I still remember rushing to call an Uber one evening a couple of years ago, about twenty minutes from home with only five percent battery left. Back then, just before COVID, a ride that distance usually cost around $15 to $20, long before most of us even knew what pricing algorithms were. But that night, just as my phone was about to die, the price suddenly jumped to $35. It felt almost personal, as if the app somehow knew I had no other choice.

Now, you’d consider yourself lucky if you could book a twenty-minute ride for $35 in the GTA. After a Jays game or a Raptors win, prices can climb to $70 or $80, and I still remember paying $120 for a twenty-five-minute ride home on New Year’s Eve.

At first, I brushed it off as coincidence — maybe there just weren’t enough drivers around. But today, with so many more drivers on the road, the dots still don’t quite connect. It kept happening during bad weather, at odd hours, or when I was in a rush. The pattern made me wonder if it was really coincidence, or if something else was at play.

Algorithms and data analytics are increasingly shaping business strategies, particularly in how companies set and adjust prices. Advances in technology, including artificial intelligence and greater access to data have accelerated this trend. In Canada alone, more than 60 companies now offer algorithm-based pricing services designed to help businesses optimize their pricing decisions.

What is algorithmic pricing and how is it used?

Algorithmic pricing refers to the use of automated systems to determine or recommend prices for products and services, often in real time, using a variety of data inputs. This practice is gaining traction across industries worldwide, from hospitality and concert ticketing to ride-sharing platforms.

Recognizing its growing influence, competition authorities around the world have begun examining the implications of algorithmic pricing. In the past year, both the U.S. Federal Trade Commission (FTC) and the U.K. Competition and Markets Authority (CMA) have launched studies on personalized and dynamic pricing. The Competition Bureau of Canada is likewise exploring how these practices may affect competition and consumer welfare in the Canadian marketplace [1].

Pricing algorithms can be categorized by the type of data they use:

• Dynamic pricing algorithms adjust prices based on market conditions such as supply and demand, competitors’ prices, inventory levels, time of day, season, weather, or local events.
• Personalized pricing algorithms set prices for individuals or groups using consumer data like demographics, online behaviour, or purchase history.

In practice, many firms combine both market and consumer data, making the distinction between the two approaches increasingly blurred.

Dynamic pricing may allow companies to maximize their profits by responding swiftly to market changes, while personalized pricing (also known as surveillance pricing) may allow companies to target as closely as possible consumers’ willingness to pay to maximize their profits [2].

Discussion:

While existing competition laws were built to address traditional forms of collusion and market dominance, the rise of algorithmic pricing and data-driven decision-making has blurred old boundaries between independent business behaviour and coordinated market outcomes.

On one hand, algorithms can make markets more efficient, allowing firms to respond quickly to supply and demand, reduce human error, and offer dynamic, data-informed prices. On the other, they may facilitate or conceal anti-competitive behaviour — enabling tacit coordination, personalized price discrimination, or even algorithmic collusion that occurs without explicit human agreement.

Given these developments, it may no longer be enough to apply traditional competition rules to a fundamentally digital and data-dependent marketplace. Instead, regulators might need to rethink what “competition” means in this context — whether through updated enforcement tools, clearer guidance on algorithmic accountability, or cross-disciplinary collaboration between technologists and competition experts.

AI-driven pricing algorithms challenge the very foundations of competition law — which relies on proving intent, communication, and agreement. As markets become more automated, enforcement agencies must adapt by developing new investigative tools, interdisciplinary expertise, and possibly updated legal frameworks to address algorithmic conduct that harms competition.