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Artificial intelligence and competition
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Table of Contents

Table of Contents
Purpose and overview ........................................................................................................... 4
Section 1: Introduction to artificial intelligence ........................................................................ 5
1.1 Exploring definitions for artificial intelligence ................................................................ 5
1.2 Breaking down artificial intelligence ............................................................................. 6
Section 2: Markets for artificial intelligence ............................................................................ 8
2.1 Vertical relationships .................................................................................................... 9
2.2 AI infrastructure ........................................................................................................... 9
2.3 AI Development ......................................................................................................... 12
2.4 AI Deployment ........................................................................................................... 13
Section 3: AI and competition .............................................................................................. 14
3.1 AI and mergers and monopolistic practices................................................................ 14
3.2 AI and cartels ............................................................................................................. 20
3.3 AI and deceptive marketing practices ........................................................................ 21
3.4 AI and competition promotion .................................................................................... 22
Conclusion ........................................................................................................................... 25
Glossary .............................................................................................................................. 26

3

Purpose and overview
Artificial intelligence (AI) is affecting many sectors of the economy, in a time of rapid evolution.
The importance of governments and policymakers in comprehending and addressing theimplications of AI is clear. The Competition Bureau (the “Bureau”) conducts ongoing work tobetter understand AI, how it might affect competition, and how we can be prepared for
addressing potential competitive harm from AI, as well as promoting competition in AI markets.
The Bureau engages in cross-governmental collaboration on this front, engaging with the Officeof the Privacy Commissioner (OPC) and the Canadian Radio-Television andTelecommunications Commission (CRTC) through the Canadian Digital Regulators Forum toaddress the implications of emerging technologies, including AI, as they intersect with ourrespective mandates.
This paper explores several considerations for how AI may affect competition, in the context ofCanadian competition law. It is not meant to predict any outcomes or provide any
recommendations. Rather, the aim is to foster thoughtful and informed dialogue which can help
the Bureau in deepening its understanding of how competition is developing in AI markets, andhow the Bureau can enforce and promote competition in these markets.
Section one introduces the concept of AI, exploring proposed definitions for AI and breaking
down AI technologies.
Section two discusses markets involved in the production of an AI product or service.
Section three outlines several considerations for how AI may affect competition. Competition
impacts are explored in the context of the Bureau’s enforcement areas, in particular Mergers
and Monopolistic Practices, Cartels, and Deceptive Marketing Practices. Section three also
provides an overview of how the Bureau can work with policy makers to promote competition in
AI markets.

4Section 1: Introduction to artificial intelligence
1.1 Exploring definitions for artificial intelligence
AI as a theory and empirical reality has been around for quite some time. The field of AI gained
momentum in the 1950s and focused on adapting tasks such as problem solving, reasoning,and even game playing to be performed by computers. The term was first coined in 1956 byJohn McCarthy as "the science and engineering of making intelligent machines". 1 Since then,alternative definitions have been proposed within academia, industry, and government but nocommon consensus has yet been reached. 2 Despite the advancements and novel technologies
that have emerged recently, there is still no universal definition for AI. One difficulty in coming toan agreement on an acceptable common definition for AI is a lack of understanding andconsensus on human intelligence.
AI has often been defined and interpreted from different perspectives, which contributes to thelack of consensus in its definitions. There is the academic perspective where AI is seen as adiscipline or branch of computer science. The advancement of technology-driven businessmodels and service delivery has seen an industrial perspective of AI as tools and applications.
More recently, certain organizations and governments have focused efforts on establishing adefinition for an AI system from broader adoption and governance perspectives. 3 For instance,
the Artificial intelligence and Data Act, a component of Canada’s Bill C-27, defines AI as "atechnological system that, autonomously or partly autonomously, processes data related to

1 John McCarthy is an emeritus Stanford professor and he is considered a pioneer in the field of Artificial intelligence.
2 For example, the Canadian Centre for Cyber Security defines Artificial intelligence as “a subfield of computerscience that develops intelligent computer programs to behave in a way that would be considered intelligent if
observed in a human (e.g. solve problems, learn from experience, understand language, interpret visual scenes)”.
The June 2023 draft of the European Union Artificial intelligence defines Artificial intelligence as a set of techniques
and approaches: “(a) Machine learning approaches including supervised, unsupervised, and reinforcement learning,
using a wide variety of methods including deep learning; (b) Logic- and knowledge-based approaches including
knowledge representation, inductive (logic) programming, knowledge bases, inferences and deductive engines,&symbolic) reasoning and expert systems; and (c) Statistical approaches, Bayesian estimation, search andoptimization models.”
3 The June 2023 draft of the European Union Artificial intelligence Act defines an AI system as “software that isdeveloped with [specific] techniques and approaches and can, for a given set of human-defined objectives, generate
outputs such as content, predictions, recommendations, or decisions influencing the environments they interact with.
The OECD defines an AI system as “a machine-based system that, for explicit or implicit objectives, infers, from the
input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can
influence virtual environments.”5human activities through the use of a genetic algorithm, a neural network, machine learning, oranother technique in order to generate content or to make decisions, recommendations or
predictions”. The governance perspective is essential for a working definition to discuss theimpacts of AI in competition and marketplace dynamics.
The impact of AI on competition and marketplace dynamics is a complex landscape. On one
hand, AI can enhance competition by fostering innovation, lowering barriers to entry, andimproving efficiency. On the other hand, there are concerns about the concentration of AI
capabilities in a few dominant companies. This could potentially lead to anticompetitive
behaviour and increased prices, reduced choice, or lower quality of options for AI users. While
there is no commonly accepted definition for AI, we must understand and evaluate its impact oncompetition. Our ability to do so is crucial for protecting competition and ensuring businesses
and consumers in Canada can benefit from competition across all sectors – including AI. We
can do this by considering the specific AI technologies and the markets, products, and services
where Canadian consumers and businesses come across them.

1.2 Breaking down artificial intelligence
To better understand artificial intelligence, and where it is appearing in Canadian markets, it is
helpful to understand the different technologies that make up AI. For consumers, AI can bring
benefits such as personalized services, improved customer experience, and access to new andinnovative products. The following list provides an overview of some of the technologies which
may be a component of an AI product or service. These can affect consumers, businesses,
and communities.

Types of AI technologies:
1. Machine learning (ML): techniques that allow machines to learn from data
independently, without human instruction. In this sense, machines, such as computers,
can learn from data to improve performance without any explicit programming.
a. Supervised learning: ML trained with labelled data sets and used to classify dataor predict outcomes.
b. Unsupervised learning: ML trained with unlabelled data sets and used to findhidden patterns or data groupings.

6

i.

Self-supervised learning: a type of unsupervised learning which learns tocreate labels for unlabelled data.

c. Semi-supervised learning: ML that aims to label unlabelled data points using
information learned from a small number of labelled data points.
d. Reinforcement: ML where model components, called agents, learn through trial
and error with the goal of maximizing a total reward.
2. Deep learning: machine learning using neural networks.
a. Neural networks: networks modeled on the human brain which consist ofinterconnected nodes and organized in layers.
3. Natural language processing (NLP): techniques that give machines the ability tounderstand and generate human language.
a. Large language models (LLMs): use a combination of NLP and deep learningtechniques. LLMs are trained on substantial amounts of data and use learned
patterns to identify the next element in a sequence (for example the next word ina sentence) based on a given input (for example a text prompt).
4. Computer vision: techniques which give machines the ability to derive information fromdigital images, videos, or other visual inputs, making recommendations or taking actions,or both, based on the visual information analyzed.
5. Generative AI: NLP and machine learning techniques used to generate new content,such as text, images, videos, code, music, and synthetic data, using patterns learned
from training data.
a. Foundation models: AI technology trained on substantial amounts of data andcan be adapted to a wide range of tasks. Most popular foundation models today
use generative AI, and some practitioners use these terms interchangeably.
However, some research has pointed to discriminative AI, which may fall under
the definition for foundation models. 4
6. Discriminative AI: ML techniques that distinguish between types of data and used forclassification and prediction tasks.

4 Rishi Bonmmasani et al. "On the opportunities and risks of foundation models." July 2021. Junaid Ali et al.

"Evaluating the fairness of discriminative foundation models in computer vision." AAAI/ACM Conference on AI,
Ethics, and Society. August 2023.

7

7. Robotics: the programming of machines to carry out a series of actions independently or
semi-independently. Robotics or robots are not AI on their own. AI robots is a robot is
controlled by an AI system.
8. Knowledge-based systems: computer systems that use a knowledge base to solve
complex problems or make decisions.
a. Expert systems: computer systems that use a knowledge base of humanexpertise to solve problems or to make decisions in a particular area ofknowledge.

SECTION 1: QUESTIONS FOR DISCUSSION••

Are there any additional technologies, not included in the above list, that the
Bureau should consider to advance its understanding of Artificial intelligence?
Are any of the identified technologies particularly significant to the AI sector in
Canada?

Section 2: Markets for artificial intelligence
Production of AI products and services may involve various markets. The creation of an AI
product or service may involve participation from:i.

Markets for AI infrastructure: the supply of compute (for instance, processing power,
memory, storage and other resources required for the computational success of any
program) and data required for the development of AI,

ii.

Markets for AI development: the supply of AI technologies such as AI models,algorithms, and architectures, andiii.

Markets for AI deployment: the supply of customer facing AI products or services.

8

2.1 Vertical relationships
Vertical relationships 5 play a significant role in AI markets. Firms in AI development markets relyon access to expertise, data, and compute 6 to design and train AI technologies. Firms in AI
deployment markets rely on access to AI technologies to create new AI products or services or
integrate an AI component to an existing product or service. Firms may be vertically integrated,
in which a single firm operates in multiple streams of production of an AI product or service. If
firms are not vertically integrated, their access to AI inputs will rely on business arrangementswith external firms. 7 Access to AI inputs, through vertical integration or other businessarrangement, is vital for participation in AI markets.

2.2 AI infrastructure
AI infrastructure provides a foundation for AI development with compute and data.

2.2.1 Compute
A trend in AI development is the requirement for increasing amounts of computational power. 8
This vast computational power is vital input for AI training 9 and inference 10. Cloud computing
infrastructure and specialized computer chips, often referred to as AI chips, are particularly
important inputs in AI production. 11

5 Business relationships between buyers and sellers operating at various stages of a production process.
6 Compute, in the context of AI, refers to the computational resources required to develop AI technologies.
7 Business arrangements can take many forms including but not limited to a one-time purchase, ongoing purchases(sometimes organized trough contracts), or partnerships.
8 Dario Amodei et al. "AI and Compute." OpenAI. May 2018; Jai Vipra and Sarah Myers West. "Computational Power

and AI." AI Now Institute. September 2023; Nestor Maslej et al. "The AI Index 2023 Annual Report." AI Index Steering
Committee, Institute for Human-Centered AI, Stanford University. April 2023.
9 Training in AI refers to the process of teaching an AI technology or system to learn from data and perform its

intended task(s).
10 Inference in AI refers to the process of an AI system providing an output
11 Competition and Markets Authority. "AI Foundation Models: Initial Report." September 2023; Jai Vipra and Sarah

Myers West. "Computational Power and AI." AI Now Institute. September 2023.

9

2.2.2 AI chips
AI chips are specialized computer chips 12 used to train and operate AI technologies. Currently,
the most popular computing chips for AI development, especially for training, are Graphics
Processing Units (GPUs). GPUs offer significantly improved AI-related efficiency compared to
traditional computing chips like Central Processing Units (CPUs), notably due to their ability toperform calculations in parallel (i.e., at the same time). Other AI chips include fieldprogrammable gate arrays (FPGAs), which are commonly used for inference, and applicationspecific integrated circuits (ASICs), which are designed for specific use cases rather than
general purpose use. 13
Production of AI chips involves design and fabrication. AI chip production can involve separate
design and fabrication firms or a single vertically integrated firm can conduct operations.
Currently, there are a small number of large technology firms which design chips most popularly
used for AI development. 14 Several other large technology firms are currently designing, or haveplans to design, their own (proprietary) AI chips. Similarly, there are a small number of firms
who fabricate (i.e. manufacture) chips popularly used for AI purposes. Costs of operation forchip design and fabrication are high and steeply increasing as the industry continues toadvance. 15

2.2.3 Supercomputers, data centres, and cloud computing
AI supercomputers are networks of connected AI chips, designed to store and processenormous quantities of data and perform complex AI tasks. Supercomputers may be hosted indata centres, which are facilities designed to efficiently host computational hardware at scale.
Supercomputers hosted by a data centre can be used by the data centre owner themselves or
supplied to third parties through cloud compute. 16 Cloud compute provides third-parties,

12 Physical units containing electronic components which process and store information for computers and otherelectronic devices.
13 Jai Vipra and Sarah Myers West. "Computational Power and AI." AI Now Institute. September 2023; Saif M. Khanand Alexander Mann. "AI Chips: What They are and Why They Matter."; Center for Security and Emerging
Technology, Georgetown University. April 2020.
14 Jai Vipra and Sarah Myers West. "Computational Power and AI." AI Now Institute. September 2023;

Competition

and Markets Authority. "AI Foundation Models: Initial Report." September 2023.
15 Jai Vipra and Sarah Myers West. "Computational Power and AI." AI Now Institute. September 2023.
16 Konstantin Pilz and Lennart Heim. "Compute at scale: a broad investigation into the data center industry."

November 2023.

10

through the internet, on-demand access to hosted computational resources such as
supercomputers.
Most commonly, participants in AI development markets access their required compute using
either their own data centre, a publicly available supercomputer, or through a cloud computeprovider. 17 AI developers who do not already own a data centre are likely to seek required
compute through a cloud compute provider. Costs of building and operating data centres, used
for the purpose of AI development, are high. 18 There are a small number of large technology
firms who are the main suppliers of cloud compute services for AI development. 19

2.2.4 Data
Data is an important input for AI development markets due to the large data requirements fortraining, testing, and evaluation of AI technologies. Generative AI has demonstrated anincreasing trend in the amount of data used for training. Large language models (LLMs)
developed in recent years have been trained using billions to trillions of tokens. 20 A token in thecontext of LLMs refer to a piece of text, created by segmenting text data into smaller units which
the model can process
Generally, AI developers can gather required training data using public or proprietary sources.
Developers can access publicly available data via open datasets or web scraping 21 while
proprietary data can be bought from the owner. In the case of LLMs, current common practice isto access training data from public sources. 22 However, certain research indicates that publicly
available data used for training of LLMs could be fully exhausted in the next few years. 23 This
17 Competition and Markets Authority. "AI Foundation Models: Initial Report." September 2023.
18 Ibid.; Konstantin Pilz and Lennart Heim. "Compute at scale: a broad investigation into the data center industry."

November 2023; Kerry Hawkins and Michael Restivo. "Data centers: expensive to build, but worth every penny."
Jones Lang Lasalle (JLL).
19 Competition and Markets Authority. "AI Foundation Models: Initial Report." September 2023; Jai Vipra and Sarah

Myers West. "Computational Power and AI." AI Now Institute. September 2023; Ofcom. "Cloud services market
study: interim report." April 2023.
20 Luv Bansal. "Guide of all open sourced large language models (LLMs). Medium.” August 2023; Tom B. Brown etal. "Language models are few-shot learners." In Advances in Neural Information Processing Systems, H. Larochelle,
M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 1877–1901. July 2020.
21 Web scraping is a process that systematically browses the web and gathers data from visited web pages.
22 Competition and Markets Authority. "AI Foundation Models: Initial Report." September 2023.
23 Pablo Villalobos et al. "Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning."

October 2022.

11

may increase AI developers' reliance on access to proprietary data sets. It is unclear to what
extent markets exist for the supply of proprietary data. Certain AI developers have begun toseek out partnerships for the purpose of creating public and private datasets for training AI
models. 24

2.3 AI development
AI development pertains to the creation of AI models, algorithms, or architecture, which can beused for or integrated into a final product or service. More broadly, AI development may
encompass the creation and innovation of AI technologies outlined in Section 1. While AI
development encompasses several types of AI, LLMs have been a particularly active area ofdevelopment, likely due to recent innovations in model architecture 25 and success of recent
consumer facing LLM-based chatbots. The market for AI development is characterized by thepresence of large technology companies, start-ups, and academic research groups. 26
In terms of AI models, the underlying code is distributed and accessible in either “closed source”
or “open source” software. Closed source software, also known as proprietary, means that the
AI model code is not freely available to the public. The development and distribution are
generally controlled by a single entity, or group of partners. Users of closed source AI models
only have access to the compiled and available executable version of software. Open source,
on the other hand, means the source code is freely available to the public. For these models,anyone may be able to view, change, and (re)distribute the code. This approach is morecollaborative in nature, relying on crowd sourcing among a central or distributed community ofdevelopers who can contribute to the improvement and customization of the AI model. Eachmodel has advantages and disadvantages. Closed source models may offer proprietaryadvantages and control, while open-source models may promote transparency, participation,
and wider accessibility.

24 OpenAI. "OpenAI Data Partnerships". November 2023.
25 Transformer architecture, first introduced by Google in 2017, offer significant efficiencies for LLMs in comparison toother model architectures. Dale Markowitz. "Transformers, Explained: Understand the Model Behind GPT-3, BERT,
and T5. Dale on AI. May 2021.
26 Competition and Markets Authority. "AI Foundation Models: Initial Report." September 2023.

12

2.4 AI deployment
AI deployment refers to markets where final AI products or services are produced. An AI productor service can broadly be thought of as any product or service which applies or integrates
technology produced through AI development. In other words, an AI products or service is one
that applies or integrates any technology listed in Section 1. Use cases for AI products andservices are broad, particularly in the case where generative AI or foundation models areapplied. AI products and services are used across many industries including finance,
healthcare, education, manufacturing, and law. There are many AI products and services
currently available including, but not limited to, AI-powered assistants and chatbots, self-driving
vehicles, recommendation systems, speech-to-text software, content generation, and AIpowered search.
Producers of AI products and services can access the required technology inputs, such as an AI
model, by either (i) developing the technology themselves in-house or (ii) buying access orownership of the technology from a supplier, through a partnership, Application Programming
Interface (API) access, plug-in purchase, or other business arrangement. 27
Putting it together: examples of routes to market
for AI chatbots and self-driving vehicles
Supply Chain Level

Chatbots

Infrastructure••

Text data input
Specific computerequirements for AI model

Development•

LLMs and various machinelearning algorithms

Deployment•

Release of chatbots

Self-driving vehicles••

Visual, image, video and audio
data input
Specific compute requirements
for AI model•

Various AI technologies
including reinforcement learningand computer vision algorithms•

Release of self-driving cars

27 Competition and Markets Authority. "AI Foundation Models: Initial Report." September 2023.

13

SECTION 2: QUESTIONS FOR DISCUSSION••

Does the above description of AI markets align with what is seen in Canadianmarkets? Are there other markets, or characteristics of the outlined markets, that the
Bureau should consider?
Are there specific considerations for Canadian market participants access to inputs
such as compute, data, or AI technologies (such as AI models), that the Bureau
should be aware of?

Section 3: AI and competition
3.1 AI and mergers and monopolistic practices
The Competition Bureau investigates and acts when it discovers abuses of market power ormergers that are likely to substantially lessen or prevent competition. Certain characteristics ofAI markets could affect the degree to which market concentration and market power may ariseand develop.

3.1.1 Barriers to entry
Participation in AI development markets heavily relies on the ability to access data and compute
inputs. 28 Difficulty in accessing the required data or compute may be a barrier to entry for new
firms seeking to enter AI development markets. Public data suitable for AI purposes (e.g.
training an AI model) is highly prevalent and currently comprises a majority of the data used todevelop recent high-profile AI technologies. 29 This indicates new entrants may be able to readily
access most of their required data inputs for market participation. However, proprietary data
currently plays a significant role in certain stages of generative AI development 30 and may

28 Competition and Markets Authority. "AI Foundation Models: Initial Report." September 2023.
29 Ibid.
30 The UK Competition and Markets Authority’s Foundation Model Report found that the data used for alignment infoundation models is often proprietary. Alignment, in the context of AI, can most simply be thought of as the ability ofan AI technology to achieve the desired output of the human user.

14

become increasingly important for AI development more broadly in coming years. 31 If
proprietary data becomes a necessary input for participation in AI markets, this may impose
certain obstacles on new entrants' ability to compete effectively.
While data access could be a barrier to entry when large quantities of, particularly proprietary,data is needed to compete effectively, innovations in AI technologies and methods could
decrease reliance on data for AI development. For example, new methods for training or
improvements in models or architectures could create efficiencies, requiring fewer data
resources to achieve the same level of performance. Firms may also increasingly use synthetic
data for the purpose of AI development. Use of synthetic data may be more easily accessible
and less costly compared to collection of public data or buying proprietary data. However, use ofsynthetic data incurs risks in terms of data contamination and bias propagation, which certain
research has shown could result in “model collapse.” 32,33
While there is an emerging market for data providers for AI purposes 34, it is unclear how
competition and market power — and consequently costs of access faced by downstream AI
development firms — may develop in these markets. Large technology firms may already haveaccess to substantial quantities of proprietary data suitable for AI development, gathered frombusiness activities in other markets they take part in. This may provide a competitive advantage
to firms with existing large data stores.
Cloud compute and AI chip markets have both been found to exhibit high concentration, with a
few large technology firms dominant in each market. 35 Upfront costs of compute inputs are high,which could impose a barrier to entry for new firms looking to enter either markets for AI
infrastructure or AI development. Start-ups would likely be required to access necessary
compute inputs from a large technology provider, who may also be their competitors in AI

31 See section 2.
32 Model collapse occurs when a model fails to learn as intended. In the context of synthetic data, certain research

has shown that use of this data to train AI models may result in a ”feedback loop” that leads to more repetitive, lessdiverse, and lower quality outcomes
33 Gonzalo Martinez et al. "Towards understanding the interplay of generative Artificial intelligence and the internet."

June 2023; Tshilidzi Marwala, Eleonore Fournier-Tombs, and Serge Stinckwich. "The use of synthetic data to train AI
models: opportunites and risks for sustainable development." United Nations University. September 2023.
34 Competition and Markets Authority. "AI Foundation Models: Initial Report." September 2023.
35 Jai Vipra and Sarah Myers West. "Computational Power and AI." AI Now Institute. September 2023.

15

development. 36 New entrants in AI development markets may face additional obstacles due to a
current shortage of GPUs used for AI purposes.

3.1.2 Product differentiation
Firms may be able to use AI to differentiate their product offerings from competitors, for example
through integrating AI functions to existing products or using AI to develop better productofferings. This could benefit competition in the sense that risk of coordinated conduct is
mitigated in markets where firms offer differentiated products. Product differentiation may allow
firms to achieve market power. Price competition may be less aggressive in differentiatedmarkets, in comparison to markets where numerous firms offer comparable products. However,as new innovations enter the market and compete, market power gained through productdifferentiation may not be sustainable.

3.1.3 Economies of scope and scale
Substantial upfront costs in AI development markets create economies of scale. Large
technology firms may have an advantage in achieving economies of scale in AI markets due toexisting in-house resources in human expertise and compute. New market participants, who
don’t enjoy the same advantages, may have difficulty entering the market or competing
effectively. 37
Firms in AI markets may be able to achieve significant economies of scope due to the widearray of AI applications across various industries and use cases. This effect may be particularly
strong in foundation model markets, as foundation models are characterised by theiradaptability to a broad range of downstream tasks. This could lead to markets becoming
concentrated among firms who can participate across many foundation model applications. 38

36 Digital Platform Regulators Forum. "Examination of technology: Large Language Models." October 2023.
37 Competition and Markets Authority. "AI Foundation Models: Initial Report." September 2023.
38 Ibid.

16

3.1.4 Network effects
Digital markets are commonly characterized by the presence of network effects. AI markets may
provide settings where indirect or data network effects could arise. 39 Markets for foundation
models or generative AI, where downstream applications are broad, may generate indirect
network effects where the value of these technologies increases with the number ofdownstream applications they are used for or integrated into. Recent research has pointed to
the relationship between AI and data network effects, in which as more user data is collected by
an AI system the success of these technologies improves, 40 in turn attracting more datagenerating users. 41

3.1.5 Predatory, exclusionary, and discriminatory conduct
A firm engages in predatory conduct when it incurs short-term losses to eliminate a competitor
and gain market power. Firms could use AI to better identify opportunities for predatory conduct,
as well as for implementing predatory strategies. For example, AI may be able to better identify
customers at risk for switching and engage in personalized pricing, targeting below-cost pricecuts for “at-risk” group while leaving them unchanged for others. This strategy would minimize
losses to the firm employing predatory conduct (by reducing losses incurred on below margin
customers), which may increase incentives to engage in such conduct. 42 A firm engages indisciplinary conduct when it tries to punish another firm. Below-cost price cuts could for example
be used to discipline competitors in AI markets, rather than to directly eliminate the competitor.
Exclusionary conduct occurs when a firm aims at preventing another firm from operating in themarket. The importance of vertical relationships in AI markets may provide an environment
where particular exclusionary conduct could arise. Vertically integrated firms could both supply
an important input for participation in AI markets, such as data, compute, and AI models, while

39 AI markets may also exhibit direct network effects, where the value of an AI technology, product, or service

increases with the number of users. However, AI markets do not appear to have any specific characteristics which
would lend themselves more towards the development of direct network effects, in comparison to other digitalmarkets.
40 User data can be leveraged for training and fine-tuning of AI models, potentially resulting in improvements such as

reduced bias and hallucinations and improve model alignment.
41 Robert Wayne Gregory, Ola Henfridsson, Evgeny Kaganer, and Harris Kyriakou. "The role of Artificial intelligenceand data network effects for creating user value." Academy of Management Review, Vol. 46, No. 3. July 2021.
42 Thomas K. Cheng and Julian Nowag. "Algorithmic Predation and Exclusion." U. Pa. J. Bus. L., 25:41. 2023; OECD.

"Algorithmic Competition." OECD Competition Policy Roundtable Background Note. 2023.

17also competing in the same downstream markets as the firms they supply to. This would be aconcern for competition if these firms were to engage in behaviour to exclude their downstream
competitors from the market, for example through refusal to supply or through minimizing themargin between the prices they charge for supplied inputs and prices charged by downstream
firms, making it difficult for downstream firms to effectively compete. AI could be leveraged by
platforms engaged in self-preferencing, providing advantages to their own products over rivals’
products, 43 and may make it more difficult for consumers to recognize sponsoredrecommendations. 44 AI markets may offer opportunities for exclusionary conduct to arise, given
the presence of vertically integrated firms who both supply important compute inputs, such as
cloud compute services, as well as competing in AI development markets.
AI markets are characterized by the presence of large technology firms, who compete in various
digital markets. These firms may engage in tying and bundling, which are common strategies
employed in many industries and can be associated with strong cost efficiencies. However,certain tying and bundling strategies may constitute an abuse of market power. For example,
firms with existing dominance in a given market, may leverage tying and bundling strategies toenhance or maintain dominance in other markets they compete in. AI itself could be leveraged
to target bundling and tying strategies, for example by targeting customers at risk of switching 45
There may be specific concerns when AI is employed as a decision-maker, for example
determining the pricing strategies of a firm. AI could for example autonomously (absent human
instruction) choose to employ predatory, exclusionary, or discriminatory conduct. Firms which
use AI for decision-making purposes, may need to ensure proper human oversight to identify
and correct any potential anti-competitive decision making of AI.

3.1.6 Considerations for mergers
The Bureau’s merger review process involves the assessment of whether the merger would be
likely to create, maintain, or enhance market power, or result in meaningful price increases. If
43 OECD. "Competition and AI." OECD Business and Finance Outlook 2021: AI in Business and Finance. 2021;

Competition and Markets Authority. "Algorithms: How they can reduce competition and harm consumers." January2021; OECD. "Algorithmic Competition." OECD Competition Policy Roundtable Background Note. 2023.
44

Digital Platform Regulators Forum. "Examination of technology: Large Language Models." October 2023.

45 Thomas K. Cheng and Julian Nowag. "Algorithmic Predation and Exclusion."

U. Pa. J. Bus. L., 25:41. 2023.

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the merger places the combined firm in a dominant position, it could result in the ability for themerged firm to engage in predatory, exclusionary, or discriminatory conduct. Horizontal
mergers, where merging firms compete in the same market(s), may involve the combination ofmarket shares, or other resources, 46 leading to the combined firm achieving market power.
Vertical mergers combine firms that operate at distinct levels of the supply chain. A verticalmerger can result in a firm’s suppliers or customers becoming their competitors and could resultin enhanced market power in either the upstream or downstream market. A vertical merger
would be concerning to the Bureau if it were likely to result in, or increase, the ability andincentive to limit or eliminate competitors’ access to inputs or markets.
In the case of a conglomerate merger, where post-merger the combined firm competes in
multiple related markets, it may create the ability or incentive of the firm to engage in anticompetitive tying strategies. Mergers, of any form, involving a firm who supplies compute inputs,
such as AI chips and cloud services, could warrant additional scrutiny due to the existing highlevels of concentration in these markets. Mergers in AI markets may require additional scrutiny
as large established firms may seek to acquire emerging competitors as a means of preventing
or lessening competition. 47
A merger may prevent or lessen competition substantially when it facilitates or encourages
coordinated behaviour among firms after the merger. Coordinated behaviour occurs when firms
engage in accommodating conduct that is profitable for all coordinating firms. Firms may
coordinate on various business decisions, such as prices or allocation of markets. Coordination
will not always require explicit communication and may be achieved through tacit
understandings. AI could help facilitate coordinated conduct, for example through collection of
information on and rapid response to competitor conduct.

46 While market shares are one of the more traditional indicators used by competition authorities to evaluate market

power, characteristics of AI markets, and other digital markets, may create new sources of achieving market power.
For example, it has been posited that network effects or exclusive data access could contribute to market power indigital markets. OECD. "The evolving concept of market power in the digital economy." OECD Competition Policy
Roundtable Background Note. 2022.
47 Bureau of Competition & Office of Technology, Federal Trade Commission. "Generative AI Raises Competition

Concerns." June 2023.

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3.2 AI and cartels
Cartels are formed when two or more firms agree to fix prices, allocate markets, or restrict
supply. Cartel agreements can result in significant harm to competition and such illegal
agreements are criminal offences under the Competition Act. While there are several types ofillegal agreements covered by the Competition Act, one of the most common are price-fixing
agreements which occur when two or more competing firms agree to set the same price forgoods or services.

3.2.1 AI and cartel agreements
AI could be used to implement or sustain cartel agreements. Firms could, for example, use AI
technology to automatically implement agreed upon prices or other business decisions, monitor,and communicate with cartel members, and quickly detect and punish any members who
deviate from the agreement. AI can increase market transparency, due to the ability toautonomously and at greater speed and scale intake data on market characteristics andcompetitors. AI may also lead to increased frequency of interaction between competitors, due togreater ease in detecting competitor price changes and quicker responsive price adjustments. 48
High market transparency and frequent competitor interactions are two market conditions
commonly associated with greater ability and incentive for cartels to form and operate.
Concern may arise if multiple competitors purchase or use the same AI technology from a thirdparty supplier. A supplier of AI may leverage the technology to facilitate a cartel agreement
amongst horizontal competitors who it supplies to, known as a “hub-and-spoke” arrangement.
While hub-and-spoke arrangements can form without the use of AI, this technology may easethe facilitation of such agreements through greater ease of monitoring or more discreet
communication. 49

3.2.2 AI and tacit collusion
Developments in AI technology, in particular AI-powered pricing algorithms, have led toincreased research and discussions around the possibility for AI technologies to achieve tacit
algorithmic collusion. This phenomenon has not yet been observed or proven explicitly in real-

48 OECD. "Competition and AI." OECD Business and Finance Outlook 2021: AI in Business and Finance. 2021.
49 Ibid.

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world markets. However, research has shown that, in experimental settings, AI could beleveraged to facilitate a form of tacit collusion 50 in which AI used by horizontal competitors, may
autonomously (without human instruction) learn and implement coordinated strategies, similarly
to what may be implemented by an explicit cartel. 51

3.3 AI and deceptive marketing practices
With continuous developments in AI markets, consumers face a vast amount of rapidly involving
information about AI, making it challenging to stay informed and make educated decisions on AI
product and services. The Bureau seeks to ensure consumers receive truthful information to
help them make informed buying decisions. It is against the law for business and individuals to
promote goods and services to Canadian consumers using deceptive or misleading methods.
The United States Federal Trade Commission (FTC) outlines a number of ways businesses can
avoid making false or misleading representations around their AI products or services. These
include exaggerating what an AI product can do, falsely promising that an AI product performs atask better than a non-AI product, not performing risk analysis, and fabricating that a productuses AI (when it may not at all). 52
The automated nature of AI, and recent developments in generative AI, could be exploited toengage in certain types of deceptive marketing conduct. AI could be leveraged in deceptive
marketing practices where AI could be used to automate certain aspects of the conduct, forexample in deceptive telemarketing or deceptive prize notices. 53 AI eases the ability to scale
such conduct, for example through automating phone calls or other telecommunications.
Generative AI could be leveraged to generate content for deceptive marketing conduct, such as

50 Tacit collusion occurs when firms make decisions that jointly maximize profits without making an explicit agreement

to do so. Tacit AI collusion is the conceptual ability for AI decision making to result in tacitly collusive strategies andoutcomes.
51OECD. "Competition and AI." OECD Business and Finance Outlook 2021: AI in Business and Finance. 2021.
52 Michael Atleson, Division of Advertising Practices, Federal Trade Commission. "Keep your AI claims in check."February 2023.
53 When a business fails to make certain disclosures or engages in false or misleading conduct via

telecommunications, this may be deceptive telemarketing, which is a criminal offence under the Competition Act.
When a business sends prize notices which give the recipient an impression they have won a prize, but requires therecipient to pay a fee or incur a cost to collect the prize, this may be a deceptive price notice. It is against the law toengage In these practices, unless the recipient has actually won the prize and the business has made specificdisclosures and satisfied certain other requirements.

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e-mail or phone scripts. This technology could be leveraged to make deceptive marketing
conduct more convincing, using “deepfake” (digitally altered) voices, images, or videos.

3.4 AI and competition promotion
In addition to enforcing the Competition Act, the Bureau promotes competition in Canada by
advocating for pro-competitive policy development at all levels of government. The way that
laws, policies, and regulations (collectively, “policies”) are designed, can promote or hinder
competition in Canadian AI markets.
The Bureau generally recommends that regulators rely on market forces to achieve the benefitsof competition. These include: innovation, affordability, productivity, and choice. Where market
forces are insufficient, the Bureau provides advice on how to achieve policy objectives in aminimally intrusive way. This can take the form of submissions to public consultations, market
studies, participation in regulatory proceedings, and working directly with decision-makers toassess the competitive impact of policies and regulations.
One of the tools the Bureau uses when working with policy makers is the Competition Policy
Assessment Toolkit. The Toolkit provides a step-by-step guide to assessing how a policy might
impact competition and outlines four key indicators of a competitive marketplace. The remainderof this section provides an overview and examples of how the four Toolkit factors might apply to
AI markets; followed by discussion questions on other considerations that policymakers should
consider in developing pro-competitive policies for AI markets.
Indicator 1: Businesses can easily enter and expand
Why this matters for competition: When a business faces formidable rivals, it must
continually innovate, improve efficiency, and keep prices low. Obstacles that make it moredifficult for businesses to enter or expand in a market or operate across borders, called barriers
to entry, diminish competitive intensity.
In the context of AI, policies should be technology-neutral or device-agnostic to avoid
unintentionally prohibiting certain business models. 54 Policies should also be harmonized across

54 Competition Bureau. "Technology-led innovation in the Canadian financial services sector." December 2017.

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geographic boundaries to facilitate businesses operating across Canada and globally. 55 Regularreviews of policies can help ensure that they remain relevant given the rapid advancement andevolution of AI. 56 Policies should also promote entry and expansion. Being careful to limit
regulatory burden is a best practice in general but it can be especially important for smaller
competitors who may struggle to support the same regulatory overhead as larger rivals. In somecases, it may be an option to tailor regulations to firm size, for example to reflect differential
risks. Policies can also promote entry and expansion by improving access to key inputs such asdata, compute, expertise, and capital . 57 Policies which support open-source models might also
help facilitate entry. 58
Indicator 2: Businesses can freely set the price, quality, and quantity of their products andservices
Why this matters for competition: Policies that require businesses to charge the same priceor offer the same quality or quantity of products and services reduces their incentive to find new
ways to lower costs, increase productivity or improve quality. This can lead to consumers paying
too much for lesser quality products and services.
In the context of AI, policies that unnecessarily inhibit open-source or other business models
may needlessly restrict competition.
Indicator 3: Businesses have a strong incentive to compete
Why this matters for competition: Policies that limit the potential gains a business may realizeby competing vigorously can reduce the incentive for businesses to compete. These include
requirements for businesses to publish information about prices, outputs or sales, selfregulatory arrangements, and profit or market share caps.
In the context of AI, when developing guidelines, policymakers should ensure standards do
not result in or facilitate coordination by requiring market participants to share competitively
sensitive information. Competitively sensitive information includes, for example, pricing plans or

55 Ibid.
56 Ibid.
57 Competition and Markets Authority. "AI Foundation Models: Initial Report." September 2023.
58 Ibid.

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costs that would otherwise not be accessible to competitors. 59 In addition, standards for AI
market participants should be calibrated so as not to create a marketplace whereby competitors
have identical products or services due to compliance with guidelines.
Indicator 4: Consumers can easily switch between competitive alternatives
Why this matters for competition: When consumers can easily switch to a new business,companies will offer better services at lower prices to win or keep customers. This is theessence of competition.
Barriers to switching, such as complicated, time-consuming, and expensive switchingprocesses, are called “switching costs.” Switching costs make it more difficult for new
businesses to gain traction in a market because customers are more likely to stay with theircurrent service provider even if a better offer exists. Businesses also have less incentive to
discount their prices and innovate when consumers find it difficult to switch.
In the context of AI, policies that promote interoperability generally have a positive impact oncompetition by facilitating consumer choice. 60 Interoperability 61 and data portability 62 are two
specific ways to promote competition by allowing consumers to use multiple models and switch
more easily between models. 63

59 Competition Bureau. "Bulletin on Amendments to the Abuse of Dominance Provisions." October 2023.
60 Competition and Markets Authority. "AI Foundation Models: Initial Report." September 2023.
61 Interoperability refers to the ability of a product or system to work with another product or system.
62 Data portability refers to consumers' ability to port their data from one product or service to another.
63 Competition and Markets Authority. "AI Foundation Models: Initial Report." September 2023.

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SECTION 3: QUESTIONS FOR DISCUSSION
•
•
•

•

Are there additional factors that can affect competition in the AI sector?
Are there additional aspects of the AI sector that are relevant to these competition
factors?
Are any of the possible issues identified above particularly significant to the AI sector
in Canada?
Are there any particularly relevant or informative market study 64 topics, specific to the
AI sector or a portion of the AI sector, which the Bureau should consider?

Conclusion
This paper has provided a high-level overview of considerations for AI and competition, as wellas an overview of technologies which may be integrated into AI products and services. The
paper provides a description of the markets involved in Artificial intelligence production, fromcomputational and data inputs, development of AI technologies themselves, and the productionof AI products and services. Finally, it explores how AI could shape how competition develops
and is promoted within markets, and how it could be leveraged for anti-competitive conduct.
This paper can broadly be viewed as the current set of considerations for AI and competition
that the Bureau is monitoring. It is intended to encourage informed feedback from stakeholders
regarding (i) the extent to which they may see any of the explored competition impacts playingout in Canadian markets, and (ii) any additional considerations that should be brought to the
Bureau’s attention.

64 Market studies allow the Bureau to conduct an in-depth examination of a market or sector to better understand its
competitive dynamics and provide informed, evidence-based advice to decision-makers.

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Glossary
Algorithm: A set of instructions or steps used to solve tasks. Computer algorithms include
instructions and specifications for data processing, calculations, decision making, andautomated reasoning.
Architecture: model architecture entails the structure of the components, layers, connections,and processes that (i) make up a machine learning model and (ii) control how data flows
through a model. Common model architectures include decision trees, deep neural networks,
convolutional networks, and transformers.
Automation: the creation and use of technology to perform tasks, make decisions, and produce
goods with minimal human intervention.
Barriers to entry: Factors which make it challenging for a new firm to enter a given market.
Classification: a supervised learning technique involving grouping data into pre-set categories.
Classification algorithms use learned patterns from training data to categorize newobservations.
Computer network: the interconnected relationship between two or more computers that arelinked together to exchange data with each other.
Data labelling: the identification and labelling of data in order to provide context for a machinelearning algorithm to train from.
Economies of scope: factors which decrease of a firm’s total cost of production when multiple
products are produced together, rather than separately.
Economies of scale: the decrease of a firm’s per-unit fixed cost due to increased production ofa good, as fixed costs become spread over a higher number of units.
Labelled data: data points that have informative labels or identifiers which allow forcomputations and learning by machine learning algorithms.
Market concentration: the distribution of a given economic market among participating firms.
Market power: the ability of a firm to influence the price of its product or service.

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Model: in the context of AI, a model is a program that uses training data to recognize patterns
and make decisions without human intervention.
Network effects: occur when the value of a product increases with the number of productusers.
Node: a physical endpoint device in a network that creates, receives, and sends information.
Prediction: in the context of machine learning, prediction refers to the output from a machinelearning algorithm after it has been trained on historical data and applied to new data.
Product differentiation: the introduction or communication of distinctive characteristics of a
product or service by firms to distinguish themselves from competitors.
Synthetic data: data that is artificially manufactured, often via algorithm. Synthetic data is often
used to train and improve machine learning models.
Training data: large datasets which are used to teach a machine learning model to process
information and learn to perform tasks at a highly accurate level.
Unlabelled data: data points that do not have distinct identifiers or label.

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