by Simon Yammine
Could dynamic pricing make markets more efficient, or will it become another mechanism for extracting more money from consumers, tenants and businesses?
Same Product, Two Different Prices
A Big Mac costs $5.69 at one McDonald’s restaurant in Fresno, California, and $6.89 at another location just a few kilometres away. That is a difference of approximately 21% for essentially the same product.
The price difference itself is not necessarily unusual. Businesses have always adjusted prices according to rent, wages, operating costs, competition and local purchasing power.
What makes this case different is the alleged involvement of artificial intelligence.
A Reuters investigation published on September 29, 2026, examined McDonald’s use of sophisticated pricing recommendations across its American restaurants. The company is now facing a lawsuit alleging that its pricing technology improperly coordinates prices among independently operated franchises.
McDonald’s disputes the allegations, maintaining that its franchisees independently determine their prices, and that AI does not directly set the price of a Big Mac.
The controversy raises a much larger question.
Traditionally, companies calculated prices using production costs, operating expenses, competition and a reasonable profit margin. Increasingly, technology allows them to estimate something else: the maximum amount a particular market, neighbourhood or individual is prepared to pay.
A customer living in an affluent neighbourhood might pay more than someone elsewhere, even when the underlying product costs virtually the same to produce.
The concern is not simply that prices differ. It is that sophisticated technology could make those differences increasingly difficult to understand, challenge or avoid.
Dynamic Pricing: An Old Practice With a Powerful New Engine
Dynamic pricing is the practice of changing prices in response to market conditions.
Airlines have used it for decades. Hotel rooms cost more during major events. Ride-sharing services increase fares when demand exceeds available vehicles.
The principle is straightforward: when demand rises, prices increase; when demand weakens, prices may fall.
Artificial intelligence takes this practice much further.
Algorithms can analyse enormous quantities of information, including purchasing patterns, local competition, weather, inventory, time of day and consumer behaviour. They can then recommend prices designed to achieve a particular objective, frequently maximizing revenue or profit.
There is an important distinction between dynamic pricing and personalized pricing.
Dynamic pricing generally responds to market conditions. Personalized pricing uses information about particular customers or groups to estimate their willingness to pay.
The latter raises more serious questions about privacy, discrimination and fairness.
Dynamic pricing can also benefit consumers. Lower off-peak prices can encourage demand, reduce waste and make better use of existing capacity.
The problem arises when consumers have little information, limited alternatives or no practical ability to postpone a purchase.
Halifax, Nova Scotia and the Construction Industry
The McDonald’s controversy may appear distant from Halifax’s housing and construction challenges. In reality, the underlying economics are closely connected.
For Halifax residents, algorithmic pricing could influence groceries, transportation, entertainment, insurance, travel and other everyday expenses.
In competitive markets, it may create opportunities for lower prices. But when alternatives are limited, sophisticated pricing can strengthen the seller’s position.
This matters particularly for lower-income households, seniors and people who cannot easily change their purchasing habits or travel to cheaper locations.
Large corporations can afford sophisticated pricing software and access enormous datasets. Independent Halifax businesses generally cannot compete with that analytical capacity.
However, affordable pricing software could also help smaller firms manage inventory, offer discounts and respond more quickly to market conditions.
For franchise operators, another concern emerges: how much genuine pricing independence remains when headquarters supplies increasingly influential recommendations?
The distinction between useful commercial guidance and improper price coordination deserves careful regulatory attention.
Housing introduces a much more sensitive dimension.
Imagine rental-management software examining vacancy rates, neighbourhood incomes, comparable listings, lease expirations and tenant demand before recommending rents.
Such systems might help landlords identify competitive prices and reduce vacancies.
But if competing landlords rely on shared, commercially sensitive information, the technology could weaken genuine price competition.
For Halifax, where housing affordability remains a major concern, algorithmic rent recommendations deserve particular scrutiny.
Construction, on a different note, already involves a form of dynamic pricing.
Concrete, steel, electrical equipment, mechanical systems, transportation and subcontractor quotations change with supply, demand and availability.
AI could make these adjustments faster and more precise.
Suppliers might automatically adjust quotations according to project location, urgency, available inventory, seasonal demand and a contractor’s purchasing history.
For developers, this could improve procurement planning and identify opportunities to purchase materials at lower prices.
But it could also make cost forecasting more difficult if quotations change rapidly or suppliers use algorithms to estimate how much a developer can afford.
Equally important is competition among subcontractors. If supposedly independent suppliers use common pricing systems or exchange competitively sensitive information, authorities may need to investigate whether competition is being reduced.
Efficiency for Whom?
Artificial intelligence can help businesses reduce waste, forecast demand, improve productivity and lower operating costs.
Those benefits are real.
But technology that improves efficiency does not automatically pass the savings to consumers.
Sometimes it simply enables businesses to capture a larger share of what customers are willing to pay.
For Halifax’s construction and housing sectors, this distinction is particularly important.
The question is no longer whether artificial intelligence will influence prices. It is whether the benefits of that intelligence will be shared among businesses, workers and consumers, or concentrated among those who control the algorithms.
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