outside the
scope of this pa per. Non -collaborative implementation scenarios
will fall outside Article 101(1) entirely if they are truly unilateral
based on the same principles as discussed in the previous section.98
We assess both categories of implementation under Article 101(1)
below.
Category 1: Collaborative implementation of Assist Clause :
R&D Cooperation
The following analysis will first consider whether
collaborative implementation of the Assist clause through R&D
cooperation falls within the Article 101(1) prohibition, and if  so,
whether it can benefit from the R&D block exemption that sets out
conditions under which forms of R&D cooperation can be exempt
from Article 101(1). Failing that, we will consider whether the R&D
cooperation is likely to breach Article 101(1) under th e framework
of the Horizontal Guidelines, and if so whether the cooperation can
benefit from the individual exemption under Article 101(3).
Defining the market is an important first step in the analysis
of R&D agreements because it allows us to assess whet her
collaborative implementation of the Assist clause through R&D
cooperation falls within the Article 101(1) prohibition .99

98 The relevant questions are whether with the existence of the Assist Clause in
the OpenAI Charter, and possibly others in the industry at the relevant point in
time, it can  truly be said that the competitor’s acts are unilateral.
99 The Commission Regu lation (EU) No 1217/2010 of 14 December 2010 on the
application of Article 101(3) of the Treaty on the Functioning of the European

462 Yale Journal of Law & Technology  [Vol. 23
The market definition will depend on whether we see TAI as
creating  an entirely new technology or as improving existing
products or  technologies, which may in turn depend on how gradual
the development trajectory is. It seems likely that the R&D
cooperation will lie somewhere between the two scenarios of
developing an entirely new technology or improving existing
technologi es. We should thus assess TAI development on both
frameworks  of analysis described below:  as an innovation market
and as an existing technology market.100
A more discontinuous development trajectory will more
likely lead to “the development of new products or technology
which either may —if emerging —one day replace existing ones or
which are being developed for a new intended use and will therefore
not replace existing products but create a completely new
demand .”101 Under this scenario, we should assess the R &D
coordination according to its effects on competition in innovation
for TAI, rather than on existing market shares .102
It is also possible that the development of TAI is
incremental , such that we can see it  as a significant improvement to

Union to certain categories of research and de velopment agreements, 2010 O.J.
(L 335) 36 (“R&D Block Exemption”) defines cate gories of R&D agreements that
the Commission views as normally satisfying the Article 101(3) exemption ( see
R&D Block Exemption, ¶ 2).
100 This “blended ” approach is discussed  in the Horizontal Cooperation
Guidelines, supra  note 94, ¶ ¶ 112 & 139.
101 Id. at ¶ 119.
102 As discussed further in the section below ‘Analysis of R&D Cooperation under
Article 101(1)’.
2021 ] Artificial Intelligence & Antitrust  463
an existing tec hnology, rather than a technology for an entirely new
intended use and creating  completely new demand. In this scenario,
in the competition analysis we should account for the competitor ’s
and leader ’s market shares on the existing technolog y market that
TAI is improving  or eventually replacing .103
There is still considerable uncertainty as to which of the
above two scenarios TAI development will fall under.104 The
following analysis is also relevant to the possibility that it may l ie
somewher e between the two ; that is,  where innovation efforts may
create technology that, over time, replaces existing ones. The
following analysis will therefore assess competitive conditions on
both relevant markets.105
Having defined the relevant market, th e nex t step is to  assess
whether the R&D cooperation could restrict competition under
Article 101(1) or  if it falls outside Article 101(1) altogether. Without
carrying out a full assessment, we can preliminarily assess that there
is a risk that R&D cooperation is prohibited under Article 101(1).
The Leader is likely to have a strong market position in existing and
related markets to TAI. In addition, the company intends the R&D
cooperation to reduc e or slow down innovation.106 Having

103 Id. at ¶ ¶ 116-118.
104 See, e.g, Gruetzemacher & Whittlestone, supra  note 2 1.
105 Horizontal Cooperation Guidelines, supra  note 94, ¶ 112.
106 The Horizontal Cooper ation Guidelines explain that R&D cooperation
agreements could lead to possible anti -competitive effects , such as the reduction
or slowing down of innovation, such that fewer or worse products come to the
market or do so later than the y otherwise would.
464 Yale Journal of Law & Technology  [Vol. 23
established that there is a risk of breaching Article 101(1) that
warrants continuing the assessment under Article 101(1),  we will
next consider whether the R&D cooperation can benefit from the
“safe harbor ” under the R&D Block Exemption.107
Is the R&D cooperation exempt under the R&D Block
Exemption?
To benefit from the R&D Block  Exemption, the R&D
cooperation must: (1) be a qualifying R&D cooperation agreement
as defined in the R&D Block Exemptio n; (2) satisfy the market share
thresholds on the existing technology market and conditions on the
innovation market; and (3) not contain any blacklisted “hardcore
restrictions .”
The R&D Block Exemption definition of an R&D
cooperation agreement includes any agreement between parties
relating to the conditions under which they pursue joint R&D of
contract108 products and technologies.109 For R&D to be “joint,” it
can be: (1) carried out by a joint team or undertaking; (2) jointly
entrusted to a third party ; or (3) allocated between the parties in any
way they consider most appropriate.110 If the company implements

107 R&D Block Exemption, supra  note 99, Article 2(1). Note that the current
version of the R&D Block Exemption is in place until December 31st, 2022. The
EC is currently consulting on the effectiveness, efficiency, relevance, coherence
and EU added value of t he R&D Block Exemption to determine  whether to allow
it to lapse, prolong its duration, or revise it and the Horizontal Cooperation
Guidelines to take proper account of new market developments since 2010.
108 “Contract ” products and technologies refer to tho se arising out of the joint
R&D or manufactured applying the contract technologies . Id. at Article 1(1)(e).
109 Id. at Article 1(1)(a).
110 Id. at Article 1(1)(m) & (1(1)(n).
2021 ] Artificial Intelligence & Antitrust  465
the Assist Clause in any of these “joint” ways, it will fall within the
scope of the R&D Block Exemption.111
Where the R&D cooperation develop s a product which
creates a completely new demand, the R&D Block Exemption treats
those agreements as benefiting f rom the Article 101(3) exemption
for the duration of R&D and for seven further years after the product
first reaches the market.112 The agreement can benefit from the
exemption if the following  conditions are satisfied . The R&D
cooperation must not:113 (1) restrict the freedom of the parties to
carry out R&D in an unrelated field , such as  outside TAI
development114; (2) limit output or sales115; or (3) “ eliminate[]
effective competition in innovation .”116 If any of these apply, the
R&D cooperation will not benefit from the R&D Block Exemption.
Where TAI development is more incremental, existing
technology markets could be significantly improved  or replaced by

111 Note that the definition of R&D agreement under the R&D Block Exemption
and the Horizontal Cooperation Guidelines are different. The definition under the
R&D is narrower.
112 Horizontal Cooperation Guidelines, supra  note 94, ¶ 126; R&D Block
Exemption, supra note 99, Article 4(1).
113 For full list of conditions, see Article 3 (conditions for exemption), Article 5
(hardcore restrictions (any R&D agreement that contains a hardcore restriction
will not be able to rely on the R&D Block Exemption at all) and Artic le 6
(excluded restriction s, i.e, restrictions within an R&D agreement that cannot be
exempt under the R&D Block Exemption, but the rest of the agreement may still
be exempt under the R&D Block Exemption).
114 R&D Block Exemption, supra  note 99, Article 5(a ).
115 Id. at Article 5(b) .
116 This “elimination of competition ” scenario may occur if R&D cooperation
applies across large swathes of the TAI development market i.e. all companies
that are credibly developing, or could credibly develop, TAI are cooperating w ith
the Leader rather th an competing with each other . See Id. at Articles 19 -21;
Horizontal Cooperation Guidelines, supra  note 94, ¶ 126.
466 Yale Journal of Law & Technology  [Vol. 23
TAI rather than the latter creating a completel y new market
altogether. In this scenario, the parties ’ position s on the existing
technology market are relevant to the analysis. This is because we
can deem that competitors on the existing technology market
competitively constrain the TAI market to some degree.
To benefit from the R&D Block Exemption, the parties ’
combined market shares of licensing fees from those existing
technologies , and the share of downstream sales of products or
services incorporating the licensed technologies, cannot exceed 25%
on either  of these markets.117 Given the likelihood that the Leader
will also have a strong market position in TAI ’s predecessor
technologies alone, the parties might exceed this market share cap
and therefore not benefit from the  R&D Block Exemption.
In conc lusion, the parties will benefit from the R&D Block
Exemption if: (1) their combined market share is below the 25%
market share threshold; (2) they satisfy the conditions outlined
above with respect to assessment under the innovation marke t
(including  no limitation of output or sales and  no elimination of
competition); and (3) the R&D agreement does not contain any other
“hardcore ” restrictions on competition.118 Outside of the R&D

117 Horizontal Cooperation Guidelines, supra  note 94, ¶ 125.
118 E.g., restriction on the parties’ ability to carry out R&D in dependently or with
third parties in unconnected fields, the limitation of output or sales (subject to
some exceptions), the fixing of prices when selling the product to third parties
(subject to some  exceptions). For the full list of hardcore restrictions , see Article
5 of the R&D Block Exemption , supra  note 83 . Note that if an agreement contains

2021 ] Artificial Intelligence & Antitrust  467
Block Exemption, competition law would assess the R&D
cooperation under the u sual Article 101(1) principles, to which we
now turn.119
Analysis of R&D Cooperation under Article 101(1)
The type of cooperation envisioned under the Assist Clause
is likely to qualify as an R&D agreement within the scope of the
Horizontal Cooperation Guidelines because: (1) it is an agreement
between competitors (2) to jointly improve existing technologies or
carry out R&D on completely new p roducts and (3) the form of
collaboration could be in any form, such as  via a cooperation
agreement or a jointly controlled company.120
The Horizontal Cooperation Guidelines explain that possible
anti-competitive effects of R&D cooperation include the reduc tion
or slowing down of innovation, such that fewer or worse products
come to the market or do so later than they otherwise would.121
These restrictive effects on competition are only likely where the
competitor and/or the Leader have market power on the ex isting
markets , and/or the R&D cooperation appreciably reduces
competition in innovation.122

hardcore restrictions , it will be entirely excluded from the benefit of the block
exemption.
119 Horizontal Cooperation Guidelines, supra  note 94, ¶ 135.
120 Id. ¶ 111.
121 Id.¶ 127.
122 Id. ¶ 127.
468 Yale Journal of Law & Technology  [Vol. 23
If TAI development produces an  entirely new technology
that creates its own market, the legal analysis would focus on the
potential for R&D cooperation to restrict  innovation.123 Under this
analysis, R&D cooperation would likely be problematic where: (1)
the competitor and Leader could both have independently developed
TAI on their own; (2) they cooperated at a relativel y late stage of
development, such that  they wer e “rather near ” the launch of the
product, and (3) the remaining firms racing to develop TAI on the
market are not viable competitors.124
On the other hand, competition concerns are less likely to
arise if the coordinating parties did not have the means to carry out
the necessary R&D independently but are able to do so jointly
because they are bringing together these complementary resources.
There is no loss of competition as a result of the cooperation b ecause
the coordinating parties would not have been able to effectively
compete with each other whilst acting independently.125
It follows that, to mitigate competition law risks, it would be
advisable that: (1) cooperation occurs at as early a stage as po ssible
and (2) cooperation with the Leader is widespread enough across the
TAI development industry to dampen harmful race dynamics, but

123 Id. ¶ 138.
124 Id. ¶ ¶ 119 -122.
125 Id. at ¶ 130.
2021 ] Artificial Intelligence & Antitrust  469
still leaves sufficient competition on the market to satisfy
competition law.
R&D cooperation would be problematic under Article
101(1) if the parties have a strong position in the existing technology
market and face little competitive constraint there. This would be
even more of a concern if the parties also hold a strong position in
the innovation market. Several factors are relevant in assess ing
whether R&D cooperation restricts competition under Article
101(1) on an existing technology market. First, the market positions
of the parties are relevant. The R&D cooperation may breach  Article
101(1) if it involves significant competitors on an existing
technology market who cooperate to develop a new technology that
may one day replace the existing (but related) technology. A
combined market share of above roughly 40 -50% may start to
indicate competition concerns126 although this will vary depending
on various other factors.
Second, there should be an assessment of the competition on
the market. The R&D cooperation may be problematic if the
competitors are each other ’s closest competito r in the market, and
the remaining competitors are not sufficiently viable to constitute a
competitive constraint on the cooperating parties.127

126 See, e.g., Case IV/D -2/34.780, Virgin/British Airways, Commission decision
of 14 July 1999, 2000 O.J. (L/30) 1; dominance at 39.7% of the market, upheld
on appeal Case T -219/99, British Airways v Commission, 2003 E.C.R. II -5917.
127 Horizontal Cooperation Guidelines, supra  note 94, ¶ ¶ 137 -139.
470 Yale Journal of Law & Technology  [Vol. 23
Third,  we should assess  entry barriers and potential market
entry  to look at potential competition . Even if ther e is no viable
existing  competition, the threat of new entry into the existing
technology market could competitively constrain the cooperating
parties. For entry to be sufficiently constraining on the parties ’
behavior , the parties must  show it  to be likely  and timely to pose a
viable competitive threat to the parties.128
Whether t he possibility and likelihood of new entry can
constrain the parties will depend on factors such as: (1) whether the
cost of entry includes sunk cost s or the need to o vercome a dif ficult
regulatory framework, like  burdensome certification; (2) minimum
efficient scale within the industry —if this is large, entry is more
costly and risky; (3) whether there has been past entry on a
significant scale or not; and (4) whether potential entrants have a
realistic chance of competin g effectively with the incumbent, (as an
illustration, whether they  have access to at least as cost efficient
technologies ).129 If we extrapolate about the nature of TAI
development based on the developm ent of the most advanced AI
today, it is probably not likely to be a market where potential

128 Id., ¶ 45 -47.
129 Article 101(3) Guidelines , supra  note 56,  ¶ 115.
2021 ] Artificial Intelligence & Antitrust  471
competition is realistic or likely . This is  because entry barriers and
incumbent advantage are likely large.130
To conclude our assessment under Article 101(1), in t he
innovation market, the R&D cooperation may breach Article 101(1)
where the competitor and Leader are cooperating at a late stage of
development where the competitor is relatively close to developing
TAI independently, and other competitors are no longer  viably “in
the race .” In an existing technology market, issues may also arise
where the competitor and Leader hold a strong position and face
little other credible competition or  potential competiti on. This seems
to be a possible or even likely scenario g iven the potential high entry
barriers and large incumbent advantage in a TAI development
market. If the R&D cooperation restricts competition in the ways
outlined above, it may still be exempt if it satisfies the conditions
under Article 101(3).
Exceptio n under Article 101(3)
The Article 101(1) prohibition will not apply to the R&D
cooperation if the four cumulative conditions under Article 101(3)
can be satisfied. The burden of proof rest s on the parties seeking to
rely on the exemption.  We consider e ach condition in turn below.

130 The only exception may be if there are la rge companies in adjacent markets
that have the required resources and related technological acumen that they can
leverage into the TAI development market. Even if such a company is technically
capable of ent ering however, it is unclear whether it would be  commercially
attractive for it  to do so, given the probable strength/dominance of the cooperating
parties in the TAI development market.
472 Yale Journal of Law & Technology  [Vol. 23
The Commission recognize s that many R&D agreements
can bring about efficiency gains by leading to  “improved or new
products and technologies being developed and mar keted more
rapidly than otherwise ,” or bringing about cost reductions.131 The
efficiency gain in this instance , compared to a counterfactual
without the R&D cooperation , would be the development of an
improved , more rigorously tested version of TAI that is more ethical
or safer,  to the benefit of consumers.132 The R&D agreement could
also achieve a reduction in cost, where this cost relat es to the cost of
consumer harm or  litigation as a result of the harmful race dynamic.
The challenge, though, is that compe tition law requires the
efficiency gains to be clearly substantiated and shown to flow
directly from the R&D cooperation. This is a relatively high
evidential standard and includes verifying the direct causal  link
between the agreement and the efficiencies .
The potential difficulty in relation to the Assist Clause  is that
the causal link between the R&D cooperation and the resulting
efficiencies is more indirect. In other words, can we actually show
and quantify that cooperation will lead to better safety o utcomes?
This may be so if we are co mbining complementary resources,

131 Horizontal Cooperation Guidelines, supra  note 94, ¶ 141
132 The following analogous example is given in id., ¶ 149 . Two engineering
companies pool their R&D efforts to improve the production of an
environmentally friendly car component that means that vehicles would consume
less fuel and therefore emit less CO2. In this scenario, the Commission recognize s
that the res triction of competition is likely to be outweighed by benefits from
consumers from a lower consumption of fuel, thereby satisfying Article 101(3).
2021 ] Artificial Intelligence & Antitrust  473
skills , or expertise, but may be harder to argue that better safety
outcomes would arise because of a reduction in “harmful race
dynamics .” This relies on a conjecture that race dynamics are
genuinely harmful, whereas competition law generally views “race
dynamics ” as competition that typically leads to better outcomes for
consumers, in the form also of better quality, more innovative (and
in this case, safer) products.133 We would therefore need to
demonstrate with evidence  that race dynamics will create worse AI
safety outcomes. One alternative is to base an Article 101(3)
argument  on more tradit ional economic efficiencies, such as  cost-
efficiencies that  the R&D cooperation implementing the Assist
Clause  would incidentally achieve . Cost efficiencies could also arise
from developing most cost -efficient production technologies and
methods, economies of scale, or combining two existing
technologi es that have complementary strengths that may reduce
production cost or lead to production of a higher quality product.134
Any restrictions on competition from the R&D cooperation
should not go beyond what is necessary to achieve the efficiency
gains. In ot her words, if there were  a realistic alternative135 that

133 Note that cooperation to achieve a desirable goal, such as environmental
protection, can later stra y into a n anti -competitive cartel ( see e.g. Case
COMP/39579 Consumer Detergents, Commission decision of 13 April 2011,
2011 O.J. (C 193) 14 ). This is always a risk when companies work together, even
if with good intentions at the outset.
134 Article 101(3) Guidelin es, supra  note 56, ¶ ¶ 64 - 68.
135 There must be no other economically practicable and less restrictive means of
achieving the efficiencies  (id., ¶ 75 ).
474 Yale Journal of Law & Technology  [Vol. 23
would achieve the same gains in safety without reducing the
competition on the TAI development mark et, for instance  stepping
up monitoring or auditing efforts rather than going as far as the R&D
coope ration agreement, then the Assist Clause would not satisfy
101(3). The parties should explain, for example, why they could not
have achieved the same efficiencies by acting alone.136
Efficiency gains must also be passed onto customers and
must outweigh the anti-competitive effects – that is to say,  the
improved safety must be demonstrated to outweigh any negative
consumer effects , like higher prices or lower output, as a result of
the loss of competitive dynamics.137 A further point to keep in mind
is that to  satisfy the “fair share ” threshold, what counts is the
“overall impact on consumers of the products within the relevant
market .”138 This means that the net effect of a restrictive agreement
on the consumers who are subject to the restriction , here users of the
eventual AI technology ,139 must be positive. Whether efficiencies
will be passed on to consumers will depend on whether there i s
enough residual competition on the market and  is closely linked to
condition 4 discussed below.140

136 Id., ¶ 76
137 Id., ¶ 85.
138 Id., ¶ 87.
139 Furthermore, the term ‘consumers’ includes all users of t he goods or services
concerned, whether undertakings or private individuals, and at whatever stage of
the supply chain ( id., ¶ 84).
140 Article 101(3) Guidelines, supra  note 56, ¶ ¶ 95 -97.
2021 ] Artificial Intelligence & Antitrust  475
Finally, the R&D agreement cannot effectively eliminate
competition in a s ubstantial part of the products or technologies in
question. We should carry out this assessment on both the existing
market and the innovation market, and in a similar way to the
competitive assessment under Article 101(1).141 The difference is
the strictness of the test. Under Article 101(1), the assessment  tests
whether the agreement  appreciably reduces competition compared
to the counterfactual. Under this Article 101(3) condition, the test is
whether the agreement eliminates, not just reduces, effective
competition. The central question, as with the analysis above, is
whether there will remain enough other companies competing to
develop TAI such that this condition is satisfied. In a way, this leads
to a direct trade -off or catch -22 scenario.  The greater the residual
competition, the  greater the likelihood that the  R&D agreement  will
be legally compliant, but the less effective it will be in pursuing the
AI governance objective to reduce harmful race dynamics.
On the other hand, if the R&D agreement  eliminates all
competition for TAI, it will be successful in satisfying its objective
from an AI governance perspective but might fall foul of
competition law. The better way to think about this conundrum is to
seek to significantly dampen race dynamics b ut leave residual
competition such that  the cooperation strategy can rely on  101(3).

141 See supra  Section III.A  (Analysis of R&D Cooperation under Article 101(1) .
476 Yale Journal of Law & Technology  [Vol. 23
In any case, t his might be the outcome in practice as it is likely that
enough companies will not comply with the Assist Clause and
continue to compete , but they will need to be viable competitors to
the cooperating AI companies.
Category 2: Non -collaborative implementation of Assist
Clause
It may be possible to implement the  Assist Clause without
any collaboration between the competitor and Leader. This  could
include shifting the R&D from TAI development to another area
such as safety or applied research, slowing down TAI development,
or disbanding R&D researchers. If these actions are truly unilateral,
the implementation will fall outside of Article 101 (1). However,
even if actions are not prima facie or designed to be collaborative,
they could still breach Article 101(1) if they involv e any agreement,
understanding or exchange of information at the point of
implementation , including  any cooperation with  the Leader with
respect to the implementation or any exchange of informati on with
the Leader about timing or method . It is therefore important to think
critically about whether actions that seem or purport to be unilateral
really are in practice.
Assist C lause as a “single and continuous
infringement ”
If the Assist Clause itself and any subsequent
implementation both breach Article 101(1), the EC could treat those
breaches as part of a “single and continuous infringement ” (SCI) .
2021 ] Artificial Intelligence & Antitrust  477
Two or more agreements or concerted practices may be
characterised as a single and continuous infringement of Article
101(1) where three cumulative conditi ons are met: (1) the
agreements or concerted practices share an overall plan pursuing a
common objective; (2) each undertaking i ntends to contribute by its
own conduct to the common objective pursued by all participants;
and (3) either each undertaking is aware of the offending conduct of
the other participants in pursuit of the same objective, or they could
have reasonably foresee n that offending conduct would occur and
was prepared to take the risk that it would. 142 The EC has confirmed
that the fact that the number and identity of participants in the cartel
changes over time does not preclude the existence of an SCI.143 Each
infringing participant may be a party to an SCI for the period during
which the regulator can prove  that it participated in the
infringement.144
Actions making up an SCI must form part of an overall plan
pursuing a common objective. Relevant criteria f or assessing

142 Joined Cases T -204/08 and T -212/08 Team Relocations and Others
v Commission , 2011 E.C.R. II -3569 , ¶ 37. The General Court considered the
caselaw and concluded that “ three conditions m ust be met in order to establish
participation in a single and continuous infringement: namely the existence of an
overall plan pursuing a common obje ctive, the intentional contribution of the
undertaking to that plan, and its awareness (proved or presumed ) of the offending
conduct of the other participants .”
143 Case T -377/06 Comap  v Commission (‘Fittings’) [2011] ECR II_1115,
EU:T:2011:108, ¶ ¶ 85 -86.
144 Concept was first applied in Case IV/31.149, Polypropylene, Commission
Decision of 23 April 1986,  1986 O.J. (L 230) 1, ¶ 81 et seq.  and in many cases
subsequently.
478 Yale Journal of Law & Technology  [Vol. 23
whether there is an SCI pursuing an overall plan include: (1)
identical nature of the products concerned ; (2) identical nature of the
objectives of the practice at issue ; (3) whether the same undertakings
participated in the practices at issue  (though this is not necessary to
find SCI) ; (4) the identical nature of the rules for implementing those
practices ; and  (5) whether the geographic scope of the practices at
issue is the same. Note , however , that this checklist is not fixed or
exhaustive, nor is every element necessarily mandatory in every
case.
Several  consequences flow from a finding of SCI. First, an
undertaking that has participated in a “single and complex
infringement ” as a whole can be liable for the conduct of other
undertakings th at contributes to the same infringement throughout
the period of its participation. Second, the SCI may also allow the
EC to penali ze conduct that would otherwise have been time -barred .
There is a five -year limit for the imposition of a penalty that runs
from the end of a period of the SCI. Third, an SCI may only attract
one fine, but the level of  the fine may be higher to reflect the wider
scope or longer duration of a SCI.
In some instances, an SCI finding may turn out to be
beneficial for an undertakin g because it is liable for a single fine
rather than one fine per infringement.145 In practice, however, the

145 See Bellamy & Child, European Union Law Of Competition 135 (2018).
2021 ] Artificial Intelligence & Antitrust  479
finding of an SCI is generally detrimental because it is more likely
to result in  the imposition of a significantly higher fine and wider
exposure to follow -on private damage claims.146
One way of mitigating the risk of breaching competition law
with an  SCI is to leave each undertaking free to implement the
practices in any way they wish, and to avoid coordinating ,
discussing , or disclosing the rules for such implementation. This
may be more feasible than trying to  avoid coordinating on the plan
or objective , as the latter  may be important for trust -building and
making AI companies aware of others who have entered into the
same obligation.
Conclusion & Mitigating Steps
The Assist Clause may breach Article 101(1) in two ways.
First, if there is some form of understanding between AI companies
that they should each enter into an Assist Clause  agreement , that
could constitute an anti -competitive agreement in breach of Article
101(1) because it seeks to reduce competition between competing
AI companies, regardless of if or when one of the companies
implements it . Sec ond, any subsequent cooperation or agreement
between competitors to implement the A ssist Clause ( such as  an
agreement to cooperate on R&D) could also breach Article 101(1).
The implementation could form a standalone breach. Alternatively,

146 Id.
480 Yale Journal of Law & Technology  [Vol. 23
both entering an Assist Clause agreement  and its subsequent
implementation could form part of one si ngle and continuous
infringement.
The potential anti -competitive effects of implementing the
Assist Clause will be particularly problematic where the par ties are
close competitors  and the rest of their competitors are relatively
weak. Such a scenario would increase the risk that an R&D
cooperation agreement implementing the Assist Clause could
breach Article 101(1). Furthermore, the R&D Block Exemption will
be challenging to apply if the Leader exceeds t he market share
threshold, for example  because of a strong position in existing and
related technologies to TAI. It is possible that R&D cooperation
could be exempt under Article 101(3), but it may be challenging to
verify that the c ooperation will result in efficiencies due to increased
safety from reduced race dynamics.
There are a few ways t o mitigate the risks. First, companies
would not fall  within the scope of  Article 101(1) in the first place if
they acted  unilaterally in both committing to the Assist Clause and
implementing it. Each AI company should unilaterally decide to be
bound by an Assist Clause. For the same reason, companies should ,
if possible , opt for non -collaborative actions that are trul y unilateral
to implement it , including  slowing down R&D, shifting R&D to
other areas , or putting employees on leave . In addition, companies
2021 ] Artificial Intelligence & Antitrust  481
should not contact competitors  to persuade them to enter into or
implement the Assist Clause, or otherwise seek to influence or reach
an unders tanding with competitors to that effect.
Second, if unilateral action is not possible, companies should
seek to implement the Assist Clause in a way that does not restrict
competition. This contradicts the Assist Clause ’s main objective, but
all other thin gs being equal, cooperation should be widespread
enough across the TAI development industry to dampen harmful
race dynamics, but still leave some sufficient competition to satisfy
competition law. It is also better to cooperate at earlier stages of TAI
development. The closer the competitor is to being able to develop
TAI independently, the greater the loss of competition.
Third, amongst the different ways t o implement  the Assist
Clause, companies can significantly mitigate  the risks if cooperation
between a competitor and the leading TAI developer do es not lead
to the loss of the competitor ’s AI development activities. In other
words, instead of shutting down the competitor ’s AI development or
getting rid of its staff, it is preferable for the development a ctivities
to shift to another, related  area of AI development, like  AI safety.
Finally, it is best if the R&D cooperation combines
complementary research talent and resources and can achieve
economies of scale and scope. Both can generate cost savings or
productive efficiencies. These efficiencies are easier to prove and
482 Yale Journal of Law & Technology  [Vol. 23
quantify compared to safety gains and will help to establish a case
for exemption under Article 101(3).
B. The Windfall Clause
Key Recommendations
The Windfall Clause does not immediately raise competition
law concerns because it s proponents do  not envisage  it to be an
agreement between competitors. However, concerns may arise
where AI companies want assurance that others will similarly
volunteer to be bound by the Windfall Clause in order to ensure a
level playing field. Such an “agreement to agree ” could infringe
competition law to the extent that it disincentivizes companies from
generating more profit or expanding sales to avoid triggering the
clause. As with the Assist Clause, a company can breach
competition law as soon as it enters into the “agreem ent to agree ,”
regardless of if or when the Windfall Clause is triggered.
Overall, there seems to be a low risk that the “agreement to
agree ” will inf ringe Article 101(1) on a “by effects ” analysis
because of its potential output -disincentivi zing effect. To further
mitigate this risk, AI companies should avoid entering into an
“agreement to agree ” to the Windfall Clause, if possible. This helps
to place  the conduct outside the scope of Article 101(1) altogether.
AI companies could also amend their company object ives, as set out
in the corporate articles of association, to the effect that they will
2021 ] Artificial Intelligence & Antitrust  483
comply in good faith with the Windfall Clause and not see k to
circumvent it.
Analysis
The Windfall Clause is one proposal by AI governance
researchers to tackle the inequality that may result from
development of TAI and its potential to concentrate wealth in the
hands of those firms that develop it. The Windfall Clause refers to
an agreemen t entered into by an AI company to redistribute a part
of its wealth if and when its earning s exceed a certain threshold such
as $1 trill ion in profit, or earnings exceeding  1% of the world ’s total
economic output.147
The first question is whether the Wind fall Clause is a
restrictive agreement under Article 101(1). One can structure t he
Windfall Clause in a way that does not constitute an agreement
under Article 101(1). For example, it may be in a private contractual
agreement between the AI company and a t rust that must collect and
distribute the proceeds of the Windfall Clause on behalf of
humanity. The obligation to pay would then be between the trust and
the AI company. That agreement would not be a qualifying
agreement under Article 101(1) so long as th e trust is not an

147 Cullen o’Keef e et al, The Windfall Clause: Distributing the Benefits of AI ,
CENTRE FOR THE GOVERNANCE OF AI RESEARCH REPORT  (2020),
https://www.fhi.ox.ac.uk/wp -content/uploads/Windfall -Clause -Report.pdf
(“Windfall Clause Report”).
484 Yale Journal of Law & Technology  [Vol. 23
“undertaking ,” that is  an entity that is active in offering goods or
services on a given market.148
However, firms may only wish to agree to the Windfall
Clause if their competitors also do  so, in order to ensure a “level
playing field.”149 In this scenario, competing AI firms may wish to
enter into a side agreement , with  each respectively committing to
enter into the Windfall Clause to ensure that no one firm is
commercially disadvantaged. This “agreement to agree ” to the
Windfall Clause could be prohibited under  Article 101(1) even if it
is not a formal written agreement, but rather some form of informal
understanding. However, even if there is an “agreement to agree ,”
the question is whether the agreement is restrictive of competi tion.
The Clause does not seem to be restrictive of competition per se —
the AI companies are just agreeing to give away some of their
revenue. However, competition law risks arise where the
“agreement to agree ” results in reduced competition between
compani es for fear of triggering the Windfall Clause.150

148 See supra  Section II.A (Article 101(1) Prohibition) .
149 This possibility is considered in the Windfall Clause Report, supra  note 147,
at 25.
150 Other possible concerns may arise from depending on how the Windfall Clause
is structured. For example, there could be issues arising from cross -shareholdings
if (1) the Windfall Clause is structured as a superjunior stock grant, and (2) a
common fund hol ds that stock. This could give rise to problems under Article
101(1) if the common fund holds that ‘Windfall Clause’ stock on behalf of
multiple AI companies, and that cross -shareholding gives the common fund the
ability to coordinate the conduct of those AI companies e.g. because its shares
give it a board vote for example in each company. However, this is easily
addressed by giving the fund only very clearly defined powers e.g. to distribute
the Windfall Clause funds under specific triggering conditions. Or, separate

2021 ] Artificial Intelligence & Antitrust  485
To some extent, any output -disincentivi zing effect will
depend on how the Windfall Clause is structured . If a company uses
a tiered approach such that  only the amount in excess of the $1
trillion is subject  to a percentage  levy, and that itself is on a stepped
basis, then the disincentivizing effect is less clear than taking a
percentage  levy of all revenue. It seems that proponents of the
Windfall Clause currently prefer a tiered system,151 which also
reduce s risks from a competition law perspective.
However,  even if there is a tiered approach, there may still
be disincentives to triggering the Windfall Clause at all. The
company may wish to avoid attention to the profitability of the firm,
leading to potential additional political and regulatory scrutiny,
including under competition law.152 Another disincentive may come
from the signal that a triggering of the Windfall Clause may send to
investors. It may alert  them that the company must redistribute
portion of future earnings and may therefore encourage a flight of
investment to competitors that are not bound by the Windfall
Clause.

recipient funds can be used for each AI company that is signatory to the Windfall
Clause. We do not propose to discuss thi s potential concern further in this paper.
151 Windfall Clause Report, supra  note 147, at 10.
152 For further exploration of these issues, see id., at 25 -26; Belfield, supra  note
16.
486 Yale Journal of Law & Technology  [Vol. 23
Infringement of Article 101(1)
For the reasons discussed above, the Windfall C lause could
lead to companies restricting sales or output to avoid triggering the
Windfall Clause. In this way, the agreement could constitute an
indirect restriction on output in breach of Article 101(1).153 The
Windfall Clause is unlikely to be an infring ement “by object .” It is
unlikely that the potential disincentivizing effects from the
“agreement to agree ” will reveal a sufficient degree of harm to
competition such that there is no need to examine its effects.154
Instead, it seems more likely that the a greement to agree is not a “by
object ” infringement, and therefore we should fully examine its
effects  on competition  to determine whether it is a breach of Article
101(1).

153 Competition law looks both at direct and indirect restrictions on competition
(the text of Article 101(1) prohibits, inter alia, all agreements which “ directly or
indirectly fix purchase or selling prices or any other trading conditions ”). An
indirect res triction is one that is de facto  capable of restricting competition on the
market. For example, Vertical Guidelines , supra  note 53, at ¶ 48 explains that
resale price maintenance (RP M), that is agreements or concerted practices ha ving
as their direct or indirect object the establishment of a fixed or minimum resale
price or a fixed or minimum price level to be observed by the buyer gives rise to
the presumption that the agreement rest ricts competition and thus falls within
Article 101(1). RPM that are set out in contractual provisions or concerted
practices that directly establish the resale price constitute ‘clear -cut’ and direct
restrictions of competition. The Vertical Guidelines go  on to explain that RPM
can also be achieved “through indirect means.” (¶ 48)  Examples of these indirect,
de facto RPM mechanisms include “an agreement fixing the distribution margin,
fixing the maximum level of discount the distributor can grant from a p rescribed
price level, making the grant of rebates or reimbursement of promotional costs by
the supplier subject to the observance of a given price level, linking the prescribed
resale price to the resale prices of competitors, threats, intimidation, warni ngs,
penalties, delay or suspension of deliveries or contract terminations in relation to
observance of a given price level.” (¶ 48) These indirect means incentivise or
induce the buyer to comply with the RPM, even if they are not directly obliged to
do, a nd can also breach the Article 101(1) prohibition.
154 Case C -67/13 P, Groupement des Cartes Bancaires v Commission, supra  note
51, ¶ 57.
2021 ] Artificial Intelligence & Antitrust  487
In the present case, the disincentivizing effect of the
“agreement to agree ” could mean that the undertakings compete less
vigorously on both output and innovation compared to the
counterfactual.155 To constitute a breach of Article 101(1), t his
adverse  effect must be both likely —that is , expected with a
reasonable degree of probability —and appreciable —that is , not
insignificant.156
However, any “agreement to agree ” that includes the Big
Tech companies is likely to lead to appreciable anticipated effects,
given their size on the market.157 Restric tive effects on competition,
that is to say  whether the restriction is sufficiently likely , will largely
depend on how the Windfall Clause is structured. I n this regard, i t
is advisable to structure the Clause in a way that mitigates  the
probability or mag nitude of any disincentivizing effect as much as
possible.

155 Article 101(3) Guidelines, supra  note 56, ¶ 24.
156 This requirement of “appreciability ” is given expression in the Commission
Notice on agreements of minor importanc e which do not appreciably restrict
competition under Article 81(1) of the Treaty establishing the European
Community (de minimis), 2001 O.J. (C 368) 13 (“ De Minimis  Notice”) . The
notice states that agreements will not appreciably restrict competition if the
aggregate market share of the parties to the agreement does not exceed 10%, where
the agreement is made between actual or potential competitors ( ¶ 8). However, the
de minimi s doctrine does not apply to “by object ” restrictions ( ¶ 2). Further, the
Horizontal Cooperation Guidelines state that “ restrictive effects on competition
within the relevant market are likely to occur where it can be expected with a
reasonable degree of p robability that, due to the agreement, the parties would be
able to profitably raise prices or reduce output, product quality, product variety or
innovation. This will depend on several factors such as the nature and content of
the agreement, the extent to  which the parties individually or jointly have or obtain
some degree of market power, and the extent to which the agreement contributes
to the creation, maintenance or strengthening of that market power or allows the
parties to exploit such market power” (¶ 28).
157 Horizontal Cooperation Guidelines, supra  note 94, ¶ ¶ 39 -47.
488 Yale Journal of Law & Technology  [Vol. 23
Overall, however, it may be difficult for a regulator to show
to the requisite evidentiary standard that a disincentivising effect is
“likely .” The EU courts are clear that the EC must support a
“restrictive effects ” finding with  robust evidence of the effects
alleged.158 This requires some speculation about firms ’ behavior  and
incentives , which may be difficult to back up with convincing
evidence. Unless the Windfall Clause is clearly structured in  a way
that would have a disincentivising effect, there is a low risk that the
“agreement to agree ” will infringe article 101(1) on a “by effects ”
analysis.
Conclusion & Mitigating Steps
Overall, there is likely to be a low risk of an “agreement to
agree ” to the Windfall Clause breaching Article 101(1). This risk
will hinge on the extent of the disincentivizing effect discussed
above. The best way to mitigate the risk is to do away with th e
“agreement to agree ” altogether. Further research should explore
whether this is commercially viable: whether  companies will agree
to the Windfall Clause even if they cannot be sure that their
competitors will do the same.

158 For example, in European Night Services v Commission  the General Court
rejected the EC’s finding that the establishment of a JV could restrict actual or
potential competition betwe en its parents. The Court considered this to be “a
hypothesis unsupported by any e vidence or any analysis of the structure of the
relevant market from which it might be concluded that it represented a real,
concrete possibility.” (Cases T -374/94 etc, Europ ean Night Services v
Commission, 1998 E.C.R. II -3141).
2021 ] Artificial Intelligence & Antitrust  489
Other ways to mitigate could involve reducing the
disincentivising effect of the Windfall Clause itself. As well as
taking a tiered approach to the “taxing ” of any windfall, other
mitigation steps could include incorporating a good faith obligation
to implement  the Windfall Clause into a company ’s articles of
association . Any attempts to circumvent it, or avoid triggering the
Clause, would then be a breach of the company directors ’ duties.
The right publicity around the Windfall Clause may also be
important , so that, for example, one could see the triggering of the
Windfall Clause as a positive fulfilment of a company ’s corporate
social responsibility (CSR).159 The objective is that such publicity
might lead the Windfall Clause to encourage investment in the firm
in question, rather than investors seeing it  as a negative funne lling
away of profits away from potential payout  in dividends.160
IV. LEGAL ANALYSIS OF PROPOSED FORMS OF COOPERATION :
GENERA L TRUST BUILDING
In this section, we analyze  cooperation strategies to
encourage safe and responsible AI development include mutual or

159 This could be plausible e specially in light of current growing interest around
sustainable, responsible and impact investing (SRI), and so may in fact encourage
investment from certain, socially impa ctful investors. For example, according to
the Global Impact Investing Network (GI IN), the global impact investing market
has grown to more than US$500 billion by 2019, more than doubling from an
estimated US$ 228 billion in 2018. See H. Liang and L. Renne boog, Corporate
Social Responsibility and Sustainable Finance: A Review of the Lit erature
(September 24, 2020) , European Corporate Governance Institute – Finance
Working Paper No. 701/2020, forthcoming in the Oxford Research Encyclopedia
of Economics and Finance.
160 Note in a draft companion piece we assess AI governance proposals that m ight
intersect with abuse of dominance, public procurement and/or state aid concerns.
See supra  note 35.
490 Yale Journal of Law & Technology  [Vol. 23
third -party  safety monitoring and standard setting . Companies
should ideally implement t hese strategie s as soon as possi ble, as
they address AI risks from both a long -term and near -term
perspective. We survey three types of cooperation: agreements,
information sharing , and standard setting . As before, for each type
of cooperation we explain what it is, why we would want it  from an
AI governance perspective , what competition concerns it might
raise, and how to address those concerns.
A. Agreements Between Competitors
Cooperation between competitors can take the form of an
“agreement ” that is specified and formalized in a legal agreement,
or written declaration, a verbal agreement, an “agreement to agree ”
or any other form of understanding. Agreements can raise
competition concerns  under Article 101(1) if they are capable of
restricting competition . The Assi st Clause and the Windfall Clause
are both agreements , and we have assessed above their compatibility
with Article 101(1) . In this section, we will examine a third type:
agreements on secure enclaves.

2021 ] Artificial Intelligence & Antitrust  491
1. Agreements on Secure Enclaves
Key Recommendations
Compe tition law will generally view a n agreement between
AI companies and hardware companies that do not compete with
each other as pro -competitive. Any agreements that competing AI
companies enter into on secure enclaves, however, are likely to
constitute a “horizontal standards ” agreement between
competitors.161
Analysis
One proposal within AI governance is that AI and hardware
companies work together to explore the feasibility of developing
secure enclaves for application in AI accelerators, or otherwise
devise best practices  on how to use secure enclaves , otherwise
known as Trusted Execution Environments , in machine learning
contexts.162 A secure enclave is a set of software and hardware
features that together provide an isolated execution environment that
enables a set of strong guarantees for applications running inside the
enclave. Secure enclaves provide guarantees of security , provide d
that their underlying assumptions cannot be broken. These
mechanisms help to focus defensive efforts and assure users that
relatively extreme measures would be required to subvert their trust.

161 Assessment of horizontal standardization agreements are further discussed in
infra  Section IV.C  (Standard -setting).
162 Brundage, Avin, Wang et al. , supra  note 4.
492 Yale Journal of Law & Technology  [Vol. 23
If the collaboration is between AI and hardware companies,
it is possible that they are not competitors. This is because AI
software  and hardware are complementary r ather than substitute
products . Competition law generally deems collaboration between
suppliers of complementary products to be pro -competitive and
efficiency -enhancing (so -called “vertical agreements ”).163
It may be that after AI companies work with hardware
companies to develop best practices on secure enclaves, AI
companies seek to agree a standard or “best practice ” between each
other. This may consti tute a horizontal agreement on safety or
technical standards between competing AI companies , in contrast to
the vertical agreement between AI and hardware companies .164 B.
Information exchange
B. Information Exchange
A variety of AI risk mitigation strategi es involve monitoring
AI labs and sharing information (such as incident sharing or
compute costs ). Although these proposals pursue legitimate aims,
competition law risks can arise if this monitoring and information
sharing includes the exchange of commerci ally sensitive

163 See, e.g., Horizontal Cooperation Guidelines, supra  note 94, ¶ 50. For further
discussion of vertical agreements, see supra  Section II.A (Article 101(1)
Prohibition).
164 Horizontal Cooperation Guidelines, supra note 94, ¶ 55. See infra Section IV.C
(Standard -setting) for further assessment of standardization  agreements.
2021 ] Artificial Intelligence & Antitrust  493
information between competitors, whether directly or indirectly
through a third party .
Generally , any exchange between competitors of
information that reduces strategic uncertainty in the market can
constitute  an unlawful information exchange under Article
101(1).165 This can lead to two key anti -competitive effects: first, it
may enable undertakings to predict each other's behavior  and
thereby coordinate their behavior on the market ; and second,
undertakings may  use it to support a ca rtel, for example,  to spot
deviation  and ensure compliance with a  price -fixing agreement.
Such “strategic ” information can include (but is not limited
to) the following types of non -public information: (1) prices,
including  wholesale or retail prices, pricing strategy, discounts,
margins, costs; (2) sales and volumes, including  sales volumes,
revenues, stock levels, market  share, production capacity; (3) R&D,
including details of a company ’s technology, R&D programmes and
their results, investments; (4) product strategy, including product
development or marketing plans; comm ercial strategy information
(geographic growth and  business expansion or contraction plans );
and (5) customers and bidding strategy, including  customer lists,
individual suppliers, terms and conditions of supply, bid amounts ,

165 Horizontal Cooperation Guidelines, supra  note 94, ¶ 86.
494 Yale Journal of Law & Technology  [Vol. 23
terms , and the decision on  whether or not to respond to a particular
tender.
In this section,  we consider forms of information exchange
or auditing  directly between competitors, information exchange
with a third party , and information exchange with “bounty hunters .”
1. Mutual Monitoring
Key Recommendations
Mutual monitoring by one AI company of another
competing AI company may raise competition law concerns where
it involves the exchange of commercially sensitive information such
as R&D plans, product roadmaps, non -public insight into how the
technology or A I system  works. Using a third party  rather than a
competitor to carry out th is monitoring can significantly reduce the
competition law risk. This could be a research institute or a n
association such as PAI .
Analysis
One AI governance proposal is for AI  companies to monitor
or audit  other AI companies ’ development.166 The benefit s of using
other AI companies to monitor in this way is that they may be
equipped with the necessary scarce resources and expertise to be
able to carry out this type of monitoring to the requisite standard ,

166 Brundage, Avin, Wang et al. , supra  note 4.
2021 ] Artificial Intelligence & Antitrust  495
and it may assure  them that others are not “cutting corners ” on
responsible development.
Mutual monitoring seeks to pursu e a legitimate and pro -
competitive purpose;  it may require sharing of non -public
informa tion about the AI company ’s R&D, technology , or
algorithms. As the Horizontal Cooperation Guidelines explain, if
companies compete on R&D then the exchange of technolog y data
is most likely to be risky from a competition law perspective . The
risk is that in monitoring  the safety  or ethics  of an AI -related
technology, a competitor will also gain insight into it s competitor ’s
AI technology, such as  the proprietary algorith m. This potentially
breaches Article 101(1).
As well as the nature  of the information exchanged under the
monitoring , the strategic usefulness (and therefore level of
competition law risk) will depend on several  other factors.  First, it
is important whether the information is public or non -public. The
exchange of genuinely public information is  a far lower risk than
exchanging confidential information. However,  presumably the
information used for mutual monitori ng is more likely to be the
latter because AI companies will wish to keep information on their
technology confidential to protect their IP.  This could increase the
competition law risk.
496 Yale Journal of Law & Technology  [Vol. 23
Second, aggregated data will be lower risk than
individuali zed data. However, it may be difficult to aggregate  the
monitoring information , as the concern is development within an
individual company rather than in the industry more generally.
Third, the older the data, the lower the risk.167 Although the
exchange of present  or past safety results, for example, is lower risk
than exchanging information on future R&D development, risks
may still arise if it remains indicative of competitors ’ future conduct.
The data will presumably be relatively recent  in order to be useful
for monitoring purposes . This may increase the competition law
risk. Fourth, the exchange must affect a sufficiently large part of the
relevant market to be capable of having a restrictive effect on
competition ( the threshold is likely to be 10% combined marke t
share between the companies exchanging the information).168 As we
are most interested in monitoring the leading AI development
companies, they are also more likely to have a significant share of
the relevant AI development market.169
The above analysis explains when the information
exchanged as part of mutual monitoring may b e strategically
significant:  when it gives the auditing company insight into the

167 Generally , the EC will regard information that is more than one year old as
historic, although in a fast - moving market such as AI developme nt it is likely to
age far quicker. See Horizontal Cooperation Guidelines,  supra  note 94, ¶ 90 n .67.
168 De Minimis Notice, supra note 156.
169 See supra  Section II (Background : The Prohibition on Anti -competitive
Agreements under Article 101(1) ).
2021 ] Artificial Intelligence & Antitrust  497
likely future behavior  or market strategy of a competitor.
Information  exchanges are especia lly capable of restricting
competition on the market where they are  non-public, non -
aggregated, relatively recent , and cover a large part of the market.
Nevertheless, competition law recognize s that some exchange s of
commercially sensitive information can be efficiency -enhancing
and pro -competitive. The analysis below will consider the exten t to
which the efficiencies, such as the  safety enhancements arising from
the monitoring  mechanism , can satisfy the four requirements of
Article 101(3).
The exchange of safety development information would
achieve  a pro -competitive benefit under the first conditions of
Article 101(3) because it should improve  safety.170 However,
another important criterion to satisfy under Article 101(3) is
indispensability, that is  that the restrictions on competition do not
go beyond what is necessary to  achieve the efficiency gains.171
In this scenario, it seems that the mutual monitoring could
feasibly be done by a third party , such as a pure research ins titute ,
that is not an active “undertaking ” that either does not provide  goods

170 Case IV/33.863, Asahi/Saint -Gobain, Commission Decision of 16 December
1994, 1994 O.J. (L 354) 87, ¶  ¶ 24-34 (the Commission found that even though
there were competitive risks with regard to a joint venture, it satisfied the
conditions of Article 101( 3) because the new type of car glass would contribute
to driver safety, and the lighter weight would help fuel  efficiency. The cooperation
between the parties had the effect of reducing the R&D costs for this new car glass
and the entry of such products on  the market would be accelerated.)
171 Horizontal Cooperation Guidelines, supra  note 94, ¶ 101.  For further
discussion of these other forms of mutual monitoring .
498 Yale Journal of Law & Technology  [Vol. 23
or services on the market or is in the AI market. This would be a
realistic and less restrictive means of achieving the same objective.
Moreo ver, it is not enough to simply use the third party  as a conduit
to pass the same commercially sensitive information between the
same competing companies, as that could itself be an unlawful cartel
or information exchange.172 If this is done via a trade association,
the dissemination of sensi tive information by the trade association
is treated as an information exchange  or concerted practice by the
members themselves.173 The companies must trust the  third party  to
carry out the monitoring and perhaps to publish results of level of
compliance without disclosing commercially sensitive information ,
unless the third party  releases it  into the public domain.
To satisfy the other grounds of Article 101(3), companies
should pass on the efficiency gains to consumers , and the companies
involved in the exchange must not eliminate competition in a
substantial part of the relevant market. These two conditions should
be satisfied so long as there are some competitors on the market that
are outside of the monitoring system.

172 For example, in 2008 the European Court of First Instance affirmed the
Commissions’ decision that a Swiss consultancy firm, A C Treuhand, had
breached competition law for facilitating an organic peroxide cartel, f inding that
Treuhand’s role, though not active in the market of the reelvant product, was that
of a passive co -perpetrator and so was caught by Article 101” (Case T -99/04, AC -
Treuhand AG v Commission, 2008 E.C.R. II -1501, ¶ 122).
173 See, e.g., Case 45/85, V erband der Sachversicherer v Commission, 1987
E.C.R. 405, EU:C:1987:34 ; id., ¶ 105.
2021 ] Artificial Intelligence & Antitrust  499
Conclusion & Mitigating Steps
The information exchange involved in mutual monitoring
risks breach ing Article 101(1). Furthermore, it may be difficult to
rely on  the Article 101(3) exemption. This is because , unless there
is a good reason why competitors must mutually monitor each other,
it seems that the exchange will not satisfy the “indispensability ”
limb of Article 101(3) , as there are less restrictive alternatives  such
as using a third party  to monitor i nstead.
Overall, using a third party  rather than a competitor to carry
out the monitoring can reduce the competition law risk. That third
party  could be a research institute or a n association such as PAI. If
that is not feasible, for instance  because only competing AI
companies have the necessary technology and skills to effectively
monitor, the parties should ensure there are contractual and
operational safeguards that information shared is only used in those
companies for the immediate purpo se. In addition, the monitoring
should only share  information that is strictly necessary for the
monitoring purposes, and nothing more. Other ways to  mitigate the
risk include sharing information that is (as far as is possible) public ,
historic , aggregated  and/or anonymized  and ensuring that every
company is able to access this initiative ( to the extent a company
gets a competitive advantage from being monitored  or receiving
results of other companies ’ monitoring results ).
500 Yale Journal of Law & Technology  [Vol. 23
2. Other Forms of Mutual Monitoring
Other forms of  mutual monitoring include shared red
teaming or “white -hat” hacking  to uncover vulnerabilities; incident
sharing; and compute accounting.
These all raise a similar concern to mutual monitoring in  the
previous section —that monitoring will lead to the sharing of
competitively sensitive information between competitors. One can
mitigate them  in a similar way, most importantly by engaging a third
party  to carry out the monitoring instead. This is particularly
advisable for compute accounting, where the information exchanged
is a key component of cost and therefore of high commercial
sensitivity and commensurately higher competition law risk.
3. Communication, especially “Heads -up”
Key Recommendations
The “heads -up” strategy could raise competition risks to the
extent that it involves the exchange of strategically important,
forward -looking R&D data. One could also see  it as an agreement
between competitors to restrict or delay output.
One can significantly  mitigate  risks  if a standard -setting
organization  (SSO) carries out any pre -publication review based on
defined, objective publication norms  or criteria .174

174 See discussion infra  Section IV.C.2 (Publication and release norms).
2021 ] Artificial Intelligence & Antitrust  501
Analysis
Greater communication between competitors can be
valuable from an AI governance perspective. This  could include
regular updates, clear communication channels and people
responsible for communication175, and joint events such as
workshops, retreats, conferences, residencies, country visits.
In the OpenAI report on lessons learn ed from GPT -2, the
authors  emphasi zed the importance of communication,
recommending:
“Build communication channels across organizations.
Research results are often kept private until the associated paper is
published. Private results hinder coordination, especially for release;
for example, we were largely unable to retrieve statuses of
replication efforts. The norm of privacy around unpublished
research holds legitimacy, as seen in non -disclosure agreements, but
robust communication channels between AI organizations will be
needed in the future. For example, prior to first announcing GPT -2,
we were unsure whether and how quickly other labs would
eventually develop and publish similar systems. Since the impact of
an individual publication decision often depends on others ’

175 Brundage, Av in, Wang et al. , supra  note 4, at 9  (“[C]lear identification of roles
or offices within organizations who are responsible for maintaining and deepening
interorganizational communication.” ).
502 Yale Journal of Law & Technology  [Vol. 23
publication decisions, we encourage AI labs to experiment with their
approaches to interorganizational communication. ”176
This subsection will focus on a pre-publication  or pre -release
“heads -up” to competi tors about R&D or products. These  are
intended to give competitors an opportunity to point out reasons why
the R&D product should be stalled , for example  because of
outstanding safety concerns or because it can be exploited by
malicious actors.177 Including others and giving them an opportunity
to express concerns can build a culture of scrutiny and
accountability amongst AI firms, especially in AI safety.
However,  communications and “heads -up” practices may
raise competition law risks and are potentially higher risk than the
examples of information exchan ge discussed above for several
reasons. First, the “heads -up” could involve sharing future R&D  or
product releases. Future information is more commercially sensitive
than past information because it gives direct insight into a
competitor ’s future behavior,  allowing the competitor to adapt their
own commercial strategy accordingly.
Second, the “heads -up” may go beyond exchange of
commercially sensitive information and provide an opportunity for
competitors to reach an agreement to limit or delay output. Both of

176 Irene Solaiman et al., Release Strategies and the Social Impacts  of Language
Models , OPENAI REPORT  24 (November 2019), arXiv:1908.09203.
177 For example, several groups and companies have not released full language
models, starting with GPT -2.
2021 ] Artificial Intelligence & Antitrust  503
these factors could make this a “by object ” infringement, which
refers to agreements that are by their very nature sufficiently
harmful to competition that ther e is no need to look at its anti -
competitive effects.178 Although a “by object ” infringement can still
theoretically benefit from the Article 101(3) exemption —indeed,
any agreement can theoretically benefit from the exemption179—it
is in practice very diffic ult for it to do so.180
In addition, the “heads -up” strategy may struggle to satisfy
the conditions under Article 101(3).  First, to satisfy the
“efficiencies ” condition, one could argue that the “heads -up”
generates efficiencies by redu cing harmful disclos ure by limiting
disclosure that might facilitate the malicious use of AI  by criminals
or terrorists . This s ecurity  improvement may constitute “an
improvement in the production or distribution of goods or in
technical or economic progress ” that outweighs an y detrimental
effects on competition.181 It is not advisable to argue efficiencies
from building interpersonal trust between AI labs, because it is

178 See, e.g., Case COMP/39188, Bananas, Commission decision of 15 October
2008, 2009 O.J. (C189) 12,  upheld on appeal, Case T -588/08,  Dole Food v
Commission, EU:T:2013:130, upheld on further appeal, Case C -286/13P, Dole
Food v Commission, EU:C:2015:184. It is also well -established that agreements
between competitors to fix prices, limit output or share markets are restrictive by
object:  see e.g. , Article 101(3) Guidelines, supra  note 56, ¶ 21 and Case C -209/07,
Competition Authority v Beef Industry Development Society, 2008 E.C.R. I -
8637, EU:C:2008:643 (agreement between competing companies to reduce
capacity was held to be a ‘by object’ infringement).
179 See, e.g., Case C -439/09, Pierre Fabre Dermo -Cosmetique SAS, supra  note 59.
180 See Horizontal Cooperation Guidelines, supra  note 94, ¶ 46.
181 Cases 56/64 and 58/64, Consten and Grundig v Commission, 1966 E.C.R. 299,
at 348; Case T -65/98 Van den Bergh Foods Ltd v Commi ssion, 2003 E.C.R. II -
4563, ¶ 139.
504 Yale Journal of Law & Technology  [Vol. 23
rather speculative and is a less -clear cut efficiency compared to
security .
However, as mentioned above, a n arrow view of Article
101(3) only permits agreements that would bring about
improvements in economic efficiency. The Article 101(3)
Guidelines clearly support this view .182 Even assuming that  a
broader view of efficiencies is possible, it is also unclear whether
we would be able to prove and quantify the alleged s ecurity  gains
with “convincing arguments and evidence ” to fulfil the stand ards of
Article 101(3) with evidence like  expert reports, or economic
analysis .183 It may be particularly difficult to show that a purported
gain in s ecurity  has a sufficient causal l ink to the “heads -up” system
such that the reduction in potential malicious use of AI is
sufficiently likely to arise from it.184
Second, even if the “efficiencies ” condition is satisfied, it
may be difficult to establish that there are no less restrictive

182 Article 101(3) Guidelines,  supra  note 56.
183 See, e.g., Case COMP/34579 MasterCard, Commission decision of 19
December 2007, ¶  ¶ 686, 690 , 732. The decision explains:
A claim that an interchange fee mechanism creates efficiencies…must
be founded on a detailed, robust and compelling analysis that relies on
its assumption and deductions on empirical data and facts…general
references to economic theory [that] do not go beyond paraphrasing
(partly inaccurately) some general conclusions that might be drawn from
economic models, but do not specify the models used, th e assumptions
relied on and the facts and data that support the analysis…does not
present a detailed and robust economic and empirical analysis.
184 See, e.g., Cases C -501/06P, etc, GlaxoSmithKline Services v Commission,
2009 E.C.R. I -9291, EU:C:2009:610, ¶ 94 (the ECJ held that the Commission’s
approach may involve determining whether “it seems more likely either that the
agreement in question must make it possible t o obtain appreciable advantages or
that it will not .”).
2021 ] Artificial Intelligence & Antitrust  505
alternatives to achieve the same aim as the “heads up .” For example,
companies could share  pre-publication R&D  or products plans with
a third -party  organization  that provides an objective view (or even
certification) as to whether it satisfies a requisite standard. T his
seems to be a viable alternative that both achieves the legitimate
purpose in a more consistent and effective way while significantly
reduc ing the competition law risk.
Conclusion & Mitigating Steps
Given the breadth of the Article 101(1) prohibition, even a
“heads -up” in the form of an informal notification or request for
feedback from one AI company to another could raise risks under
Article 101(1). These risks could arise if the “heads -up” involves an
exchange of commerc ially sensitive information —for example,
forwar d-looking R&D data —that reduces one company ’s
uncertainty about its competitor ’s commercial strategy. If the
“heads -up” strategy provides an opportunity for competing AI labs
to agree or reach an understanding on restricting or delaying the
launch of a tec hnology, this could also give rise to material
competition law risks because it potentially constitutes an agreement
between competitors to restrict output.185

185 Of course, it is possible that l abs may (and in fact do) disagree about the safety
implications of some R&D data, even in the context of heads -ups being given. If
no agreement or ‘meeting of minds’  occurs on whether or not to release the data,
then Article 101(1) is unlikely to apply. Ho wever, even if no agreement is reached,
the discussions could still involve exchange of commercially sensitive
information (e.g. , R&D data) which raises separate competition law risk.
506 Yale Journal of Law & Technology  [Vol. 23
Furthermore, it may be challenging to argue that the
agreement satisfies Article 101(3). First, the increase in safety may
be difficult to evidence and quantify. Second, there are likely to be
less restrictive means available to achieve the same aims as the
“heads -up” strategy.
A lower risk alternative could be to send the pre -publication
material to  an SSO such as PAI for review based on pre -defin ed,
objective publication norms or criteri a.186
4. Seconding Staff
Key Recommendations
To help build trust between AI companies, AI companies
could second their researchers or engineers to  competing AI labs.
Several kinds of joint projects between competitors could be
valuable from a cooperation perspective, such as technical projects,
research efforts and papers, policy projects, projects to serve some
social good, or efforts to expand acc ess. This analysis will look at a
specific scenario where secondment of staff is part of an R&D
cooperation agreement, for example to implement the Assist Clause.
Analysis
The secondment of staff to a competitor necessarily involves
the exchange of commer cially sensitive information. The staff

186 Note that this is the model proposed for AI systems in high -risk secto rs and
application areas in the EU White Paper on Artificial Intelligence: A European
Approach to Excellence and Trust  (Feb. 19,  2020 ),
https://ec.europa.eu/info/sites/info/files/commission -white -paper -artificial -
intelligence -feb2020_en.pdf .
2021 ] Artificial Intelligence & Antitrust  507
member is likely to receive some information about the competitor
firm’s R&D plans, technology, costs,  or capacity . The staff member
may also wish to share information about their “home ” company ’s
R&D  or technology to legitimately assist in the joint R&D project.
At the same time, this exchange of commercially sensitive
information also risks breaching Article 101(1). The information
exchange reduces the strategic uncertainty between the competing
AI labs if the infor mation were to be used to inform the labs ’ overall
R&D and commercial strategy. However, where the information
exchange is part of a legitimate agreement or transaction , such as an
R&D cooperation agreement that has been found to be lawful, it may
qualify as an “ancillary restraint ” and fall outside the Article 101(1)
prohibition altogether.187
The information exchange will be an ancillary restraint
falling outside Article 101(1) if it is necessary and proportionate to
the implementation of a main, legitimat e agreement. To qualify as
an ancillary restrai nt, the exchange must first be “directly ” related
to the R&D cooperation agreement . For this condition to be
satisfied, the  exchange must be  subordinate to the implementation
of that transaction , and inseparably and evidently linked to it.

187 We have not assessed this as a standalone information exchange because we
assume it will be part of some kind of R&D cooperation agreement. If it is
standalone, it should be assessed under normal Article 101(1) principles
especially those relating to info rmation exchange - the assessment in section above
on auditing/information exchange should apply similarly in this instance.
508 Yale Journal of Law & Technology  [Vol. 23
Second, the exchange  must satisfy the requirement of objective
necessity , that is  one must show  that without the information
exchange , the R&D cooperation would be impossible  to carry out .188
It is not sufficient  if it is merely more difficult or less probable.189
Third, t he information exchange  must be proportionate in duration
and scope to the implementation of the main transaction. This means
there should only be the minimum amount of information exchange
strict ly necessary to achieve the legitimate purpo se of the R&D
cooperation —that is, to jointly pool engineer resources to boost
safety development.190 To help ensure these conditions are satisfied,
it is important to take a number of mitigating steps outlined be low.
Conclusion & Mitigating Steps
In theory, the information exchange related to the staff
secondment should be a lawful ancillary restraint that falls outside
Article 101(1), subject to  putting  the safeguards above in place.
However, in reality, the risk  is that there will be “leakage ” of
information beyond the confines of what is lawful, which is natural
when a secondee is mixing with a competitor AI lab ’s employees  on

188 An ancillary restraints  assessment borrows some concepts from the exemption
under Article 101(3) such as ‘necessity’. However, an important distinction is that
it does not require the challenging exercise of weighing up pro - and anti -
competitive effects. That exercise is reserve d exclusively for Article 101(3)
(Article 101(3) Guidelines, supra  note 56, ¶ 31).
189 Id., ¶ 29 ; Case T -112/99, Metropole Television (M6) v Commission, 2001
E.C.R. II -2459, ¶ 109 .
190 Brundage, Avin, Wang et al. , supra  note 4 , ¶ 9.
2021 ] Artificial Intelligence & Antitrust  509
a day -to-day basis. Therefore, this strategy could give rise to
competition law risk.
Several  mitigating steps are possible . First, to ensure that the
information exchange relates to only what is sufficiently related and
objectively necessary, the seco ndees must be sufficiently “ring
fenced ,” for example  under strict contractual obligations to use the
competitor ’s commercially sensitive information only f or a
legitimate purpose of enhancing safety development. They should
not be using it for any other p urpose, such as to inform the
secondee ’s company ’s own R&D plans unrelated to enhancing
safety development.
Similarly, the secondee should disclose their own
company ’s commercially sensitive information only for the
legitimate purpose. There should also be  corresponding contractual
obligations between the two companies that any information that
they receive about each other can only be used for the legitimate
safety purpose and no other , and especially not to inform each
other ’s commercial strategy. Practic al safeguards should also bolster
contractual ones . For example, the secondee should be given
sufficient and targeted competition law training and should be
physically located in a team that is focused on , for instance,  safety
R&D and no other functions. I n particular, the secondee should be
510 Yale Journal of Law & Technology  [Vol. 23
separated from teams that are invo lved in commercial strategy, like
sales and marketing teams.
However, even with  the most stringent contractual and
practical restrictions on information exchange, it may be difficult in
practice to guarantee that the secondee never shares any more
information than is necessary for the legitimate purpose, and that the
companies receiving the competitor information guards against the
temptation to use that information to generally inform i ts
commercial strategy. Given the potential risks, it may be advisable
to opt for alternative ways to achieve the same purpose. For
example, using a third party  such as PAI to send its own staff in to
carry out quality control may be one option that signif icantly lowers
the competition law risk.191 PAI could then draw up a periodic “trust
report ” to review how “trustworthy ” or reliable they believe AI
development is in the different AI labs.
5. Third -party Auditor s
Key Recommendations
Using a third -party auditor (such as PAI) is a less risky
alternative to mutual monitoring between competing AI companies.
However, companies should not use the third -party  organization  as
a mere  intermediary for the competing firms to exchange the same
commercially sensitive information. Instead, the third -party

191 See infra  Section IV.B (Information Exchange) .
2021 ] Artificial Intelligence & Antitrust  511
organization  should carry out the auditing on an independent basis,
without disseminating any commercially sensitive information
between competitors. Alternatively, the organization  could collect
commercially sensitive information and then disseminate that to the
competing p arties in a “saniti zed” form:  aggregated  or anonymi zed.
Analysis
The process of AI development is often opaque to those
outside a given organization and various barriers make it
challenging for third  parties to verify the claims developers make .
As a result, it may not be easy to verify claims about system
attributes. AI developers have justifiable concerns about being fully
transparent with information conc erning their AI develop ment, for
example to  prevent malicious use. One potential solution is to set up
or use  a third -party  association or standards body that could carry
out independent, third -party  auditing of AI systems. AI companies
could legitimately fund this , so long as it nevertheless operates as a
separate and independent body.
As mentioned above, many forms of cooperation and
auditing that we consider  in this paper are more likely to comply
with competition law if a third -party  organization  rather than
anothe r AI company carries them out . However, this depends on two
key points. First, any third -party  organization  will not mitigate
competition law risk if simply used as a “cover ” or vehicle for
collusive conduct or conduit for illegal information exchange
512 Yale Journal of Law & Technology  [Vol. 23
betw een competitors. However,  where an  organization  such as PAI
carries out the third -party  audit in a ge nuinely independent manner ,
this is likely to be  compliant with competition law.
Second, a trade association  or cooperative body will run
risks under compe tition law if it restricts the ability of market
participants to compete. Therefore , to the extent that a third -party
auditing or certifying companies in this way would confer a
significant competitive advantage, all AI companies  should have the
opportunit y to do so.  If the audit is restricted, for example  to PAI
members only, that means membership of PAI is likely to be an
essential or important pre -conditions to operating on the market. It
follows that unreasonable refusal to admit members could restrict
competition and  infringe Article 101(1). One should therefore
design a ny restrictions on membership rules to fall within the  Article
101(3) exemption . To help satisfy the indispensability requirement
in Article 101(3), the admi ssion rules must be objective, sufficiently
determinate, and capable of uniform and non -discriminatory
application.192
Conclusion & Mitigating Steps
Generally, as mentioned above, it is less risky to use a third -
party organization  to carry out some of the envisaged risk mitigation
auditing exercises. The exchange of competitively sensitive

192 Cases T -528/93 etc, Metropole Television v. Commission, 1996 E.C.R. II -649,
EU:T:1996:99, ¶ 102.
2021 ] Artificial Intelligence & Antitrust  513
information or other anti -competitive agreements leads to highe r
competition law risk when competitors carry it  out between
themselves . They can significantly mitigate ri sks if auditing is
delegated to a third -party  organization .
However, this is not a “catch -all” solution. If the competing
firms simply use the third -party organization  as an intermediary to
carry out an anti-competitive cartel  or information exchange, then
that arrangement will still breach Article 101(1). To reduc e
competition law risk to a low, manageable level, the third -party
organization  should: (1) operate as an independent and distinct
entity in carrying out the audits, vis -a-vis the AI labs that fund it; (2)
make the possibility of audit open to all AI labs (or in the case of
PAI, only to PAI members but subject to the criteria above on
membership rules); (3) not disseminate any commercially sensitive
information that it re ceives as a result its audit to any other AI labs,
except in a “sanitized ” format.
6. Bias and Safety Bounties
Key Recommendations
Competition risks can arise where the bounty hunter
discloses confidential information about its technology or R&D in
reporting how its systems exposed the vulnerability. However, on
its own , such information may not be strategically significant and
may be justified under the Article 101(3) exemption.

514 Yale Journal of Law & Technology  [Vol. 23
Analysis
“Bug bounty” programs, in which organizations commit to
giving individua ls recognition or compensati on for reporting bugs,
have become popular  in the information security industry as a way
to compensate individuals for recognizing and reporting bugs,
especially those related to exploits and vulnerabilities. Bug bounties
enable  individuals to report bugs directly to the institutions affected,
rather than immediately exposing the bugs publicly or selling the
bugs to others. To earn the bounty, individuals typically need to
articulate the scale and severity of the bugs in order to determi ne
appropriate compensation. Researchers have  suggested the
establishment of  a simi lar system for bias and safety problems
around AI systems .193
The level of competition risk largely depend s on how th e
programme is structured. The first question is whether there is an
exchange of any competitively sensitive information. For example,
an in dividual simply accessing an AI company ’s open - source
software, discovering its vulnerabilities, and reporting them, could
earn the bug bounty . This does not involve any disclosure of
competitively sensitive information, therefore would not give rise to
any competition law risk. Risks may arise, however, if in reporting
the bug the individual or reporting company discloses commercially

193 Brundage, Avin, Wang et al. , supra  note 4, 16.
2021 ] Artificial Intelligence & Antitrust  515
sensitive information about its own systems or processes. However,
this can be mitigated to the extent that it shares  only as much
information as is strictly necessary for the legitimate purpose of
enabling better bias or safety development.
Another relevant question is the relationship between the
organization  providing the bounty, and the “bounty hunter ’. If the
bounty hunt er is not an actual competitor or potential competitor to
the relevant AI company then any information exchange is less
likely to raise concerns.194 To be a “potential competitor ,” it must be
likely that the bounty hunter can within a short period of time
undertake the necessary additional investment or necessary
switching costs to enter into the relevant market, if there were a
sufficient profit incentive to do so.195
Conclusion & Mitigating Steps
The competition law risks for a bug bounty programme will
generally be low as long as: (1) there is no or  minimal exchange of
commercially sensitive information; (2) that commercially sensitive
information is made public; or (3) there is no  exchange between
actual or potential competitors on the relevant market. However,
take a scenario where there is some  non-public  exchange of

194 Horizontal Cooperation Guidelines, supra  note 94, ¶ 10. See also discussion
on “horizontal and vertical agreements ” infra  Section II (Background: The
Prohibition on Anti -Competitive Agreements Under Article 101(1)) .
195 Further, this assessment must be based on realistic grounds, and not just a mere
theoretical possibilit y (id., ¶ 10).
516 Yale Journal of Law & Technology  [Vol. 23
commercially sensitive information between competitors because
the bounty hunter has disclosed how its systems exp osed the
vulnerability and the bounty hunter is a competitor. Even where
none of the above three conditions apply, and although the
disclosure  provides some insight into the technical system of the
bounty hunter, it may be justifiable under Article 10 1(3). This may
be possible on the basis that this is not highly strategic information,
and the hunter has exchange d the information that is nec essary to
achieve the objective.
C. Standard -Setting
Competition law usually recognize s standardization
agreements, defined as technical or quality requirements with which
products, production processes or  methods should comply,  as pro -
competitive .196 These agreements can achieve positive economic
effects such as  promoting innovation, en hancing interoperability,
and maintaining and enhancing quality.197 However, standards can
also give rise to some competition  law risks . Th ese include
facilitating unlawful information exchange, foreclosing innovative
technologies and excluding certain comp anies from effective access
to the established standard.198

196 Id., ¶ 257.
197 Id., ¶ ¶ 263, 308, 325 -332.
198 Horizontal Cooperation Guidelines, supra  note 94, ¶ 264  et seq. .
2021 ] Artificial Intelligence & Antitrust  517
Researchers have suggested p articipating in standard
setting .199 We can view t wo types of proposed cooperation as
standard setting : common performance measures and audit trails , on
the one hand,  and publication and release norms , on the other . One
can s tructure  both in ways that do not raise competition law
concerns.
PAI is a particularly relevant body for AI com panies. PAI
collects best practices  and sets  informal standards. This process
consists of PAI staff and “coalitions of the willing ” amongst  its
members. This can involve setting up a steering comm ittee to guide
this process. It publishes  outcomes  on its website for public
consultation, following whi ch it considers responses, although how
it takes responses into account is not entirely clear. Its executive
committee,  which is made up of profit and non -profit companies ,
oversees this process . There is no formal voting process at the
steering committee or executive committee level . Its standards are
non-binding, “best practice ” principles, and compete with several
other voluntary standards. However, given the size and importance
of PAI’s members, it is likely that any standard —even if technically

199 Peter Cihon, Standards For  AI Governance: International Standards To
Enable Global Coordination In AI Research & Development , FHI  TECHNICAL
REPORT  28 (Ap r. 2019), https://www.fhi.ox.ac.uk/wp -
content/uploads/Standards_ -FHI-Technical -Report.pdf; Ethically Aligned
Design, First Editio n: A Vision For Prioritizing Human Well -Being With
Autonomous And Intelligent Systems , IEEE  REPORT  (July 29, 2018),
https://standards.ieee.org/content/dam/ieee -
standards/standards/web/documents/other/ead1e.pdf.
518 Yale Journal of Law & Technology  [Vol. 23
non-binding—will have a significant market impact, that is,  broad
take-up in practice. Some risks may arise because there is some lack
of transparency in how exactly the standard -setting process works .
To address this, PAI could consider publishing a more formali sed
procedure on its website. Given the importance of the public
consultation stage in ensuring fair participation in the standard -
setting process, more clarity on how the process takes views into
account is also advisable .
Other more formal SSOs in this s pace include IEEE,
ICANN, the US National Institute of Standards and Technology
(NIST), International Organization for Standardization (ISO), and
the International Telecommunication Union (ITU). The IEEE, for
example, has stringent standard -setting process es, which is
publishe d on its website.200 These include  ensuring that all interested
parties can participate ; highly visible standard creation processes ;
formal voting procedure ; and right to appeal a standard decision
open to anyone at any point in time.

200  Develop Standards , IEEE SA, https://standards.ieee.org/develop/develop -
standards/govern.html  (last accessed: 25 August 2020) .
2021 ] Artificial Intelligence & Antitrust  519
1. Suites of Tools or Guides
Key Recommendations
If the standard is voluntary and effectively competes with a
number of other voluntary standards, it likely falls outside of Article
101(1) altogether and is the safest course. If not , participation in the
standard -setting process, and the ability of third parties not involved
in the standard -setting process to access and comply with the
standard , should be on fair, reasonable and non -discriminatory
(FRAND) terms.
Analysis
AI developers could create and share suites of tools and
guides for machine learning that include measures of performance
against collaboratively established standards.201 This could be
useful  for fairness, safety, explainability, or robustness to
adversarial inputs. In this section,  we take standards around privacy -
preserving machine learning as our example.
Standardize d public benchmarks —typically a dataset and a
performance target —are a key way to measure, guide and encourage
progress in ML, from MNIST (handwriting)202 and ImageNet

201 Brundage, Avin, Wang et al. , supra  note 4 , at 25.
202 Yann LeCun, Corinna Cartes , Christopher J.C. Burges, The MNIST Database
of Handwritten Digits , http://yann.lecu n.com/exdb/mnist/ (last accessed: Aug. 25,
2020).
520 Yale Journal of Law & Technology  [Vol. 23
(image classification)203 to SQUAD (reading comprehension).204
Researchers can train their systems on these datasets and see how
they perform: are they state -of-the-art? However, as benchmarks
can guide progress , they can also affect in what domains progress is
made and so the overall competitive landscape. For example,
Amazon released logistics benchmarks205 and Audi released self -
driving car benchmarks.206 Ther e can be competing benchmarks.
Chinese universities and companies created the AIBench207
benchmarking suite purportedly  to balance MLPerf,208 which is
largely developed by US universities and companies. PAI is
developing benchmarks for safety, fairness, and other ethical
objectives, for example through ABOUT ML209 and SafeLife.210

203 Dawnbench , STANFORD DAWN ,
https://dawn.cs.stanford.edu/benchmark/ImageNet/train.html (last accessed :
Aug. 25, 2020).
204 Archa Jain, Reading Comprehension With Squad , STANFORD UNIVERSITY
REPORTS ,
https://w eb.stanford.edu/class/archive/cs/cs224n/cs224n.1174/reports/2758649.p
df (last accessed Aug . 25, 2020).
205 Bharathan Balaji, Jordan Bell -Masterson, Enes Bilgin et al ., ORL:
Reinforcement Learning Benchmarks for Online Stochastic Optimization
Problems , arXiv:1911.10641v2 ( Dec. 1, 2019),
https://arxiv.org/pdf/1911.10641v2.
206 Jakob Geyer, Yohannes Kassahun, Mentar Mahmudi et al. , A2D2: Audi
Autonomous Driving Dataset , arXiv:2004.06320v1 (Apr . 14,  2020)
https://arxiv.org/pdf/2004.06320.
207 Wanling Gao, Fei Tang, Lei Tang et al., AIBench: An Industry Standard
Internet Service AI Benchmark Suite , arXiv:1908.08998v2 (Aug . 12, 2019).
208 MLPerf, https://mlperf.org/ (last accessed  Sept. 7, 2020).
209 ABOUT ML , PARTNERSHIP ON AI, https://www.partnershiponai.org/about -ml/
(last accessed  Sept. 7, 2020).
210 Carroll Wainwright and Peter Eckersley, Introducing SafeLife: Safety
Benchmarks for Reinforcement L earning , NEWS IN PARTNERSHIP ON AI (Dec. 4,
2019), https://www.partnershiponai.org/safelife/.
2021 ] Artificial Intelligence & Antitrust  521
One associated problem that standard tools and measures of
performance face is that AI systems tend to lack a traceable log of
steps in problem -definition, design, development, and operation.
This could lead to a lack of accountability for subsequent claims
about those system s’ properties and impacts. Standard audit trails
are a proposed solution for safety -critical applications of AI systems
and refer to a traceable  log of steps in system operation, and
potentially also in design and testing.211
There are two relevant elements here: (1) collaboratively
established standards, especially for privacy -preserving machine
learning technologies and (2) sharing the tools and guides to comply
with the standards. Both potentially offer cle ar pro -competitive
benefits, including  enhancing the privacy protection of machine
learning technologies ; helping companies to comply with standards ;
or helping to more consistently measure a nd audit the compliance of
companies to these standards. However , one must weigh these
benefits against any restrictive effects on competition on the markets
that the standard may affect.
A standardization  agreement should be analyze d in a three -
step fram ework. First, it may not be capable of restricting
competition and therefore fall outside of the Article 101(1)
prohibition altogether if  it meets  certain conditions. If so, no further

211 Brundage, Avin, Wang et al., supra  note 4, at 24.
522 Yale Journal of Law & Technology  [Vol. 23
analysis is required. I f it does not meet these conditions to bring the
agreement outside Article 101(1) , the agreement may be capable of
restricting competition and therefore one should assess it  under
Article 101(1). Third and finally, even if the standardization
agreement infringes Article 101(1) (if the p revious condition s are
not met)  it can still be exempt under Article 101(3) if the standards
are indispensable in producing efficiency gains that outweigh the
restrictions on competition, those gains are passed on to the
consumer, and the agreement does not eliminate compe tition on the
market.
First, a standardization  agreement will not generally be
capable of restricting competition and hence fall outside the Article
101(1) prohibition if the standard does not risk creating market
power212 or where a number of conditions ar e satisfied.213  The
analysis should be done according to the following potential
relevant markets: (1) the market for the product to which the
standard relates (for example, machine learning technology); (2)
where the standard involves selection of separate technology ( such
as a specific type of software to ensure privacy standards), that
related technology market; (3) the market for standard -setting itself,
if different and competing standard -setting bodies or agreements

212 Horizontal Cooperation Guidelines, supra  note 94, ¶ ¶ 277 -278.
213 Id., ¶ ¶ 280 -283.
2021 ] Artificial Intelligence & Antitrust  523
exist; and (4) the market for testing and certifi cation, where
applicable.214
Whether the  standardization  agreement could  create market
power —thereby increasing the competition law risk —will depend,
amongst other factors , on the market shares of the technologies
based on the standard.215 It might not be po ssible to assess with any
certainty at an early stage whether a large part of the industry will
adopt a standard . In that case, one would use the market shares of
the companies participating in developing the standard as a proxy.
If PAI is leading any standard setting , though , it is likely that the
standard -setting agreement will risk creating market power.
If the standard is liable to create market power, it may
nevertheless fall outside Article 101(1) if the following conditions
are satisfied: (1) member s of th e standard -setting organization  or
group are free to develop alternative standards or products that do
not comply with the agreed standard; (2) all competitors on the
market(s) affected by the standard216 (see the four markets described
above) can fa irly participate in the process leading to the selection
of the standard;217 (3) the standard -setting process is open and
transparent, with an  objective and non -discriminatory procedure for

214 Id., ¶ ¶ 261 -262.
215 Id., ¶ 296.
216 Id., ¶ ¶ 261 -262.
217 If fully open participation is not practicable, all players on the market should
have sufficient representation in the standard -setting process and be informed of
developments.
524 Yale Journal of Law & Technology  [Vol. 23
allocating voting rights;218 and (4) access to the standard is on fa ir,
reasonable and non -discriminatory terms (FRAND).219
In the case of PAI, for example, any standard is likely to
create market power, so these four conditions must be satisfied to
fall outside Article 101. Its standards are voluntary and non -binding,
and therefore its members are free to develop alternative standards
or products that do not comply with the agreed standard. Access to
the standard is also on FRAND terms as they are publicly accessible
on its website. The process includes a n unrestricted public
consultation phase . This means  all affected companies can fairly
participate in the process. However, its standard -setting process is
currently difficult to ascertain. To mitigate the competition law risk,
the process could be more transparent. This is especially important
given i t doe s not have a formal voting or  balloting system. To
enhance transparency, PAI might consider publish ing a clear
standard -setting process guide on the PAI website , for example .
If a standardization  agreement does not clearly comply with
the conditions set o ut above, it should be assessed under Article
101(1), again along the (up to four) relevant markets discussed
above. The aim is to ascertain whether  the standard can limit
differentiation and technical development. Assuming that
companies did  not design the standards to implement an anti -

218 Horizontal Cooperation Gu idelines, supra  note 94, ¶ 281.
219 Id., ¶ 297.
2021 ] Artificial Intelligence & Antitrust  525
competitive cartel , which would likely render it a “by object ”
infringement, the next step is to analyze  the agreement under an
effects -based analysis.220
Under an effects -based analysis, the first question is whether
the s tandard is binding. A standard will be less restrictive of
competition if the members of the standard -setting organization
(SSO) remain free to develop alternative standards and products that
do not comply with the agreed standard. In addition, standards t hat
only cover minor aspects of the end product  are less likely to lead to
competition concerns compared to standards that are more
comprehensive .221 In the present case, the answer will likely depend
on the intended use of the machine learning technology, and the
importance of the privacy -preserving element to that use. However,
PAI’s standards are non -binding and therefore will be less likely to
restrict competition.
The greater the likely market impact of the standard, the
more important it is to ensure equal access to the standard -setting
process in order to reduce competition law risk. A standard
established by an organization  such as PAI with indu stry influence
and participants covering a wide swathe of the market i s likely to

220 A standard agreement that is used to implement a cartel , i.e., as part of a
broader restrictive agreement aimed at excluding competitors will be a ‘by object’
infringement of Article 101(1). This does not  apply to the coordination in
question, which seeks  to enhance consumer benefit by helping to develop privacy -
preserving machine learning. Therefore, this analysis focuses on an effects -based
analysis under Article 101(1).
221 Horizontal Cooperation Guidelin es, supra  note 94, ¶ 293.
526 Yale Journal of Law & Technology  [Vol. 23
have a large market impact. At the same time, it may be impossible
or extremely difficult for participation to be ful ly open to all
competitors and/ or stakeholders on all the  relevant markets. In this
scenario, it may be permissible to restrict participation by ensuring
that the SSO keeps all stakeholders at least  informed and consulted
on the work in progress, even if they do not have a right to vote on
the result.222 PAI’s public consultation process is helpful in this
regard in ensuring wide participation, but it could be clearer on how
these responses are given due regard. PAI also keeps stakeholders
informed on the work in progress through regular blogposts, emails ,
and mee tings, which helps to reduce the competition risk.
A standard -setting agreement should not clearly
discriminate against any of the participating or potential members ,
either in the standard -setting process or the substance of the standard
itself. For exam ple, any standard -setting process that explicitly
exclude s new entrants , or companies active in certain countries,
would be acting in a clearly discriminatory manner. In addition, both
upstream and downstream companies that the standard  affects
should have  an opportunity to participate in the standard . A blanket
ban on either would be difficult to objectively justify. If
manufacturers of soft drinks sought to establish a new standard on
the packaging materials for use  in their drink cans, for example, they

222 Id., ¶ ¶ 295 -296.
2021 ] Artificial Intelligence & Antitrust  527
should also include packaging suppliers in the upstream market,  as
the standard will impact them .223
Another example is if the technical specifications of the
standard audit trail only allow  use of a particular auditing software,
even if it is of recognize d equivalent performance to others. Such a
standard would not satisfy the non -discriminatory condition and
may reduce or prevent innovation because companies who have
invested in an audit software may face large switching costs . There
is also no o bjective justification or efficiency gains given the
equivalence of performance.224
Finally, all competing undertakings should have fair access
to the standard , including any intellectual property rights that they
need to implement the standard. Transparenc y is a key element of
ensuring fair access. If the requirements of the standard are unclear
and imprecise,  or simply not published to those who were not
involved in the standard setting , this will have a foreclosure effect
on third par ties who will find it  difficult or impossible to comply.225
However, where there are several competing standards or there is
effective competition between standardize d and non -standardize d
solutions, a limitation of access may not raise any competition

223 “Upstream ” markets in a supply chain generally refer to material inputs needed
for production, as opposed to “downstream ” markets where goods get produced
and distributed.
224 Horizontal Cooperation Guidelines, supra  note 94, ¶ 325.
225 Id., ¶ 324, by analogy.
528 Yale Journal of Law & Technology  [Vol. 23
risks.226 PAI “best practi ce” standards ar e fairly accessible to the
public because PAI publishes them on its  website.
Exemption under Article 101(3)
There may be some justifiable scenarios where it is not
possible for the standards to compl y with the conditions above:  to
be entire ly open, accessible, and fully participative . Where this is
the case, the standard may be liable to restrict competition in breach
of Article 101(1), especially if one expects it  to have a significant
market impact. Nevertheless, the standard may satisfy t he
exemption under Article 101(3) if it generates countervailing
efficiencies that outweigh the restrictive effects. The following
analysis l ooks at each of the conditions for exemption in turn.
First, under the efficiencies condition, standards that
enhance the comparability of privacy -preserving machine learning
can produce clear efficiency gains. They can enhance competition
between technologies on this front and therefore drive innovation by
allowing companies to compete along agreed parameters. One could
satisfy t his condition by arguing that a privacy -preserving machine
learning standard is beneficial to consumers because it improves the
quality of the technology.
Second, to satisfy the indis pensability condition, any
restrictions of competition arising from the standardization

226 Id., ¶ 294.
2021 ] Artificial Intelligence & Antitrust  529
agreement should not go beyond what is necessary to achieve
efficiencies. Where participation in standard setting  must be limited
to avoid serious inefficiencies in the process, the SSO should put
recognize d procedures in place to ensure there is a collective
representation of interests.227 However, competition law would
generally see a requirement for technical competency as a
reasonable eligibility criterion  for partici pation in standard setting .
The third requirement of Article 101(3) is that the
efficiencies  are passed on to consumers. In our case, it is likely the
standardize d measures and audit trails will facilitate competition
between different technologies . It is therefore likely that  efficiencies
will pass -on to consumers.228
Finally, the standard should not eliminate competition in a
substantial part of the market. If a standard is expected to become a
de facto  industry standard covering a wide  part of the market, it must
not foreclose third parties from effective access to the standard,
thereby eliminating competition. This final condition relates to
transparency —third parties must be able to clearly ascertain what is
required in order to compl y.229

227 Id., ¶ 316. For example, in the Commission decision X/Open Group , the
Commission held that if every company that was willing to commit itself to the
Group objectives had a right to become a member, it would create practical and
logistical di fficulties for the management of work and possibly prevent
appropriate proposals being passed (Case IV/31.458, X/Open Group,
Commission decision of 6 February 1987, 1987 O.J. (L 35) 36, ¶ 45.
228 Horizontal Cooperation Guidelines, , supra  note 94, ¶ ¶ 321-323.
229 Id., ¶ 324.
530 Yale Journal of Law & Technology  [Vol. 23
Conclusion & Mitigating Steps
Generally, the proposal for standardize d measurements for
privacy -preserving ML and standard audit trails may f all outside
Article 101(1) if one can show  they provide scope for significant
pro-competitive benefits. This is s o long as the  setting of the
standard is on a fair and open basis, that the standard is voluntary to
the extent possible, and that access to the standard is also on a
FRAND terms.
Participation in standard setting  can be subject to objectively
justifiable criteria to ensure quality and efficiency (such as technical
competence), but there should not be any unnecessary barriers to
membership to the standard -setting process. To ensure that setting
such a standard through PAI does not raise any compet ition
concerns, PAI could be more transparent abou t its standard -setting
process: especially  on how the process gives public consultation
responses due weight , and the decision -making process given the
lack of a formal voting process. Finally, any exchange  of
competitively sensitive information during the standard setting
process will also be subject to the usual rules on information
exchange.230

230 See supra  Section IV.B  (Information Sharing) .
2021 ] Artificial Intelligence & Antitrust  531
2. Publication and Release of Norms
Key Recommendations
The principles that ap ply to standardization  on audit trails
and comparability measures apply in a similar way to publication
norms. Generally, norms that provide voluntary guidance or expert
advice to help ensure safe and responsible publication will not
breach competition law. However, more extreme norms that
prescribe restricti ng or withholding publication altogether could
give rise to competition law risks, and the potential for abuse.
Analysis
Given the potential harm from some AI systems falling into
the hands of malicious actors, it is important to ensure that any
“dual-use” (potent ially dangerous) research that companies publish ,
and technology t hat they release , is made public in a responsible and
considered way. This is the reason why PAI, for example, is
currently coordinating discussions around publications norms to
avoid such pitfalls.231 Norms could be part of a self -assessment or
voluntary regime, where firms must  satisfy themselves that they
comply before publication, an d where rules act more as industry
guidelines. Norms and guidance  that PAI is currently considering ,
for example, include: (1) a comprehensive guide for researchers to

231 Publication Norms for Responsible AI: Ongoing Initiative , PARTNERSHIP ON
AI, https://www.partnershiponai.org/case -study/publication -norms/ (last
accessed  Aug. 25, 2020).
532 Yale Journal of Law & Technology  [Vol. 23
self-assess their work; (2) access to a pool of experts who can advise
on difficult publ ication decisions and facilitate consideration of
risks; and (3) guidance on how to structure internal review processes
designed to assess potential risks prior to publication.
A more radical —and therefore far less likely to be used —
set of norms could re commend or require the withholding of some
types of potentially dangerous rese arch from the general public:
limiting its publication or withholding publication altogether. This
could be formalised through an industry contractual agreement to be
bound by th e publication norms. A n alternative model would be  an
organization  such as PAI needing  to issue a formal approval or
certification that a publication or release complies with the norms
before a company makes it public .
As a preliminary point, any publicat ion norms could only fall
within Article 101(1) if they are  agreed between competitors, for
example  through a n association such as PAI. On the other hand, a
purely unilater al policy of an undertaking, like  an academic
journal ’s publication policy or NeurIP S’ impact assessment would
likely  not fall within the scope of  Article 101(1).232 The nature of
the norms is also important in assessing the competition law risk.
Publication norms that provide voluntary guidance or expert advice

232 Note that a non -profit can also be an “undertaking ” under Article 101(1), if
certain conditions are satisfied. See supra  Section II.A (Arti cle 101(1)
Prohibition) .
2021 ] Artificial Intelligence & Antitrust  533
should be analyze d in a similar way to privacy -preserving
standards.233 They would usually be consistent with competition law
as long as they are voluntary and effectively compete with a number
of other voluntary standards.
However, risks could arise for norms that potential ly
prohibit the public release of certain research or new  products
altogether, even if one intends those norms to  achieve a legitimate
purpose. This could constitute an agreement between competitors to
limit production or output, which may be an infringeme nt “by
object .” This means that it reveals, by its very nature, a sufficient
degree of harm to competition that an analysis of its effects is not
necessary to conclude that it  breach es Article 101(1).234 Although it
is theoretically possible for a “by objec t” restriction to benefit from
Article 101(3), in practice any exemption argument will be difficult
to bring successfully.
Furthermore, norms that seek to restrict publication of
research could be more liable to abuse. For example, certain
companies could  skew the norms in a direction that would have a
disproportionately restrictive effect on the publication or releases of
a rival AI company or of new entrants or may go beyond what is
necessary to achieve the legitimate purpose of the standards. This

233 See supra  Section IV.C  (Standard -setting) .
234 Case C -67/13 P, Groupement des Cartes Bancai res v Commission, supra  note
51, ¶¶ 49, 57.
534 Yale Journal of Law & Technology  [Vol. 23
might be a particular risk wh ere certain AI companies have undue
influence over the standard -setting process. It might also be a
particular risk because of the potentially fluid and indeterminate line
between what is a “potentially dangerous ” publication and not . An
agreement that was abused in this way would likely be treated by a
competition authority as forming part of a broader restrictive
agreement aimed at excluding competitors and may constitute a “by
object ” infringement of Article 101(1).235 It would also  be difficult
to justify under Article 101(3).
A deliberate abuse of the standard to restrict competition , for
example  to hinder new entrants , is unlikely to benefit from the
exemption under Article 101(3). Outside of an abuse scenario,
publication norms should be assessed under the Article 101(3)
exemption in a similar way to those outlined above236. However,
some types of publication norms , especi ally those that seek to
restrict publication of research may be more challenging to satisfy
compared to privacy standards for a number of reasons.
First, the EC generally sees standards that enhance “quality ”
factors such as quality, safety , and environme ntal aspects as
beneficial to the consumer, and therefore giving  rise to efficiency
gains.237 Publication norms to, for example, prevent research being

235 Horizontal Cooperation Guidelines, supra  note 94, ¶ ¶ 273 -75.
236 See supra  Section IV.C  (Standard -setting) .
237 Horizontal Cooperation Guidelines, supra  note 94, ¶ ¶ 308 -13.
2021 ] Artificial Intelligence & Antitrust  535
used by malicious actors could constitute an increase in safety  or
security , and should therefore satisfy  this condition in principle.
Second, restrictions on release or publication must be indispensable
to the achievement of those efficiencies and go no further. For
extreme norms that prohibit certain product launches or research
publications altogether, one should consider whether there is a less
restrictive alternative that could still achieve the legitimate
objective.238 These alternatives might include, for example,
publishing to some authori zed circles rather than the public as a
whole . This can still allow consumers to benefit from the pro -
competitive benefits of publication of research. Another, less
restrictive  alternative could be publishing in a partly redacted form ,
withholding  the parts of the publication that are most liable to abuse,
but releasing the remainder.
Third, the party seeking to rely on the exemption must
demonstrate that countervailing efficien cy gains that are passed on
to consumers outweigh the restrictions on competition .239 The
challenge here may be to demonstrate that the safety gains from
keeping this research or technology away from malicious actors
sufficiently outweighs the restriction on competition. It may be more
difficult to argue pass -on for these types  of efficiencies compared
to, for example,  standards on the battery life of fire alarms or

238 Id., ¶ ¶ 314 -19.
239 Id., ¶ 321.
536 Yale Journal of Law & Technology  [Vol. 23
emissions from cars. Arguably, the benefits of the latter cases are
more direct,  tangible, testable and measurable .240 Fourth, if the norm
is expected to become a de facto  industry standard covering a wide
part of the market, it must not foreclose third parties from effective
access to the standard, thereby eliminating competition. In other
words, third parties must be able to clearly ascertain what is required
in order to comply.241
Conclusion & Mitigating Steps
The principles that apply to standardization  on audit tra ils
and comparability measures  apply in a similar way to publication
norms. Norms that provide voluntary guidance or expert advice to
help ensure safe and responsible publication will not usually breach
competition law. However, more prohibitive norms that prescribe
restricting  or withholding publication altogether could gi ve rise to
competition law risks. These more extreme norms could be a “by
object ” infringement of Article 101(1) to the extent that they restrict
publications or releases altogether. Furthermore, it could be
challenging to rely on the Article 101(3) exempt ion. First, it may be
difficult to demonstrate that there are consumer efficiencies that
outweigh the restrictive effects given the difficulty in quantifying
the “benefit ” from the norms to the required evidential level.

240 This is similar to the challenge with justifying the Windfall Clau se or Assist
Clause, discussed supra  in Section s III.A (OpenAI Assist Clause) and III.B  (The
Windfall Clause).
241 Horizontal Cooperation Guidelines, supra  note 94, ¶ 324.
2021 ] Artificial Intelligence & Antitrust  537
Second, the “indispensability ” elem ent of the Article 101(3)
exemption is particularly important here. The salient question is
whether there is a less restrictive alternative that can still achieve the
legitimate objective , such as publishing in redacted form or to a
trusted circle, rather than withholding publication altogether. To
mitigate competition law risks, the least restrictive alternative
should always be the preferred avenue in the first instance.
Another risk from these extreme norms is the possibility that
companies could use th em to deliberately dampen competition. An
important safeguard is to ensure that a fair and open system of
participation exists and that no one company has undue influence
throughout the standard -setting process . For these reasons, more
voluntary forms of guidance may be advisable as lower risk
alternatives to these more extreme norms.
CONCLUSION
In this paper, we discussed how the competition law
prohibition on restrictive agreements under Article 101(1) might
apply  to forms of cooperation between AI compa nies, advised on
the potent ial level of risk and suggested  practical steps to mitigate
any risks. AI cooperation seeks to ensure that AI is developed and
deployed safely, securely , and beneficially. Forms of AI cooperation
range from AI bias auditing and s etting safety benchmarks, to those
that seek to address longer -term risks  of catastrophic accidents or
538 Yale Journal of Law & Technology  [Vol. 23
power concentration that may arise if and when we n ear the
development of TAI , like the Assist Clause and Windfall Clause.
Whilst the goals of AI cooper ation are crucial and legitimate,
cooperation between competing AI companies can risk infringing
Article 101(1), for example,  where they involve an agreement or
understanding to restrict competition between AI companies or the
exchange of commercially sens itive information. The scope of
Article 101(1) is broad, ranging from binding agreements to
scenarios where an undertaking “knowingly substitute practical
cooperation between them for the risks of competition .”242 An
agreement can breach Article 101(1) both  where it is capable of
restricting competition and where it actually does so. The
prohibition may apply  as soon as undertakings enter into an
agreement, without any need for them to (ever) implement the
agreement .
However, this Article  argued  that some r elatively simple and
“low-cost” mitigating steps  that do not sacrifice the objectives of the
AI cooperation strategies could  significantly help to reduce
competition law risks. These mitigating steps may restructur e the
agreement so that it is not  capable of producing anti-competitive
effects or by ensuring that an AI company acts unilaterally rather
than cooperatively whilst  retaining the gains from an AI governance

242 Case C-48/69, Imperial Chemical Industries v European Commission
(Dyestuffs) , 1972 E.C.R. 619.
2021 ] Artificial Intelligence & Antitrust  539
perspective. These  may help to structure cooperation outside of the
Article 101(1) prohibit ion altogether.
Even if one cannot easily restructure the cooperation  to fall
outside Article 101(1), mitigating steps might allow the cooperation
to benefit from the exemption under Article 101(3), if one can
demonstrate countervailing efficiencies and t he agreement is
necessary to achieve them. In relation to responsible AI
development , the AI company seeking to rely on the exemption
would need to demonstrate efficiency gains from the development
of an improved AI system , for example,  one that has been more
rigorously tested for ethics or safety , to the benefit of consumers .
There will be scenarios where one cannot  so easily
restructure cooperation strategies to comply with competition law .
This presents a more difficult trade -off between gains in respon sible
AI development  and the risk of breaching competition law. One
example may be implementation of the Assist Clause at a relatively
late stage in TAI development where its implementation may lead
to a significant reduction in competition. In these insta nces, we
would recommend seeking specialist competition law advice as the
level of risk in each case would  depend on several specific factors.
These include  the market position of the parties, how one defines
the relevant market, the level of competition i n the rest of the
540 Yale Journal of Law & Technology  [Vol. 23
market, and the strength of evidence of any countervailing
efficiencies for the purposes of an Article 101(3) defense.
Overall, EU competition law should be an important
consideration for all AI companies and AI governance researchers
when planning AI cooperation strategies. Substantively, AI
governance and competition law share similar goals : to prevent
concentrations of power, to encourage innovation, and to promote
societal and consumer welfare. Prudentially, t he EC ’s current
regulatory focus on the tech sector, as well as its strategic focus on
AI and Big Tech ’s activities in AI development, are likely to
continue for the foreseeable future. This makes it more likely that
the EC will bring competition enforcement aga inst AI companies.
Sanctions can be wide -ranging and significant, as well as expending
significant company resources in defending investigations that can
go on for many years.
The stakes  are high on both sides. C ompetition law is
important and powerful;  AI cooperation seek s to ave rt potentially
catastrophic harms . However , EU competition law should not be an
insurmountable obstacle to AI cooperation strategies that
legitimately seek to enhance safe and responsible AI development
and deployment . This paper advocates for AI companies to build a
good awareness of the key principles applicable to AI cooperation,
to maintain ongoing diligence to ensure they are structuring forms
2021 ] Artificial Intelligence & Antitrust  541
of AI cooperation in ways that are  consistent with competition law,
and to seek spec ialist competition law advice where relevant . These
steps can significantly help reconcile the tensions between
cooperative AI strategies and EU competition law.

2021]  Artificial Intelligence & Antitrust  App’x .
Appendix 1: Table of Implementation  Scenarios for the Assist Clause
Implementation Method  Potential Risks
Full Acquisition: Competitor is fully acquired by Leader   This acquisition will likely be subject to merger review, assuming turnover
thresholds are met.
Partial acquisition: A part of the competitor that is
developing TAI -relevant technology (e.g. safety, language
models, reinforcement learning environments) is acquired
by the Leader, the rest of the competit or continues to
operate on the market   The principles in (1 ) above also apply here.

However, may be lower risk than full acquisition to the extent there are fewer
‘overlaps’ in a partial acquisition i.e. relevant markets where the competitor and
Leader ov erlap.

This should reduce the level of antitrust scrutiny and therefore reduce the
likelihood that the merger will be blocked/ subject to remedies.
Joint venture (“JV”) with Leader:  parties enter into  a
joint commercial enterprise, but otherwise parties retain
their distinct identities  The competition law analysis would depend on whether the JV would be a ‘full -
function joint venture’ (FFJV). A FFJV would be notifiable under EUMR,
whereas a non -FFJV wo uld not be notifiable but would be assessed under Article
101. It is preferable for a JV not to be notifiable under the EU merger regime,
because it will be subject to a higher level of scrutiny compared to under Article
101(1).

As discussed above, it is advisable to structure the JV to avoid being caught
under EUMR. This could be done by ensuring that the JV only takes over one
specific function of its parents' activities, such as R&D, which would mean the
second condition for a FFJV is not satisfied. Or it could be made clear that the JV
is only intended to be established for a short, finite period and not meant to be
long-lasting (fourth condition).

However, a non -full function JV would still need to be assessed under Article
101(1).
Partial joint v enture with Leader:  i.e. only contributing
the part of the competitor that is developing TAI -relevant
technology   The legal principles set out in row (3) above also apply here.

In practice, a partial JV may be less likely to qualify as a FFJV that is not ifiable
under EUMR if e.g. the JV is only taking over one specific function of the parent,
or does not have its own dedicated resources. However, this will depend on what
resources/functions the Leader contributes to the JV, as the conditions must be
satisfied by considering the JV as a whole.

Even if a partial JV is notifiable, there are potentially fewer ‘overlaps’ compared
to a ‘full’ JV i.e.  relevant markets where the competitor and Leader overlap. This
will reduce the level of antitrust scrutiny and therefore reduce the likelihood that
the merger will be blocked.

If a partial JV is not notifiable, it would still need to be assessed under Article
101(1).
“Unilateral ” R&D slow -down: competitor unilaterally
slows down its R&D on TAI   If the competitor can establish that it is slowing down its R&D on a genuinely
unilateral basis, then it is likely to fall outside of the Article 101(1) prohibi tion.

However, the R&D slow -down could constitute a form of cooperation subject to
Article 101(1) if it implements a collaborative Assist Clause. In this scenario, the
App’x . Yale Journal of Law & Technology  [Vol. 23
slow -down could be seen as part of a single and continuous infringement flowing
from t he Assist Clause itself.

In a scenario where the competitor acts under a unilateral Assist Clause, the
slow -down could still breach Article 101(1) if it involves any agreement,
understanding or exchange of information at the point of implementation (e.g.
any coordination with the Leader as to the timing or method of the slow -down).
R&D shift: competitor shifts R&D to a particular topic
(e.g. safety or applied/ product development)  If the shift in R&D is truly unilateral, then it would fall outside of the Article
101(1) prohibition.

However, this R&D shift may be a breach of Article 101(1) if it implements a
collaborative Assist Clause, wherein the R&D shift and the Assist Clause would
form part of a ‘single and continuous’ infringement. The potential  overall
infringement could be seen as an agreement between parties that are part of the
pattern of collaborative Assist Clauses to restrict competition in the market for
TAI development by shifting TAI R&D development.

However, it may be possible to rely  on the Article 101(3) exception if the parties
can demonstrate that the switch to safety R&D generates efficiencies that will
benefit consumers, and those efficiencies outweigh the restriction on TAI
development competition.

Note also that the competiti on analysis is per product market, therefore the
countervailing efficiencies must be in relation to TAI, and not any other product,
in order to fulfil the conditions under Article 101(3).

In a scenario where the competitor acts under a unilateral Assist C lause, the shift
could still breach Article 101(1) if it involves any agreement, understanding or
exchange of information at the point of implementation (e.g. any cooperation
with the Leader as to the timing or nature of the shift).
Competitor stops TAI R&D, and shifts to applied AI
development (e.g. a product development)  See (6).
Staffing changes:  Competitor puts many employees on
gardening leave or fires them.  If the competitor can establish that it is really acting unilaterally , then this is
likely to fall outside the Article 101(1) prohibition.

In practice, it may be difficult to show that putting many employees on gardening
leave/ firing them, which effectively restricts the competitor’s R&D efforts and
potential output, is n ot part of implementing a collaborative Assist Clause, i.e. an
agreement or understanding between competitors. If so, the Assist Clause and
staffing changes would form part of a single and continuous infringement.

The difference between this option and the R&D switch is that this is less likely
to satisfy Article 101(3) - there is no direct countervailing benefit from alternative
forms of R&D. We would need to rely on the more indirect benefits from
enhanced safety development - a more challenging argument  to bring for reasons
discussed above.

In a scenario where the competitor acts under a unilateral Assist Clause, the shift
could still breach Article 101(1) if it involves any agreement, understanding or
exchange of information at the point of implementation (e.g. any cooperation
with the Leader as to the timing or nature of the staffing changes).
2021]  Artificial Intelligence & Antitrust  App’x .
Employee resignations: competitor’s employees resign or
are fired  If the competitor’s employees are fired, the same analysis for scenario (6) and (7)
above apply i.e. the Assist Clause has the effect of restricting competition in TAI
development by prompting/obliging the competitor to take this action. If it can be
shown to be truly unilateral, then no issues arise.

As an initial point, although only undertakings can be bound by competition law,
an undertaking will have strict liability for any actions of their employees under
competition law, even if the employer has no knowledge of these actions or if the
employee is acting contrary to the employer’s instructions.

If the competitor’s employees resign, and it can be shown that they took this
decision independently and without any influence or pressure from the
competitor company, this may be lawful because it is a unilateral conduct,
applying the same principles as discussed above.

However, it is possible that the employees were influenced by the Assist Clause,
even if they are not strictly bound by it. Therefore, it is arguable that the
resignations are an implementation of the Assist  Clause. In this case, the Assist
Clause could be seen as an agreement that restricts competition, albeit indirectly,
(by influencing the employees to resign in order to curb competition in TAI
development). The Clause, as well as its subsequent implementa tion via the
employee resignations, could therefore   be treated as part of a single and
continuous infringement.
Competitor’s employees resign or are fired, and are hired
by Leader  The competition law risk here will depend on what basis the firing and hiring /
resigning and hiring occur, and whether these acts are truly unilateral (which
would fall outside Article 101(1), or whether they are in fact implementing the
Assist Clause.

If this conduct is subject to an agreement or understanding between the
competitor and Leader that the competitor’s employees will be fired and the
Leader will hire them, that could be an implementation of the Assist Clause that
breaches Article 101(1). Here, the Clause and its subsequent implementation
constitutes a single an d continuous infringement that indirectly restricts
competition (between the competitor and Leader in TAI development as a result
of transfer of employees).

In a scenario where the competitor acts under a unilateral Assist Clause, the
hiring/firing could  still breach Article 101(1) if it involves any agreement,
understanding or exchange of information at the point of implementation (e.g.
any cooperation with the Leader with respect to the hiring/firing).
Some employees (e.g. safety, language models, or
reinforcement learning environments teams) resign or are
fired, and are hired by leader  Principles from (10) will apply similarly here. See row above.
Competitor provides compute cheaply or free to leader   In this scenario, the competitor is providing the Leader with an input that is the
main cost of AI R&D. This conduct may give rise to competition risks where the
provision to the Leader is not unilateral, and it means that the competitor can no
longer compete with it, or needs to lower outpu t, reduce its own innovation, etc.
For example, if the competitor never reached critical mass to be able to viably
compete with the Leader, and would have dropped out of the market anyway,
then this grant of compute would not have led to any appreciable re duction in
competition that the competitor would otherwise be able to exert.

App’x . Yale Journal of Law & Technology  [Vol. 23
However, just conferring an advantage to a competitor is not a breach of
competition law per se. It depends overall on the effects on competition as a
result of the provision on both parties. If the agreement to provide the compute is
capable/intended to/ does indeed hamper the ability of the competitor to compete
with the Leader, that would give rise to competition law breach (per the
reasoning in (5)).   If the grant of compute c an be justified e.g. because the
competitor wishes to switch to another area of AI R&D that requires less
compute, and still brings countervailing benefits in e.g. AI safety, that may be
compliant under similar reasoning to the R&D ‘shift’ in (6).

Further , if the grant allows the Leader to gain a critical scale in input that speeds
up or improves the quality of its AI R&D, that could be seen as a countervailing
benefit that justifies the conduct under Article 101(3) as long as it outweighs the
restriction on competition that the competitor is able to exert and does not go
beyond what is necessary.
Competitor provides research/ software cheaply or free to
leader   Similar principles to above.

2021]  Artificial Intelligence & Antitrust  App’x .
Appendix 2: Cooperation Strategies & Antitrust Analysis
Strategy  Description  Antitrust Concern s Mitigating Steps Discussion
Assist Clause
 A centr al risk from TAI
development is race dynamics
leading to corner - cutting  on
responsible development . To
address this, OpenAI proposed the
‘Assist Clause’ in its Charter,
which commits it to stop competing
and start assisting any ‘value -
aligned’ company tha t gets close to
developing AGI.  Entering into the Assist
Clause could be an
agreement to restrict
competition;
implementing the Assist
Clause could also
separately restrict
competition.  Companies should not  seek to agree
with, persuade or  influence their
competitors to also adopt Assist
Clause; implement assistance under
the Clause in ways that are efficiency -
enhancing, do not reduce overall
output or innovation.  Section III.A
Windfall Clause  An agreement entered into  by an AI
company to redistribute a part of its
wealth if and when its earnings
exceed a certain threshold.  ‘Agreement to agree’
between AI companies to
enter into Windfall Clause
could raise risks to extent
Windfall Clause
disincentivizes
competition bet ween
companies, restricts sales
or output etc.  Avoid entering into an ‘agreement to
agree’ to the Windfall Clause ;
structure Windfall Clause to minimise
disincentive effect.  Section III.B
Agreements on
secure enclave
 A set of software and hardware
features that together provide an
isolated execution environment that
enables a set of strong guarantees
for applications running inside the
enclave.  Competing AI companies
that enter into an
agreement on secure
enclaves cou ld constitute
a horizontal agreement on
standards that could
exclude competitors. See
(12) on standardize d
benchmarks.  See (12) on standardize d benchmarks.  Section IV.A
Mutual
monitoring
 AI companies  could monitor or
audit the responsible  AI
development of other AI
companies.  Risks arise if competitors
exchange  commercially
sensitive information
between themselves . Use a third party to carry out audits
instead of a competing AI company;
minimise the commercially sensitive
information exchange to only what is
strictly necessary for the immediate
purpose.  Section
IV.B.1
Red-teaming
 To enhance AI safety, 'white hat'
hackers try to uncover
vulnerabilities in systems and
organisations and then tell the
people that run them.  See (4)  on mutual
monitoring.  See (4)  on mutual monitoring.  Section
IV.B.2
Incident sharing  AI companies share information
about what has gone wrong with
safety testing.  See (4)  on mutual
monitoring.  See (4)  on mutual monitoring.  Section
IV.B.2

Compute
accounting  An AI lab should comprehensively
account for the computing power See (4)  on mutual
monitoring.  See (4)  on mutual monitoring.  Section
IV.B.2
App’x . Yale Journal of Law & Technology  [Vol. 23
used in a major AI project, and
share lessons learned on the
challenges of precision and
comparability across projects.
Communication
and ‘heads -up’ Greater communication between
competitors could include regular
updates, clear communication
channels, and joint events such as
workshops, retreats, conferences,
residencies, country visits.

Separately, pre -publication or pre -
release ‘heads -up’ to competitors
about R&D or products are
intended to give competitors an
opportunity to point out legitimate
reasons  why the R&D product
should be stalled e.g. because of
outstanding safety concerns, or
because it can be exploited by
malicious actors.  See (4)  on mutual
monitoring. Risks also
arise to the extent the
‘heads -up’ gives
competitors the ability to
restrict  or delay output.  See (4)  on mutual monitoring .
Moreover,  do not give competitors
who are receiving the  ‘heads -up’ the
ability to restrict or delay output.  Section
IV.B.3
Seconding staff  Seconding engineers or researchers
from one AI lab to another to help
trust building.  See (4) on mutual
monitoring.  See (4) on mutual monitoring.  Section
IV.B.4
Third -party
auditing  Use a third -party  trade association
or safety standards body to carry
out independent, third party
auditing of AI systems.
 Do not use  the third party
as an intermediary for
competing firms to
exchange the same
commercially sensitive
information, just
indirectly.  Third -party audit should be on an
independent basis, without
disseminating any commercially
sensitive information betw een
competitors.  Section
IV.B.5
Bias and safety
bounties  Organizations commit to giving
individuals recognition or
compensation for recognizing and
reporting bugs, especially those
related to exploits and
vulnerabilities. Organisations could
establish a similar system  for bias
and safety problems around AI
syste ms. See (4) on mutual
monitoring.  Risks if the
bounty hunter discloses
confidential information
about its technology or
R&D in reporting how its
systems exposed the
vulnerability.  See (4) on mutual monitoring.  Section
IV.B.6
Standardi zed
benchmarks  These  could include measures of
performance against collaboratively
established standards. This could be
useful for  fairness, safety,
explainability, or robustness to
adversarial inputs.  Risks where the standard
has the potential to
exclude competitors from
the standard -setting
process or from access to
the standard  once set.  Standard -setting process and access to
the final standard should be on fair,
reasonable and non -discriminatory
(FRA ND) terms.

 Section
IV.C.1
Standardi zed
audit trails  Standardize d audit trails are a
subset of standardized benchmarks.
They constitute a traceable log of See (12)  on standardize d
benchmarks.  See (12) on standardize d benchmarks.  Section
IV.C.1
2021]  Artificial Intelligence & Antitrust  App’x .
steps in system operation, and
potentially also in design and
testing. This helps to ensure that
there is accountability for
subsequent claims about safety -
critical AI system’s properties and
impacts.
Publication and
release norms  Given the potential harm from AI
systems falling into the hands of
malicious actors, it is important to
ensure that any ‘dual -use’
(potentially d angerous) research
that companies publish , and
technology they release , is made
public in a responsible and
considered wa y. See (12) on standardize d
benchmarks. Additional
risks where more extreme
norms prescribe the
restriction or withholding
publication altogether,
which could constitute an
anti-competitive
agreement.  See (12) on standardize d benchmarks.
Opt for more volun tary forms of
guidance or expert advice; and avoid
more extreme norms that restrict
publication altogether.  Section
IV.C.2
App’x . Yale Journal of Law & Technology  [Vol. 23
Appendix 3: Forms of Mutual Monitoring
Strategy  Competition Law Risk Mitigation
(1) Shared red teaming

AI developers should
conduct “red -teaming”
(adversarial attacks)
exercises. They are
“white hat ” hackers who
try to uncover
vulnerabilities in
systems and
organisations and then
tell the people that run
them.  The level of risk will depend on whether the red teams exchange any
commercially sensitive information. The lowest risk approach is for the attacks
to avoid sharing any co mmercially sensitive or non -public information.

However, it may be necessary to share certain commercially sensitive
information to achieve the safety -enhancing purpose of the red teaming . For
example, the ‘hackers’ may wish to share technical information  about their AI
systems with the company that is subject to the adversarial attacks. This may be
necessary to inform the subject company of the type of technology that was
capable of exposing the vulnerabilities.

Risks may be manageable if mitigating ste ps are taken . Minimi ze information
exchanged to what is necessary
to achieve the purpose.

Add safeguards to ensure  staff
only disclose  the information
within a ‘clean  team’ if possible
and only use it  for the
designated purpose.

One can further mitigate r isks if
a third party and not competing
AI labs carry out red teaming
(unless there are clear
justifications , e.g. third party
has the technological acumen to
carry out successful ‘hacks’).

However, one can reduce risks
to a manageable level just based
on steps 1 and 2 above.

(2) Incident sharing

AI developers should
participate in “incident
sharing” more often,
including through
multilateral channels
(e.g. PAI).   If ‘incident sharing’ is publicly available, this should not give rise to any
competition law risk.

If incident sharing will only be carried out e.g. within the confines of a trade
association such as P AI, it will be relevant to consider whether it would involve
sharing any strategically useful information with other companies.

If incident sha ring only involves sharing past ( historic ) information about what
has gone wrong with safety testing, that information would only be strategically
significant if it nonetheless provides an insight into the future developme nt of
the company’s technology or  R&D, e.g. by prov iding insight into its so ftware or
algorithms. If it does not provide such an insight, its disclosure is unlikely to
raise competition law risk.

However, if the incident sharing does provide a strategic insight, Article 101(1)
potentially prohibits it . For example, it might be st rategically significant in
nature because it gives insight into R&D and essential technology unless
information is historic  or publicly available . However,  incident sharing is
probably only worthwhile if the incidents are sufficiently recent that they can be
useful for parties to feed into their current safety development.

To ensure one can rely on the Article 101(3) exemption, the commercially
significant information should be the minimum necessary to achieve the
legitimate purpose of enhancing safety te sting. However, it is arguably an
efficiency -enhancing measure (condition 1 of Article 101(3)) that allows
competitors to benchmark against safety standards in the market.
 Incident sharing should be
released to the public domain if
possible.

If non -public, s hare only what is
strictly necessary to achieve the
legitimate objective.

Share in an aggregated and/or
anonymised form if possible
through a third party such as
PAI.
2021]  Artificial Intelligence & Antitrust  App’x .
A multilateral channel such as PAI could also collate the incident sharing .
However , using PAI as a sharing mechanism will only reduce the competition
law risk if they collect the individual incident data and share it with PAI
members on an aggregated and anonymised form. Further, if having access to
this incident sharing confers a mater ial competitive advantage to members of the
PAI, its membership criterion should be sufficiently open to all industry
participants in order to comply with Article 101.

(3) Compute
accounting

An AI lab should
comprehensively
account for the
computing power used
in a major AI project,
and share lessons
learned on the
challenges of precision
and comparability
across projects.   Sharing compute data is likely to be commercially sensitive information because
it is an essential input cost of an AI lab. If this is historic information, this is
likely to be commercially sensitive only to the extent that even such historic
information on compute power could provide an insight into the AI lab’s future
strategic direction.

If this historic compute info rmation does provide such insight, then exchanging
such information between competing AI companies could breach Article 101(1).
As above, consider instead using less anticompetitive options to achieve the
legitimate objective of enhancing safety. For examp le, consider using a third
party e.g. PAI to aggregate and anonymous computer information and then share
that ‘ sanitize d’ information with the AI companies. If having access to this
compute power information confers a material competitive advantage to
memb ers of the PAI, its membership criterion should be sufficiently open to all
industry participants in order to comply with Article 101.

If it is not possible to achieve the legitimate safety objective by this method e.g.
because information must be indivi dualised to be meaningful, then the question
in each case should be ‘is this information running the minimum competition
law risk possible to still achieve the legitimate objective’?

As to sharing the lessons learned about precision and comparability acr oss
projects, this could  arguably be objective/ technical information that has a pro -
competitive purpose in allowing companies to benchmark against each other. It
seems unlikely to have the object or effect of restricting competition.  See (2).