ChatCCB: The Competition Bureau Invites Discussion on Algorithmic Pricing

On June 10, 2025, the Competition Bureau released its discussion paper on “Algorithmic pricing and competition”, inviting interested parties to provide feedback by August 4, 2025 Although the paper does not include policy recommendations, it provides useful insight into the Bureau’s current views on this emerging issue and areas of interest for further study. Of particular note:

• A definition of “algorithmic pricing” will likely include three components: (1) automation (runs on an algorithm), (2) the algorithm engages in price optimization for products and services, and (3) the algorithm uses a variety of data sources as an input to inform its pricing recommendations.
• Algorithmic pricing can both promote competition by encouraging innovation and efficiency and hurt competition by facilitating price coordination, price discrimination, and misleading consumers about the collection of their data.
• Price coordination and price discrimination impacts of algorithmic pricing will likely be particular areas of concern for the Bureau.

Given the widespread adoption of AI pricing tools across many industries, it will be worth watching the Bureau’s further developments on these topics as past discussion papers have been the harbinger of enforcement action.

The Bureau’s Working Definition of Algorithmic Pricing

The discussion paper begins by outlining the Bureau’s current answer to the question: what is algorithmic pricing?

Without precluding the possibility of additional relevant factors emerging from this discussion, the Bureau outlines three general components that should be included in the definition of algorithmic pricing. First, algorithmic pricing involves automation in the sense that it runs on an algorithm driven by predefined rules or artificial intelligence such that it can complete tasks without human interaction. Second, it involves price optimization such that the algorithm is essentially analyzing data to determine optimal prices for products and services to maximize a business’s profits. Third, the algorithm runs on data as an input sourced from publicly available information, businesses’ internal information systems, and external information providers, among other possible sources. 

The Bureau then refines these components to particularize different types of algorithms that could each fit within a broad definition of algorithmic pricing.

The Bureau observes that pricing algorithms can be subdivided into two categories based on “how it works”. One category is rules based algorithms that complete tasks, such as price optimization, within predetermined parameters or conditions that are set and changed by human action. The second category is AI-driven algorithms that collect and learn from continuous updates to its data to complete tasks, such as price optimization, in increasingly refined ways.

The Bureau also observes that pricing algorithms can be subdivided based on function. Two types of pricing algorithms that the Bureau identifies are dynamic pricing algorithms (that respond to market conditions such as supply and demand, competitor prices, and inventory levels etc.) and personalized pricing algorithms (that respond to individual consumer data such as demographics, online behaviour and transaction history).

In these ways, the discussion paper reveals the Bureau’s current conception of “algorithmic pricing” as well as the distinctions between pricing algorithms that may become important to its future policies and guidelines. Interested parties should be on the lookout for whether distinctions are drawn between the different types of algorithms. For example, AI-driven algorithms may be of particular concern given the lower level of transparency into the parameters for its recommendations. Likewise, personal pricing algorithms may garner attention due to its collection of consumer data and ability to facilitate price discrimination.

Presumptive Pro- and Anti-Competitive Impacts of Algorithmic Pricing

The discussion paper also sets out the current impacts of algorithmic pricing that have captured the Bureau’s attention. The Bureau acknowledges that “[a]lgorithmic pricing can both promote and hurt competition”. Specific examples of the Bureau’s perceived anti- and pro-competitive impacts of algorithmic pricing include:

Algorithmic Pricing may Increase the Prevalence of Price Coordination: The Bureau hypothesizes that algorithmic pricing could facilitate both explicit and tacit agreements to fix prices between competitors. Competitors could use the same algorithm to process their data that, in turn, sets or recommends prices to each of them to earn the highest combined profit for all. In this way, algorithmic pricing could facilitate “hub-and-spoke” conspiracies between competitors where pricing algorithms pool competitor data and implement a coordinated pricing strategy. While the Bureau is now turning its mind to the price coordination impacts of algorithmic pricing, these questions have already begun to be litigated in other jurisdictions: see for example In RealPage, Case No. 3:23-md-3071 (M.D. Tenn.).

Algorithmic Pricing may Increase the Prevalence of Price Discrimination: Throughout the discussion paper, the Bureau hypothesizes that new pricing strategies made possible by algorithmic pricing could increase the prevalence of price discrimination. For example, the Bureau observes that while personalized pricing could legitimately operate by providing targeted upgrade or downgrade options to premium and budget consumers, businesses seeking to maximize profit could also engage in cross-subsidized price discrimination by using personalized pricing algorithms to charge different prices for the same product to different consumers depending on their likelihood to switch to competitors. While price discrimination does not, on its own, offend the Competition Act, the Bureau could become concerned if this conduct is engaged in by a dominant player or group of players in a particular market resulting ins a substantial prevention or lessening of competition to that market as a whole. 

Algorithmic Pricing Could Promote Competitive Entry and Expansion: The Bureau also observes that algorithmic pricing could promote competition by facilitating the entry of new businesses into a market. In the same way that personalized pricing algorithms could facilitate harmful price discrimination, they could also help new businesses to target specific groups of consumers who are likely to switch from existing firms thereby facilitating their entry and new competition.

Algorithmic Pricing Could Improve Innovation and Market Efficiency: The Bureau observes that pricing algorithms could increase the efficiency of businesses by allowing them to react more quickly to supply and demand and thereby better manage their inventories. Algorithmic pricing could also encourage disruptive innovation brought by new firms whose entry into the market they facilitate leading to an increase in new products and services.

Overall, the discussion paper reflects a nuanced view of algorithmic pricing recognizing both its potential benefits and harms, and provides insight into some of the key distinctions (rule based vs. AI-driven algorithmic pricing; dynamic vs. personalized pricing algorithms) that may drive future guidance. It will be worth monitoring future Bureau developments on algorithmic pricing as past discussion papers have foreshadowed enforcement action.

For more information, please consult our Competition/Antitrust & Foreign Investment Group.